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• Radiology Thesis – More than 400 Research Topics (2022)!

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Introduction

A thesis or dissertation, as some people would like to call it, is an integral part of the Radiology curriculum, be it MD, DNB, or DMRD. We have tried to aggregate radiology thesis topics from various sources for reference.

Not everyone is interested in research, and writing a Radiology thesis can be daunting. But there is no escape from preparing, so it is better that you accept this bitter truth and start working on it instead of cribbing about it (like other things in life. #PhilosophyGyan!)

Start working on your thesis as early as possible and finish your thesis well before your exams, so you do not have that stress at the back of your mind. Also, your thesis may need multiple revisions, so be prepared and allocate time accordingly.

Tips for Choosing Radiology Thesis and Research Topics

Keep it simple silly (kiss).

Retrospective > Prospective

Retrospective studies are better than prospective ones, as you already have the data you need when choosing to do a retrospective study. Prospective studies are better quality, but as a resident, you may not have time (, energy and enthusiasm) to complete these.

Choose a simple topic that answers a single/few questions

Original research is challenging, especially if you do not have prior experience. I would suggest you choose a topic that answers a single or few questions. Most topics that I have listed are along those lines. Alternatively, you can choose a broad topic such as “Role of MRI in evaluation of perianal fistulas.”

You can choose a novel topic if you are genuinely interested in research AND have a good mentor who will guide you. Once you have done that, make sure that you publish your study once you are done with it.

Get it done ASAP.

In most cases, it makes sense to stick to a thesis topic that will not take much time. That does not mean you should ignore your thesis and ‘Ctrl C + Ctrl V’ from a friend from another university. Thesis writing is your first step toward research methodology so do it as sincerely as possible. Do not procrastinate in preparing the thesis. As soon as you have been allotted a guide, start researching topics and writing a review of the literature.

At the same time, do not invest a lot of time in writing/collecting data for your thesis. You should not be busy finishing your thesis a few months before the exam. Some people could not appear for the exam because they could not submit their thesis in time. So DO NOT TAKE thesis lightly.

Do NOT Copy-Paste

Reiterating once again, do not simply choose someone else’s thesis topic. Find out what are kind of cases that your Hospital caters to. It is better to do a good thesis on a common topic than a crappy one on a rare one.

Books to help you write a Radiology Thesis

Event country/university has a different format for thesis; hence these book recommendations may not work for everyone.

• Amazon Kindle Edition
• Gupta, Piyush (Author)
• English (Publication Language)
• 206 Pages - 10/12/2020 (Publication Date) - Jaypee Brothers Medical Publishers (P) Ltd. (Publisher)

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List of Radiology Research /Thesis / Dissertation Topics

• State of the art of MRI in the diagnosis of hepatic focal lesions
• Multimodality imaging evaluation of sacroiliitis in newly diagnosed patients of spondyloarthropathy
• Multidetector computed tomography in oesophageal varices
• Role of positron emission tomography with computed tomography in the diagnosis of cancer Thyroid
• Evaluation of focal breast lesions using ultrasound elastography
• Role of MRI diffusion tensor imaging in the assessment of traumatic spinal cord injuries
• Sonographic imaging in male infertility
• Comparison of color Doppler and digital subtraction angiography in occlusive arterial disease in patients with lower limb ischemia
• The role of CT urography in Haematuria
• Role of functional magnetic resonance imaging in making brain tumor surgery safer
• Prediction of pre-eclampsia and fetal growth restriction by uterine artery Doppler
• Role of grayscale and color Doppler ultrasonography in the evaluation of neonatal cholestasis
• Validity of MRI in the diagnosis of congenital anorectal anomalies
• Role of sonography in assessment of clubfoot
• Role of diffusion MRI in preoperative evaluation of brain neoplasms
• Imaging of upper airways for pre-anaesthetic evaluation purposes and for laryngeal afflictions.
• A study of multivessel (arterial and venous) Doppler velocimetry in intrauterine growth restriction
• Multiparametric 3tesla MRI of suspected prostatic malignancy.
• Role of Sonography in Characterization of Thyroid Nodules for differentiating benign from
• Role of advances magnetic resonance imaging sequences in multiple sclerosis
• Role of multidetector computed tomography in evaluation of jaw lesions
• Role of Ultrasound and MR Imaging in the Evaluation of Musculotendinous Pathologies of Shoulder Joint
• Role of perfusion computed tomography in the evaluation of cerebral blood flow, blood volume and vascular permeability of cerebral neoplasms
• MRI flow quantification in the assessment of the commonest csf flow abnormalities
• Role of diffusion-weighted MRI in evaluation of prostate lesions and its histopathological correlation
• CT enterography in evaluation of small bowel disorders
• Comparison of perfusion magnetic resonance imaging (PMRI), magnetic resonance spectroscopy (MRS) in and positron emission tomography-computed tomography (PET/CT) in post radiotherapy treated gliomas to detect recurrence
• Role of multidetector computed tomography in evaluation of paediatric retroperitoneal masses
• Role of Multidetector computed tomography in neck lesions
• Estimation of standard liver volume in Indian population
• Role of MRI in evaluation of spinal trauma
• Role of modified sonohysterography in female factor infertility: a pilot study.
• The role of pet-CT in the evaluation of hepatic tumors
• Role of 3D magnetic resonance imaging tractography in assessment of white matter tracts compromise in supratentorial tumors
• Role of dual phase multidetector computed tomography in gallbladder lesions
• Role of multidetector computed tomography in assessing anatomical variants of nasal cavity and paranasal sinuses in patients of chronic rhinosinusitis.
• magnetic resonance spectroscopy in multiple sclerosis
• Evaluation of thyroid nodules by ultrasound elastography using acoustic radiation force impulse (ARFI) imaging
• Role of Magnetic Resonance Imaging in Intractable Epilepsy
• Evaluation of suspected and known coronary artery disease by 128 slice multidetector CT.
• Role of regional diffusion tensor imaging in the evaluation of intracranial gliomas and its histopathological correlation
• Role of chest sonography in diagnosing pneumothorax
• Role of CT virtual cystoscopy in diagnosis of urinary bladder neoplasia
• Role of MRI in assessment of valvular heart diseases
• High resolution computed tomography of temporal bone in unsafe chronic suppurative otitis media
• Multidetector CT urography in the evaluation of hematuria
• Contrast-induced nephropathy in diagnostic imaging investigations with intravenous iodinated contrast media
• Comparison of dynamic susceptibility contrast-enhanced perfusion magnetic resonance imaging and single photon emission computed tomography in patients with little’s disease
• Role of Multidetector Computed Tomography in Bowel Lesions.
• Role of diagnostic imaging modalities in evaluation of post liver transplantation recipient complications.
• Role of multislice CT scan and barium swallow in the estimation of oesophageal tumour length
• Malignant Lesions-A Prospective Study.
• Value of ultrasonography in assessment of acute abdominal diseases in pediatric age group
• Role of three dimensional multidetector CT hysterosalpingography in female factor infertility
• Comparative evaluation of multi-detector computed tomography (MDCT) virtual tracheo-bronchoscopy and fiberoptic tracheo-bronchoscopy in airway diseases
• Role of Multidetector CT in the evaluation of small bowel obstruction
• Sonographic evaluation in adhesive capsulitis of shoulder
• Utility of MR Urography Versus Conventional Techniques in Obstructive Uropathy
• MRI of the postoperative knee
• Role of 64 slice-multi detector computed tomography in diagnosis of bowel and mesenteric injury in blunt abdominal trauma.
• Sonoelastography and triphasic computed tomography in the evaluation of focal liver lesions
• Evaluation of Role of Transperineal Ultrasound and Magnetic Resonance Imaging in Urinary Stress incontinence in Women
• Multidetector computed tomographic features of abdominal hernias
• Evaluation of lesions of major salivary glands using ultrasound elastography
• Transvaginal ultrasound and magnetic resonance imaging in female urinary incontinence
• MDCT colonography and double-contrast barium enema in evaluation of colonic lesions
• Role of MRI in diagnosis and staging of urinary bladder carcinoma
• Spectrum of imaging findings in children with febrile neutropenia.
• Spectrum of radiographic appearances in children with chest tuberculosis.
• Role of computerized tomography in evaluation of mediastinal masses in pediatric
• Diagnosing renal artery stenosis: Comparison of multimodality imaging in diabetic patients
• Role of multidetector CT virtual hysteroscopy in the detection of the uterine & tubal causes of female infertility
• Role of multislice computed tomography in evaluation of crohn’s disease
• CT quantification of parenchymal and airway parameters on 64 slice MDCT in patients of chronic obstructive pulmonary disease
• Comparative evaluation of MDCT  and 3t MRI in radiographically detected jaw lesions.
• Evaluation of diagnostic accuracy of ultrasonography, colour Doppler sonography and low dose computed tomography in acute appendicitis
• Ultrasonography , magnetic resonance cholangio-pancreatography (MRCP) in assessment of pediatric biliary lesions
• Multidetector computed tomography in hepatobiliary lesions.
• Evaluation of peripheral nerve lesions with high resolution ultrasonography and colour Doppler
• Multidetector computed tomography in pancreatic lesions
• Multidetector Computed Tomography in Paediatric abdominal masses.
• Evaluation of focal liver lesions by colour Doppler and MDCT perfusion imaging
• Sonographic evaluation of clubfoot correction during Ponseti treatment
• Role of multidetector CT in characterization of renal masses
• Study to assess the role of Doppler ultrasound in evaluation of arteriovenous (av) hemodialysis fistula and the complications of hemodialysis vasular access
• Comparative study of multiphasic contrast-enhanced CT and contrast-enhanced MRI in the evaluation of hepatic mass lesions
• Sonographic spectrum of rheumatoid arthritis
• Diagnosis & staging of liver fibrosis by ultrasound elastography in patients with chronic liver diseases
• Role of multidetector computed tomography in assessment of jaw lesions.
• Role of high-resolution ultrasonography in the differentiation of benign and malignant thyroid lesions
• Radiological evaluation of aortic aneurysms in patients selected for endovascular repair
• Role of conventional MRI, and diffusion tensor imaging tractography in evaluation of congenital brain malformations
• To evaluate the status of coronary arteries in patients with non-valvular atrial fibrillation using 256 multirow detector CT scan
• A comparative study of ultrasonography and CT – arthrography in diagnosis of chronic ligamentous and meniscal injuries of knee
• Multi detector computed tomography evaluation in chronic obstructive pulmonary disease and correlation with severity of disease
• Diffusion weighted and dynamic contrast enhanced magnetic resonance imaging in chemoradiotherapeutic response evaluation in cervical cancer.
• High resolution sonography in the evaluation of non-traumatic painful wrist
• The role of trans-vaginal ultrasound versus magnetic resonance imaging in diagnosis & evaluation of cancer cervix
• Role of multidetector row computed tomography in assessment of maxillofacial trauma
• Imaging of vascular complication after liver transplantation.
• Role of magnetic resonance perfusion weighted imaging & spectroscopy for grading of glioma by correlating perfusion parameter of the lesion with the final histopathological grade
• Magnetic resonance evaluation of abdominal tuberculosis.
• Diagnostic usefulness of low dose spiral HRCT in diffuse lung diseases
• Role of dynamic contrast enhanced and diffusion weighted magnetic resonance imaging in evaluation of endometrial lesions
• Contrast enhanced digital mammography anddigital breast tomosynthesis in early diagnosis of breast lesion
• Evaluation of Portal Hypertension with Colour Doppler flow imaging and magnetic resonance imaging
• Evaluation of musculoskeletal lesions by magnetic resonance imaging
• Role of diffusion magnetic resonance imaging in assessment of neoplastic and inflammatory brain lesions
• Radiological spectrum of chest diseases in HIV infected children High resolution ultrasonography in neck masses in children
• with surgical findings
• Sonographic evaluation of peripheral nerves in type 2 diabetes mellitus.
• Role of perfusion computed tomography in the evaluation of neck masses and correlation
• Role of ultrasonography in the diagnosis of knee joint lesions
• Role of ultrasonography in evaluation of various causes of pelvic pain in first trimester of pregnancy.
• Role of Magnetic Resonance Angiography in the Evaluation of Diseases of Aorta and its Branches
• MDCT fistulography in evaluation of fistula in Ano
• Role of multislice CT in diagnosis of small intestine tumors
• Role of high resolution CT in differentiation between benign and malignant pulmonary nodules in children
• A study of multidetector computed tomography urography in urinary tract abnormalities
• Role of high resolution sonography in assessment of ulnar nerve in patients with leprosy.
• Pre-operative radiological evaluation of locally aggressive and malignant musculoskeletal tumours by computed tomography and magnetic resonance imaging.
• The role of ultrasound & MRI in acute pelvic inflammatory disease
• Ultrasonography compared to computed tomographic arthrography in the evaluation of shoulder pain
• Role of Multidetector Computed Tomography in patients with blunt abdominal trauma.
• The Role of Extended field-of-view Sonography and compound imaging in Evaluation of Breast Lesions
• Evaluation of focal pancreatic lesions by Multidetector CT and perfusion CT
• Evaluation of breast masses on sono-mammography and colour Doppler imaging
• Role of CT virtual laryngoscopy in evaluation of laryngeal masses
• Triple phase multi detector computed tomography in hepatic masses
• Role of transvaginal ultrasound in diagnosis and treatment of female infertility
• Role of ultrasound and color Doppler imaging in assessment of acute abdomen due to female genetal causes
• High resolution ultrasonography and color Doppler ultrasonography in scrotal lesion
• Evaluation of diagnostic accuracy of ultrasonography with colour Doppler vs low dose computed tomography in salivary gland disease
• Role of multidetector CT in diagnosis of salivary gland lesions
• Comparison of diagnostic efficacy of ultrasonography and magnetic resonance cholangiopancreatography in obstructive jaundice: A prospective study
• Evaluation of varicose veins-comparative assessment of low dose CT venogram with sonography: pilot study
• Role of mammotome in breast lesions
• The role of interventional imaging procedures in the treatment of selected gynecological disorders
• Role of transcranial ultrasound in diagnosis of neonatal brain insults
• Role of multidetector CT virtual laryngoscopy in evaluation of laryngeal mass lesions
• Evaluation of adnexal masses on sonomorphology and color Doppler imaginig
• Role of radiological imaging in diagnosis of endometrial carcinoma
• Comprehensive imaging of renal masses by magnetic resonance imaging
• The role of 3D & 4D ultrasonography in abnormalities of fetal abdomen
• Diffusion weighted magnetic resonance imaging in diagnosis and characterization of brain tumors in correlation with conventional MRI
• Role of diffusion weighted MRI imaging in evaluation of cancer prostate
• Role of multidetector CT in diagnosis of urinary bladder cancer
• Role of multidetector computed tomography in the evaluation of paediatric retroperitoneal masses.
• Comparative evaluation of gastric lesions by double contrast barium upper G.I. and multi detector computed tomography
• Evaluation of hepatic fibrosis in chronic liver disease using ultrasound elastography
• Role of MRI in assessment of hydrocephalus in pediatric patients
• The role of sonoelastography in characterization of breast lesions
• The influence of volumetric tumor doubling time on survival of patients with intracranial tumours
• Role of perfusion computed tomography in characterization of colonic lesions
• Role of proton MRI spectroscopy in the evaluation of temporal lobe epilepsy
• Role of Doppler ultrasound and multidetector CT angiography in evaluation of peripheral arterial diseases.
• Role of multidetector computed tomography in paranasal sinus pathologies
• Role of virtual endoscopy using MDCT in detection & evaluation of gastric pathologies
• High resolution 3 Tesla MRI in the evaluation of ankle and hindfoot pain.
• Transperineal ultrasonography in infants with anorectal malformation
• CT portography using MDCT versus color Doppler in detection of varices in cirrhotic patients
• Role of CT urography in the evaluation of a dilated ureter
• Characterization of pulmonary nodules by dynamic contrast-enhanced multidetector CT
• Comprehensive imaging of acute ischemic stroke on multidetector CT
• The role of fetal MRI in the diagnosis of intrauterine neurological congenital anomalies
• Role of Multidetector computed tomography in pediatric chest masses
• Multimodality imaging in the evaluation of palpable & non-palpable breast lesion.
• Sonographic Assessment Of Fetal Nasal Bone Length At 11-28 Gestational Weeks And Its Correlation With Fetal Outcome.
• Role Of Sonoelastography And Contrast-Enhanced Computed Tomography In Evaluation Of Lymph Node Metastasis In Head And Neck Cancers
• Role Of Renal Doppler And Shear Wave Elastography In Diabetic Nephropathy
• Evaluation Of Relationship Between Various Grades Of Fatty Liver And Shear Wave Elastography Values
• Evaluation and characterization of pelvic masses of gynecological origin by USG, color Doppler and MRI in females of reproductive age group
• Radiological evaluation of small bowel diseases using computed tomographic enterography
• Role of coronary CT angiography in patients of coronary artery disease
• Role of multimodality imaging in the evaluation of pediatric neck masses
• Role of CT in the evaluation of craniocerebral trauma
• Role of magnetic resonance imaging (MRI) in the evaluation of spinal dysraphism
• Comparative evaluation of triple phase CT and dynamic contrast-enhanced MRI in patients with liver cirrhosis
• Evaluation of the relationship between carotid intima-media thickness and coronary artery disease in patients evaluated by coronary angiography for suspected CAD
• Assessment of hepatic fat content in fatty liver disease by unenhanced computed tomography
• Correlation of vertebral marrow fat on spectroscopy and diffusion-weighted MRI imaging with bone mineral density in postmenopausal women.
• Comparative evaluation of CT coronary angiography with conventional catheter coronary angiography
• Ultrasound evaluation of kidney length & descending colon diameter in normal and intrauterine growth-restricted fetuses
• A prospective study of hepatic vein waveform and splenoportal index in liver cirrhosis: correlation with child Pugh’s classification and presence of esophageal varices.
• CT angiography to evaluate coronary artery by-pass graft patency in symptomatic patient’s functional assessment of myocardium by cardiac MRI in patients with myocardial infarction
• MRI evaluation of HIV positive patients with central nervous system manifestations
• MDCT evaluation of mediastinal and hilar masses
• Evaluation of rotator cuff & labro-ligamentous complex lesions by MRI & MRI arthrography of shoulder joint
• Role of imaging in the evaluation of soft tissue vascular malformation
• Role of MRI and ultrasonography in the evaluation of multifidus muscle pathology in chronic low back pain patients
• Role of ultrasound elastography in the differential diagnosis of breast lesions
• Role of magnetic resonance cholangiopancreatography in evaluating dilated common bile duct in patients with symptomatic gallstone disease.
• Comparative study of CT urography & hybrid CT urography in patients with haematuria.
• Role of MRI in the evaluation of anorectal malformations
• Comparison of ultrasound-Doppler and magnetic resonance imaging findings in rheumatoid arthritis of hand and wrist
• Role of Doppler sonography in the evaluation of renal artery stenosis in hypertensive patients undergoing coronary angiography for coronary artery disease.
• Comparison of radiography, computed tomography and magnetic resonance imaging in the detection of sacroiliitis in ankylosing spondylitis.
• Mr evaluation of painful hip
• Role of MRI imaging in pretherapeutic assessment of oral and oropharyngeal malignancy
• Evaluation of diffuse lung diseases by high resolution computed tomography of the chest
• Mr evaluation of brain parenchyma in patients with craniosynostosis.
• Diagnostic and prognostic value of cardiovascular magnetic resonance imaging in dilated cardiomyopathy
• Role of multiparametric magnetic resonance imaging in the detection of early carcinoma prostate
• Role of magnetic resonance imaging in white matter diseases
• Role of sonoelastography in assessing the response to neoadjuvant chemotherapy in patients with locally advanced breast cancer.
• Role of ultrasonography in the evaluation of carotid and femoral intima-media thickness in predialysis patients with chronic kidney disease
• Role of H1 MRI spectroscopy in focal bone lesions of peripheral skeleton choline detection by MRI spectroscopy in breast cancer and its correlation with biomarkers and histological grade.
• Ultrasound and MRI evaluation of axillary lymph node status in breast cancer.
• Role of sonography and magnetic resonance imaging in evaluating chronic lateral epicondylitis.
• Comparative of sonography including Doppler and sonoelastography in cervical lymphadenopathy.
• Evaluation of Umbilical Coiling Index as Predictor of Pregnancy Outcome.
• Computerized Tomographic Evaluation of Azygoesophageal Recess in Adults.
• Lumbar Facet Arthropathy in Low Backache.
• “Urethral Injuries After Pelvic Trauma: Evaluation with Uretrography
• Role Of Ct In Diagnosis Of Inflammatory Renal Diseases
• Role Of Ct Virtual Laryngoscopy In Evaluation Of Laryngeal Masses
• “Ct Portography Using Mdct Versus Color Doppler In Detection Of Varices In
• Cirrhotic Patients”
• Role Of Multidetector Ct In Characterization Of Renal Masses
• Role Of Ct Virtual Cystoscopy In Diagnosis Of Urinary Bladder Neoplasia
• Role Of Multislice Ct In Diagnosis Of Small Intestine Tumors
• “Mri Flow Quantification In The Assessment Of The Commonest CSF Flow Abnormalities”
• “The Role Of Fetal Mri In Diagnosis Of Intrauterine Neurological CongenitalAnomalies”
• Role Of Transcranial Ultrasound In Diagnosis Of Neonatal Brain Insults
• “The Role Of Interventional Imaging Procedures In The Treatment Of Selected Gynecological Disorders”
• Role Of Radiological Imaging In Diagnosis Of Endometrial Carcinoma
• “Role Of High-Resolution Ct In Differentiation Between Benign And Malignant Pulmonary Nodules In Children”
• Role Of Ultrasonography In The Diagnosis Of Knee Joint Lesions
• “Role Of Diagnostic Imaging Modalities In Evaluation Of Post Liver Transplantation Recipient Complications”
• “Diffusion-Weighted Magnetic Resonance Imaging In Diagnosis And
• Characterization Of Brain Tumors In Correlation With Conventional Mri”
• The Role Of PET-CT In The Evaluation Of Hepatic Tumors
• “Role Of Computerized Tomography In Evaluation Of Mediastinal Masses In Pediatric patients”
• “Trans Vaginal Ultrasound And Magnetic Resonance Imaging In Female Urinary Incontinence”
• Role Of Multidetector Ct In Diagnosis Of Urinary Bladder Cancer
• “Role Of Transvaginal Ultrasound In Diagnosis And Treatment Of Female Infertility”
• Role Of Diffusion-Weighted Mri Imaging In Evaluation Of Cancer Prostate
• “Role Of Positron Emission Tomography With Computed Tomography In Diagnosis Of Cancer Thyroid”
• The Role Of CT Urography In Case Of Haematuria
• “Value Of Ultrasonography In Assessment Of Acute Abdominal Diseases In Pediatric Age Group”
• “Role Of Functional Magnetic Resonance Imaging In Making Brain Tumor Surgery Safer”
• The Role Of Sonoelastography In Characterization Of Breast Lesions
• “Ultrasonography, Magnetic Resonance Cholangiopancreatography (MRCP) In Assessment Of Pediatric Biliary Lesions”
• “Role Of Ultrasound And Color Doppler Imaging In Assessment Of Acute Abdomen Due To Female Genital Causes”
• “Role Of Multidetector Ct Virtual Laryngoscopy In Evaluation Of Laryngeal Mass Lesions”
• MRI Of The Postoperative Knee
• Role Of Mri In Assessment Of Valvular Heart Diseases
• The Role Of 3D & 4D Ultrasonography In Abnormalities Of Fetal Abdomen
• State Of The Art Of Mri In Diagnosis Of Hepatic Focal Lesions
• Role Of Multidetector Ct In Diagnosis Of Salivary Gland Lesions
• “Role Of Virtual Endoscopy Using Mdct In Detection & Evaluation Of Gastric Pathologies”
• The Role Of Ultrasound & Mri In Acute Pelvic Inflammatory Disease
• “Diagnosis & Staging Of Liver Fibrosis By Ultraso Und Elastography In
• Patients With Chronic Liver Diseases”
• Role Of Mri In Evaluation Of Spinal Trauma
• Validity Of Mri In Diagnosis Of Congenital Anorectal Anomalies
• Imaging Of Vascular Complication After Liver Transplantation
• “Contrast-Enhanced Digital Mammography And Digital Breast Tomosynthesis In Early Diagnosis Of Breast Lesion”
• Role Of Mammotome In Breast Lesions
• “Role Of MRI Diffusion Tensor Imaging (DTI) In Assessment Of Traumatic Spinal Cord Injuries”
• “Prediction Of Pre-eclampsia And Fetal Growth Restriction By Uterine Artery Doppler”
• “Role Of Multidetector Row Computed Tomography In Assessment Of Maxillofacial Trauma”
• “Role Of Diffusion Magnetic Resonance Imaging In Assessment Of Neoplastic And Inflammatory Brain Lesions”
• Role Of Diffusion Mri In Preoperative Evaluation Of Brain Neoplasms
• “Role Of Multidetector Ct Virtual Hysteroscopy In The Detection Of The
• Uterine & Tubal Causes Of Female Infertility”
• Role Of Advances Magnetic Resonance Imaging Sequences In Multiple Sclerosis Magnetic Resonance Spectroscopy In Multiple Sclerosis
• “Role Of Conventional Mri, And Diffusion Tensor Imaging Tractography In Evaluation Of Congenital Brain Malformations”
• Role Of MRI In Evaluation Of Spinal Trauma
• Diagnostic Role Of Diffusion-weighted MR Imaging In Neck Masses
• “The Role Of Transvaginal Ultrasound Versus Magnetic Resonance Imaging In Diagnosis & Evaluation Of Cancer Cervix”
• “Role Of 3d Magnetic Resonance Imaging Tractography In Assessment Of White Matter Tracts Compromise In Supra Tentorial Tumors”
• Role Of Proton MR Spectroscopy In The Evaluation Of Temporal Lobe Epilepsy
• Role Of Multislice Computed Tomography In Evaluation Of Crohn’s Disease
• Role Of MRI In Assessment Of Hydrocephalus In Pediatric Patients
• The Role Of MRI In Diagnosis And Staging Of Urinary Bladder Carcinoma
• USG and MRI correlation of congenital CNS anomalies
• HRCT in interstitial lung disease
• X-Ray, CT and MRI correlation of bone tumors
• “Study on the diagnostic and prognostic utility of X-Rays for cases of pulmonary tuberculosis under RNTCP”
• “Role of magnetic resonance imaging in the characterization of female adnexal  pathology”
• “CT angiography of carotid atherosclerosis and NECT brain in cerebral ischemia, a correlative analysis”
• Role of CT scan in the evaluation of paranasal sinus pathology
• USG and MRI correlation on shoulder joint pathology
• “Radiological evaluation of a patient presenting with extrapulmonary tuberculosis”
• CT and MRI correlation in focal liver lesions”
• Comparison of MDCT virtual cystoscopy with conventional cystoscopy in bladder tumors”
• “Bleeding vessels in life-threatening hemoptysis: Comparison of 64 detector row CT angiography with conventional angiography prior to endovascular management”
• “Role of transarterial chemoembolization in unresectable hepatocellular carcinoma”
• “Comparison of color flow duplex study with digital subtraction angiography in the evaluation of peripheral vascular disease”
• “A Study to assess the efficacy of magnetization transfer ratio in differentiating tuberculoma from neurocysticercosis”
• “MR evaluation of uterine mass lesions in correlation with transabdominal, transvaginal ultrasound using HPE as a gold standard”
• “The Role of power Doppler imaging with trans rectal ultrasonogram guided prostate biopsy in the detection of prostate cancer”
• “Lower limb arteries assessed with doppler angiography – A prospective comparative study with multidetector CT angiography”
• “Comparison of sildenafil with papaverine in penile doppler by assessing hemodynamic changes”
• “Evaluation of efficacy of sonosalphingogram for assessing tubal patency in infertile patients with hysterosalpingogram as the gold standard”
• Role of CT enteroclysis in the evaluation of small bowel diseases
• “MRI colonography versus conventional colonoscopy in the detection of colonic polyposis”
• “Magnetic Resonance Imaging of anteroposterior diameter of the midbrain – differentiation of progressive supranuclear palsy from Parkinson disease”
• “MRI Evaluation of anterior cruciate ligament tears with arthroscopic correlation”
• “The Clinicoradiological profile of cerebral venous sinus thrombosis with prognostic evaluation using MR sequences”
• “Role of MRI in the evaluation of pelvic floor integrity in stress incontinent patients” “Doppler ultrasound evaluation of hepatic venous waveform in portal hypertension before and after propranolol”
• “Role of transrectal sonography with colour doppler and MRI in evaluation of prostatic lesions with TRUS guided biopsy correlation”
• “Ultrasonographic evaluation of painful shoulders and correlation of rotator cuff pathologies and clinical examination”
• “Colour Doppler Evaluation of Common Adult Hepatic tumors More Than 2 Cm  with HPE and CECT Correlation”
• “Clinical Relevance of MR Urethrography in Obliterative Posterior Urethral Stricture”
• “Prediction of Adverse Perinatal Outcome in Growth Restricted Fetuses with Antenatal Doppler Study”
• Radiological evaluation of spinal dysraphism using CT and MRI
• “Evaluation of temporal bone in cholesteatoma patients by high resolution computed tomography”
• “Radiological evaluation of primary brain tumours using computed tomography and magnetic resonance imaging”
• “Three dimensional colour doppler sonographic assessment of changes in  volume and vascularity of fibroids – before and after uterine artery embolization”
• “In phase opposed phase imaging of bone marrow differentiating neoplastic lesions”
• “Role of dynamic MRI in replacing the isotope renogram in the functional evaluation of PUJ obstruction”
• Characterization of adrenal masses with contrast-enhanced CT – washout study
• A study on accuracy of magnetic resonance cholangiopancreatography
• “Evaluation of median nerve in carpal tunnel syndrome by high-frequency ultrasound & color doppler in comparison with nerve conduction studies”
• “Correlation of Agatston score in patients with obstructive and nonobstructive coronary artery disease following STEMI”
• “Doppler ultrasound assessment of tumor vascularity in locally advanced breast cancer at diagnosis and following primary systemic chemotherapy.”
• “Validation of two-dimensional perineal ultrasound and dynamic magnetic resonance imaging in pelvic floor dysfunction.”
• “Role of MR urethrography compared to conventional urethrography in the surgical management of obliterative urethral stricture.”

Search Diagnostic Imaging Research Topics

You can also search research-related resources on our custom search engine .

Free Resources for Preparing Radiology Thesis

• Radiology thesis topics- Benha University – Free to download thesis
• Radiology thesis topics – Faculty of Medical Science Delhi
• Radiology thesis topics – IPGMER
• Fetal Radiology thesis Protocols
• Radiology thesis and dissertation topics
• Radiographics

Proofreading Your Thesis:

Make sure you use Grammarly to correct your spelling ,  grammar , and plagiarism for your thesis. Grammarly has affordable paid subscriptions, windows/macOS apps, and FREE browser extensions. It is an excellent tool to avoid inadvertent spelling mistakes in your research projects. It has an extensive built-in vocabulary, but you should make an account and add your own medical glossary to it.

Guidelines for Writing a Radiology Thesis:

These are general guidelines and not about radiology specifically. You can share these with colleagues from other departments as well. Special thanks to Dr. Sanjay Yadav sir for these. This section is best seen on a desktop. Here are a couple of handy presentations to start writing a thesis:

Read the general guidelines for writing a thesis (the page will take some time to load- more than 70 pages!

A format for thesis protocol with a sample patient information sheet, sample patient consent form, sample application letter for thesis, and sample certificate.

Resources and References:

• Guidelines for thesis writing.
• Format for thesis protocol
• Thesis protocol writing guidelines DNB
• Informed consent form for Research studies from AIIMS 
• Radiology Informed consent forms in local Indian languages.
• Sample Informed Consent form for Research in Hindi
• Guide to write a thesis by Dr. P R Sharma
• Guidelines for thesis writing by Dr. Pulin Gupta.
• Preparing MD/DNB thesis by A Indrayan
• Another good thesis reference protocol

Hopefully, this post will make the tedious task of writing a Radiology thesis a little bit easier for you. Best of luck with writing your thesis and your residency too!

More guides for residents :

• Guide for the MD/DMRD/DNB radiology exam!
• Guide for First-Year Radiology Residents
• FRCR Exam: THE Most Comprehensive Guide (2022)!
• Radiology Practical Exams Questions compilation for MD/DNB/DMRD !
• Radiology Exam Resources (Oral Recalls, Instruments, etc )!
• Tips and Tricks for DNB/MD Radiology Practical Exam
• FRCR 2B exam- Tips and Tricks !
• FRCR exam preparation – An alternative take!
• Why did I take up Radiology?
• Radiology Conferences – A comprehensive guide!
• ECR (European Congress Of Radiology)
• European Diploma in Radiology (EDiR) – The Complete Guide!
• Radiology NEET PG guide – How to select THE best college for post-graduation in Radiology (includes personal insights)!
• Interventional Radiology – All Your Questions Answered!

What It Means To Be A Radiologist: A Guide For Medical Students!

• Radiology Mentors for Medical Students (Post NEET-PG)
• MD vs DNB Radiology: Which Path is Right for Your Career?

DNB Radiology OSCE – Tips and Tricks

More radiology resources here: Radiology resources This page will be updated regularly. Kindly leave your feedback in the comments or send us a message here . Also, you can comment below regarding your department’s thesis topics.

Note: All topics have been compiled from available online resources. If anyone has an issue with any radiology thesis topics displayed here, you can message us here , and we can delete them. These are only sample guidelines. Thesis guidelines differ from institution to institution.

Image source: Thesis complete! (2018). Flickr. Retrieved 12 August 2018, from https://www.flickr.com/photos/cowlet/354911838 by Victoria Catterson

About The Author

Dr. amar udare, md, related posts ↓.

7 thoughts on “Radiology Thesis – More than 400 Research Topics (2022)!”

Amazing & The most helpful site for Radiology residents…

Thank you for your kind comments 🙂

Dr. I saw your Tips is very amazing and referable. But Dr. Can you help me with the thesis of Evaluation of Diagnostic accuracy of X-ray radiograph in knee joint lesion.

Wow! These are excellent stuff. You are indeed a teacher. God bless

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Radiology Research Paper Topics

Radiology research paper topics encompass a wide range of fascinating areas within the field of medical imaging. This page aims to provide students studying health sciences with a comprehensive collection of radiology research paper topics to inspire and guide their research endeavors. By delving into various categories and exploring ten thought-provoking topics within each, students can gain insights into the diverse research possibilities in radiology. From advancements in imaging technology to the evaluation of diagnostic accuracy and the impact of radiological interventions, these topics offer a glimpse into the exciting world of radiology research. Additionally, expert advice is provided to help students choose the most suitable research topics and navigate the process of writing a research paper in radiology. By leveraging iResearchNet’s writing services, students can further enhance their research papers with professional assistance, ensuring the highest quality and adherence to academic standards. Explore the realm of radiology research paper topics and unleash your potential to contribute to the advancement of medical imaging and patient care.

100 Radiology Research Paper Topics

Radiology encompasses a broad spectrum of imaging techniques used to diagnose diseases, monitor treatment progress, and guide interventions. This comprehensive list of radiology research paper topics serves as a valuable resource for students in the field of health sciences who are seeking inspiration and guidance for their research endeavors. The following ten categories highlight different areas within radiology, each containing ten thought-provoking topics. Exploring these topics will provide students with a deeper understanding of the diverse research possibilities and current trends within the field of radiology.

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Diagnostic Imaging Techniques

• Comparative analysis of imaging modalities: CT, MRI, and PET-CT.
• The role of artificial intelligence in radiological image interpretation.
• Advancements in digital mammography for breast cancer screening.
• Emerging techniques in nuclear medicine imaging.
• Image-guided biopsy: Enhancing accuracy and safety.
• Application of radiomics in predicting treatment response.
• Dual-energy CT: Expanding diagnostic capabilities.
• Radiological evaluation of traumatic brain injuries.
• Imaging techniques for evaluating cardiovascular diseases.
• Radiographic evaluation of pulmonary nodules: Challenges and advancements.

Interventional Radiology

• Minimally invasive treatments for liver tumors: Embolization techniques.
• Radiofrequency ablation in the management of renal cell carcinoma.
• Role of interventional radiology in the treatment of peripheral artery disease.
• Transarterial chemoembolization in hepatocellular carcinoma.
• Evaluation of uterine artery embolization for the treatment of fibroids.
• Percutaneous vertebroplasty and kyphoplasty: Efficacy and complications.
• Endovascular repair of abdominal aortic aneurysms: Long-term outcomes.
• Interventional radiology in the management of deep vein thrombosis.
• Transcatheter aortic valve replacement: Imaging considerations.
• Emerging techniques in interventional oncology.

Radiation Safety and Dose Optimization

• Strategies for reducing radiation dose in pediatric imaging.
• Imaging modalities with low radiation exposure: Current advancements.
• Effective use of dose monitoring systems in radiology departments.
• The impact of artificial intelligence on radiation dose optimization.
• Optimization of radiation therapy treatment plans: Balancing efficacy and safety.
• Radioprotective measures for patients and healthcare professionals.
• The role of radiology in addressing radiation-induced risks.
• Evaluating the long-term effects of radiation exposure in diagnostic imaging.
• Radiation dose tracking and reporting: Implementing best practices.
• Patient education and communication regarding radiation risks.

Radiology in Oncology

• Imaging techniques for early detection and staging of lung cancer.
• Quantitative imaging biomarkers for predicting treatment response in solid tumors.
• Radiogenomics: Linking imaging features to genetic profiles in cancer.
• The role of imaging in assessing tumor angiogenesis.
• Radiological evaluation of lymphoma: Challenges and advancements.
• Imaging-guided interventions in the treatment of hepatocellular carcinoma.
• Assessment of tumor heterogeneity using functional imaging techniques.
• Radiomics and machine learning in predicting treatment outcomes in cancer.
• Multimodal imaging in the evaluation of brain tumors.
• Imaging surveillance after cancer treatment: Optimizing follow-up protocols.

Radiology in Musculoskeletal Disorders

• Imaging modalities in the evaluation of sports-related injuries.
• The role of imaging in diagnosing and monitoring rheumatoid arthritis.
• Assessment of bone health using dual-energy X-ray absorptiometry (DXA).
• Imaging techniques for evaluating osteoarthritis progression.
• Imaging-guided interventions in the management of musculoskeletal tumors.
• Role of imaging in diagnosing and managing spinal disorders.
• Evaluation of traumatic injuries using radiography, CT, and MRI.
• Imaging of joint prostheses: Complications and assessment techniques.
• Imaging features and classifications of bone fractures.
• Musculoskeletal ultrasound in the diagnosis of soft tissue injuries.

Neuroradiology

• Advanced neuroimaging techniques for early detection of neurodegenerative diseases.
• Imaging evaluation of acute stroke: Current guidelines and advancements.
• Role of functional MRI in mapping brain functions.
• Imaging of brain tumors: Classification and treatment planning.
• Diffusion tensor imaging in assessing white matter integrity.
• Neuroimaging in the evaluation of multiple sclerosis.
• Imaging techniques for the assessment of epilepsy.
• Radiological evaluation of neurovascular diseases.
• Imaging of cranial nerve disorders: Diagnosis and management.
• Radiological assessment of developmental brain abnormalities.

Pediatric Radiology

• Radiation dose reduction strategies in pediatric imaging.
• Imaging evaluation of congenital heart diseases in children.
• Role of imaging in the diagnosis and management of pediatric oncology.
• Imaging of pediatric gastrointestinal disorders.
• Evaluation of developmental hip dysplasia using ultrasound and radiography.
• Imaging features and management of pediatric musculoskeletal infections.
• Neuroimaging in the assessment of pediatric neurodevelopmental disorders.
• Radiological evaluation of pediatric respiratory conditions.
• Imaging techniques for the evaluation of pediatric abdominal emergencies.
• Imaging-guided interventions in pediatric patients.

Breast Imaging

• Advances in digital mammography for early breast cancer detection.
• The role of tomosynthesis in breast imaging.
• Imaging evaluation of breast implants: Complications and assessment.
• Radiogenomic analysis of breast cancer subtypes.
• Contrast-enhanced mammography: Diagnostic benefits and challenges.
• Emerging techniques in breast MRI for high-risk populations.
• Evaluation of breast density and its implications for cancer risk.
• Role of molecular breast imaging in dense breast tissue evaluation.
• Radiological evaluation of male breast disorders.
• The impact of artificial intelligence on breast cancer screening.

Cardiac Imaging

• Imaging evaluation of coronary artery disease: Current techniques and challenges.
• Role of cardiac CT angiography in the assessment of structural heart diseases.
• Imaging of cardiac tumors: Diagnosis and treatment considerations.
• Advanced imaging techniques for assessing myocardial viability.
• Evaluation of valvular heart diseases using echocardiography and MRI.
• Cardiac magnetic resonance imaging in the evaluation of cardiomyopathies.
• Role of nuclear cardiology in the assessment of cardiac function.
• Imaging evaluation of congenital heart diseases in adults.
• Radiological assessment of cardiac arrhythmias.
• Imaging-guided interventions in structural heart diseases.

Abdominal and Pelvic Imaging

• Evaluation of hepatobiliary diseases using imaging techniques.
• Imaging features and classification of renal masses.
• Radiological assessment of gastrointestinal bleeding.
• Imaging evaluation of pancreatic diseases: Challenges and advancements.
• Evaluation of pelvic floor disorders using MRI and ultrasound.
• Role of imaging in diagnosing and staging gynecological cancers.
• Imaging of abdominal and pelvic trauma: Current guidelines and techniques.
• Radiological evaluation of genitourinary disorders.
• Imaging features of abdominal and pelvic infections.
• Assessment of abdominal and pelvic vascular diseases using imaging techniques.

This comprehensive list of radiology research paper topics highlights the vast range of research possibilities within the field of medical imaging. Each category offers unique insights and avenues for exploration, enabling students to delve into various aspects of radiology. By choosing a topic of interest and relevance, students can contribute to the advancement of medical imaging and patient care. The provided topics serve as a starting point for students to engage in in-depth research and produce high-quality research papers.

Radiology: Exploring the Range of Research Paper Topics

Introduction: Radiology plays a crucial role in modern healthcare, providing valuable insights into the diagnosis, treatment, and monitoring of various medical conditions. As a dynamic and rapidly evolving field, radiology offers a wide range of research opportunities for students in the health sciences. This article aims to explore the diverse spectrum of research paper topics within radiology, shedding light on the current trends, innovations, and challenges in the field.

Radiology in Diagnostic Imaging : Diagnostic imaging is one of the core areas of radiology, encompassing various modalities such as X-ray, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, and nuclear medicine. Research topics in this domain may include advancements in imaging techniques, comparative analysis of modalities, radiomics, and the integration of artificial intelligence in image interpretation. Students can explore how these technological advancements enhance diagnostic accuracy, improve patient outcomes, and optimize radiation exposure.

Interventional Radiology : Interventional radiology focuses on minimally invasive procedures performed under image guidance. Research topics in this area can cover a wide range of interventions, such as angioplasty, embolization, radiofrequency ablation, and image-guided biopsies. Students can delve into the latest techniques, outcomes, and complications associated with interventional procedures, as well as explore the emerging role of interventional radiology in managing various conditions, including vascular diseases, cancer, and pain management.

Radiation Safety and Dose Optimization : Radiation safety is a critical aspect of radiology practice. Research in this field aims to minimize radiation exposure to patients and healthcare professionals while maintaining optimal diagnostic image quality. Topics may include strategies for reducing radiation dose in pediatric imaging, dose monitoring systems, the impact of artificial intelligence on radiation dose optimization, and radioprotective measures. Students can investigate how to strike a balance between effective imaging and patient safety, exploring advancements in dose reduction techniques and the implementation of best practices.

Radiology in Oncology : Radiology plays a vital role in the diagnosis, staging, and treatment response assessment in cancer patients. Research topics in this area can encompass the use of imaging techniques for early detection, tumor characterization, response prediction, and treatment planning. Students can explore the integration of radiomics, machine learning, and molecular imaging in oncology research, as well as advancements in functional imaging and image-guided interventions.

Radiology in Neuroimaging : Neuroimaging is a specialized field within radiology that focuses on imaging the brain and central nervous system. Research topics in neuroimaging can cover areas such as stroke imaging, neurodegenerative diseases, brain tumors, neurovascular disorders, and functional imaging for mapping brain functions. Students can explore the latest imaging techniques, image analysis tools, and their clinical applications in understanding and diagnosing various neurological conditions.

Radiology in Musculoskeletal Imaging : Musculoskeletal imaging involves the evaluation of bone, joint, and soft tissue disorders. Research topics in this area can encompass imaging techniques for sports-related injuries, arthritis, musculoskeletal tumors, spinal disorders, and trauma. Students can explore the role of advanced imaging modalities such as MRI and ultrasound in diagnosing and managing musculoskeletal conditions, as well as the use of imaging-guided interventions for treatment.

Pediatric Radiology : Pediatric radiology focuses on imaging children, who have unique anatomical and physiological considerations. Research topics in this field may include radiation dose reduction strategies in pediatric imaging, imaging evaluation of congenital anomalies, pediatric oncology imaging, and imaging assessment of developmental disorders. Students can explore how to tailor imaging protocols for children, minimize radiation exposure, and improve diagnostic accuracy in pediatric patients.

Breast Imaging : Breast imaging is essential for the early detection and diagnosis of breast cancer. Research topics in this area can cover advancements in mammography, tomosynthesis, breast MRI, and molecular imaging. Students can explore topics related to breast density, imaging-guided biopsies, breast cancer screening, and the impact of artificial intelligence in breast imaging. Additionally, they can investigate the use of imaging techniques for evaluating breast implants and assessing high-risk populations.

Cardiac Imaging : Cardiac imaging focuses on the evaluation of heart structure and function. Research topics in this field may include imaging techniques for coronary artery disease, valvular heart diseases, cardiomyopathies, and cardiac tumors. Students can explore the role of cardiac CT, MRI, nuclear cardiology, and echocardiography in diagnosing and managing various cardiac conditions. Additionally, they can investigate the use of imaging in guiding interventional procedures and assessing treatment outcomes.

Abdominal and Pelvic Imaging : Abdominal and pelvic imaging involves the evaluation of organs and structures within the abdominal and pelvic cavities. Research topics in this area can encompass imaging of the liver, kidneys, gastrointestinal tract, pancreas, genitourinary system, and pelvic floor. Students can explore topics related to imaging techniques, evaluation of specific diseases or conditions, and the role of imaging in guiding interventions. Additionally, they can investigate emerging modalities such as elastography and diffusion-weighted imaging in abdominal and pelvic imaging.

Radiology offers a vast array of research opportunities for students in the field of health sciences. The topics discussed in this article provide a glimpse into the breadth and depth of research possibilities within radiology. By exploring these research areas, students can contribute to advancements in diagnostic accuracy, treatment planning, and patient care. With the rapid evolution of imaging technologies and the integration of artificial intelligence, the future of radiology research holds immense potential for improving healthcare outcomes.

Choosing Radiology Research Paper Topics

Introduction: Selecting a research topic is a crucial step in the journey of writing a radiology research paper. It determines the focus of your study and influences the impact your research can have in the field. To help you make an informed choice, we have compiled expert advice on selecting radiology research paper topics. By following these tips, you can identify a relevant and engaging research topic that aligns with your interests and contributes to the advancement of radiology knowledge.

• Identify Your Interests : Start by reflecting on your own interests within the field of radiology. Consider which subspecialties or areas of radiology intrigue you the most. Are you interested in diagnostic imaging, interventional radiology, radiation safety, oncology imaging, or any other specific area? Identifying your interests will guide you in selecting a topic that excites you and keeps you motivated throughout the research process.
• Stay Updated on Current Trends : Keep yourself updated on the latest advancements, breakthroughs, and emerging trends in radiology. Read scientific journals, attend conferences, and engage in discussions with experts in the field. By staying informed, you can identify gaps in knowledge or areas that require further investigation, providing you with potential research topics that are timely and relevant.
• Consult with Faculty or Mentors : Seek guidance from your faculty members or mentors who are experienced in the field of radiology. They can provide valuable insights into potential research areas, ongoing projects, and research gaps. Discuss your research interests with them and ask for their suggestions and recommendations. Their expertise and guidance can help you narrow down your research topic and refine your research question.
• Conduct a Literature Review : Conducting a thorough literature review is an essential step in choosing a research topic. It allows you to familiarize yourself with the existing body of knowledge, identify research gaps, and build a strong foundation for your study. Analyze recent research papers, systematic reviews, and meta-analyses related to radiology to identify areas that need further investigation or where controversies exist.
• Brainstorm Research Questions : Once you have gained an understanding of the current state of research in radiology, brainstorm potential research questions. Consider the gaps or controversies you identified during your literature review. Develop research questions that address these gaps and contribute to the existing knowledge. Ensure that your research questions are clear, focused, and answerable within the scope of your study.
• Consider the Practicality and Feasibility : When selecting a research topic, consider the practicality and feasibility of conducting the study. Evaluate the availability of resources, access to data, research facilities, and ethical considerations. Assess the time frame and potential constraints that may impact your research. Choosing a topic that is feasible within your given resources and time frame will ensure a successful and manageable research experience.
• Collaborate with Peers : Consider collaborating with your peers or forming a research group to enhance your research experience. Collaborative research allows for a sharing of ideas, resources, and expertise, fostering a supportive environment. By working together, you can explore more complex research topics, conduct multicenter studies, and generate more impactful findings.
• Seek Multidisciplinary Perspectives : Radiology intersects with various other medical disciplines. Consider exploring interdisciplinary research topics that integrate radiology with fields such as oncology, cardiology, neurology, or orthopedics. By incorporating multidisciplinary perspectives, you can address complex healthcare challenges and contribute to a broader understanding of patient care.
• Choose a Topic with Clinical Relevance : Select a research topic that has direct clinical relevance. Focus on topics that can potentially influence patient outcomes, improve diagnostic accuracy, optimize treatment strategies, or enhance patient safety. By choosing a clinically relevant topic, you can contribute to the advancement of radiology practice and have a positive impact on patient care.
• Seek Ethical Considerations : Ensure that your research topic adheres to ethical considerations in radiology research. Patient privacy, confidentiality, and informed consent should be prioritized when conducting studies involving human subjects. Familiarize yourself with the ethical guidelines and regulations specific to radiology research and ensure that your study design and data collection methods are in line with these principles.

Choosing a radiology research paper topic requires careful consideration and alignment with your interests, expertise, and the current trends in the field. By following the expert advice provided in this section, you can select a research topic that is engaging, relevant, and contributes to the advancement of radiology knowledge. Remember to consult with mentors, conduct a thorough literature review, and consider practicality and feasibility. With a well-chosen research topic, you can embark on an exciting journey of exploration, innovation, and contribution to the field of radiology.

How to Write a Radiology Research Paper

Introduction: Writing a radiology research paper requires a systematic approach and attention to detail. It is essential to effectively communicate your research findings, methodology, and conclusions to contribute to the body of knowledge in the field. In this section, we will provide you with valuable tips on how to write a successful radiology research paper. By following these guidelines, you can ensure that your paper is well-structured, informative, and impactful.

• Define the Research Question : Start by clearly defining your research question or objective. It serves as the foundation of your research paper and guides your entire study. Ensure that your research question is specific, focused, and relevant to the field of radiology. Clearly articulate the purpose of your study and its potential implications.
• Conduct a Thorough Literature Review : Before diving into writing, conduct a comprehensive literature review to familiarize yourself with the existing body of knowledge in your research area. Identify key studies, seminal papers, and relevant research articles that will support your research. Analyze and synthesize the literature to identify gaps, controversies, or areas for further investigation.
• Develop a Well-Structured Outline : Create a clear and well-structured outline for your research paper. An outline serves as a roadmap and helps you organize your thoughts, arguments, and evidence. Divide your paper into logical sections such as introduction, literature review, methodology, results, discussion, and conclusion. Ensure a logical flow of ideas and information throughout the paper.
• Write an Engaging Introduction : The introduction is the opening section of your research paper and should capture the reader’s attention. Start with a compelling hook that introduces the importance of the research topic. Provide background information, context, and the rationale for your study. Clearly state the research question or objective and outline the structure of your paper.
• Conduct Rigorous Methodology : Describe your research methodology in detail, ensuring transparency and reproducibility. Explain your study design, data collection methods, sample size, inclusion/exclusion criteria, and statistical analyses. Clearly outline the steps you took to ensure scientific rigor and address potential biases. Include any ethical considerations and institutional review board approvals, if applicable.
• Present Clear and Concise Results : Present your research findings in a clear, concise, and organized manner. Use tables, figures, and charts to visually represent your data. Provide accurate and relevant statistical analyses to support your results. Explain the significance and implications of your findings and their alignment with your research question.
• Analyze and Interpret Results : In the discussion section, analyze and interpret your research results in the context of existing literature. Compare and contrast your findings with previous studies, highlighting similarities, differences, and potential explanations. Discuss any limitations or challenges encountered during the study and propose areas for future research.
• Ensure Clear and Coherent Writing : Maintain clarity, coherence, and precision in your writing. Use concise and straightforward language to convey your ideas effectively. Avoid jargon or excessive technical terms that may hinder understanding. Clearly define any acronyms or abbreviations used in your paper. Ensure that each paragraph has a clear topic sentence and flows smoothly into the next.
• Citations and References : Properly cite all the sources used in your research paper. Follow the citation style recommended by your institution or the journal you intend to submit to (e.g., APA, MLA, or Chicago). Include in-text citations for direct quotes, paraphrased information, or any borrowed ideas. Create a comprehensive reference list at the end of your paper, following the formatting guidelines.
• Revise and Edit : Take the time to revise and edit your research paper before final submission. Review the content, structure, and organization of your paper. Check for grammatical errors, spelling mistakes, and typos. Ensure that your paper adheres to the specified word count and formatting guidelines. Seek feedback from colleagues or mentors to gain valuable insights and suggestions for improvement.

Conclusion: Writing a radiology research paper requires careful planning, attention to detail, and effective communication. By following the tips provided in this section, you can write a well-structured and impactful research paper in the field of radiology. Define a clear research question, conduct a thorough literature review, develop a strong outline, and present your findings with clarity. Remember to adhere to proper citation guidelines and revise your paper before submission. With these guidelines in mind, you can contribute to the advancement of radiology knowledge and make a meaningful impact in the field.

iResearchNet’s Writing Services

Introduction: At iResearchNet, we understand the challenges faced by students in the field of health sciences when it comes to writing research papers, including those in radiology. Our writing services are designed to provide you with expert assistance and support throughout your research paper journey. With our team of experienced writers, in-depth research capabilities, and commitment to excellence, we offer a range of services that will help you achieve your academic goals and ensure the success of your radiology research papers.

• Expert Degree-Holding Writers : Our team consists of expert writers who hold advanced degrees in various fields, including radiology and health sciences. They possess extensive knowledge and expertise in their respective areas, allowing them to deliver high-quality and well-researched papers.
• Custom Written Works : We understand that each research paper is unique, and we tailor our services to meet your specific requirements. Our writers craft custom-written research papers that align with your research objectives, ensuring originality and authenticity in every piece.
• In-Depth Research : Research is at the core of any high-quality paper. Our writers conduct comprehensive and in-depth research to gather relevant literature, scientific articles, and other credible sources to support your research paper. They have access to reputable databases and libraries to ensure that your paper is backed by the latest and most reliable information.
• Custom Formatting : Formatting your research paper according to the specified guidelines can be a challenging task. Our writers are well-versed in various formatting styles, including APA, MLA, Chicago/Turabian, and Harvard. They ensure that your paper adheres to the required formatting standards, including citations, references, and overall document structure.
• Top Quality : We prioritize delivering top-quality research papers that meet the highest academic standards. Our writers pay attention to detail, ensuring accurate information, logical flow, and coherence in your paper. We conduct thorough editing and proofreading to eliminate any errors and improve the overall quality of your work.
• Customized Solutions : We understand that every student has unique research requirements. Our services are tailored to provide customized solutions that address your specific needs. Whether you need assistance with topic selection, literature review, methodology, data analysis, or any other aspect of your research paper, we are here to support you at every step.
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Don’t let the complexities of choosing a research topic hold you back. Our expert advice on selecting radiology research paper topics will guide you through the process, ensuring that you choose a topic that aligns with your interests and has the potential to make a meaningful contribution to the field of radiology.

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• Published: 12 April 2022

Machine learning for medical imaging: methodological failures and recommendations for the future

• Gaël Varoquaux 1 , 2 , 3 &
• Veronika Cheplygina   ORCID: orcid.org/0000-0003-0176-9324 4  

npj Digital Medicine volume  5 , Article number:  48 ( 2022 ) Cite this article

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Research in computer analysis of medical images bears many promises to improve patients’ health. However, a number of systematic challenges are slowing down the progress of the field, from limitations of the data, such as biases, to research incentives, such as optimizing for publication. In this paper we review roadblocks to developing and assessing methods. Building our analysis on evidence from the literature and data challenges, we show that at every step, potential biases can creep in. On a positive note, we also discuss on-going efforts to counteract these problems. Finally we provide recommendations on how to further address these problems in the future.

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Shih-Cheng Huang, Anuj Pareek, … Akshay S. Chaudhari

Introduction

Machine learning, the cornerstone of today’s artificial intelligence (AI) revolution, brings new promises to clinical practice with medical images 1 , 2 , 3 . For example, to diagnose various conditions from medical images, machine learning has been shown to perform on par with medical experts 4 . Software applications are starting to be certified for clinical use 5 , 6 . Machine learning may be the key to realizing the vision of AI in medicine sketched several decades ago 7 .

The stakes are high, and there is a staggering amount of research on machine learning for medical images. But this growth does not inherently lead to clinical progress. The higher volume of research could be aligned with the academic incentives rather than the needs of clinicians and patients. For example, there can be an oversupply of papers showing state-of-the-art performance on benchmark data, but no practical improvement for the clinical problem. On the topic of machine learning for COVID, Robert et al. 8 reviewed 62 published studies, but found none with potential for clinical use.

In this paper, we explore avenues to improve clinical impact of machine learning in medical imaging. After sketching the situation, documenting uneven progress in Section It’s not all about larger datasets, we study a number of failures frequent in medical imaging papers, at different steps of the “publishing lifecycle”: what data to use (Section Data, an imperfect window on the clinic), what methods to use and how to evaluate them (Section Evaluations that miss the target), and how to publish the results (Section Publishing, distorted incentives). In each section, we first discuss the problems, supported with evidence from previous research as well as our own analyses of recent papers. We then discuss a number of steps to improve the situation, sometimes borrowed from related communities. We hope that these ideas will help shape research practices that are even more effective at addressing real-world medical challenges.

It’s not all about larger datasets

The availability of large labeled datasets has enabled solving difficult machine learning problems, such as natural image recognition in computer vision, where datasets can contain millions of images. As a result, there is widespread hope that similar progress will happen in medical applications, algorithm research should eventually solve a clinical problem posed as discrimination task. However, medical datasets are typically smaller, on the order of hundreds or thousands: 9 share a list of sixteen “large open source medical imaging datasets”, with sizes ranging from 267 to 65,000 subjects. Note that in medical imaging we refer to the number of subjects, but a subject may have multiple images, for example, taken at different points in time. For simplicity here we assume a diagnosis task with one image/scan per subject.

Few clinical questions come as well-posed discrimination tasks that can be naturally framed as machine-learning tasks. But, even for these, larger datasets have to date not lead to the progress hoped for. One example is that of early diagnosis of Alzheimer’s disease (AD), which is a growing health burden due to the aging population. Early diagnosis would open the door to early-stage interventions, most likely to be effective. Substantial efforts have acquired large brain-imaging cohorts of aging individuals at risk of developing AD, on which early biomarkers can be developed using machine learning 10 . As a result, there have been steady increases in the typical sample size of studies applying machine learning to develop computer-aided diagnosis of AD, or its predecessor, mild cognitive impairment. This growth is clearly visible in publications, as on Fig. 1 a, a meta-analysis compiling 478 studies from 6 systematic reviews 4 , 11 , 12 , 13 , 14 , 15 .

A meta-analysis across 6 review papers, covering more than 500 individual publications. The machine-learning problem is typically formulated as distinguishing various related clinical conditions, Alzheimer’s Disease (AD), Healthy Control (HC), and Mild Cognitive Impairment, which can signal prodromal Alzheimer’s . Distinguishing progressive mild cognitive impairment (pMCI) from stable mild cognitive impairment (sMCI) is the most relevant machine-learning task from the clinical standpoint. a Reported sample size as a function of the publication year of a study. b Reported prediction accuracy as a function of the number of subjects in a study. c Same plot distinguishing studies published in different years.

However, the increase in data size (with the largest datasets containing over a thousand subjects) did not come with better diagnostic accuracy, in particular for the most clinically relevant question, distinguishing pathological versus stable evolution for patients with symptoms of prodromal Alzheimer’s (Fig. 1 b). Rather, studies with larger sample sizes tend to report worse prediction accuracy. This is worrisome, as these larger studies are closer to real-life settings. On the other hand, research efforts across time did lead to improvements even on large, heterogeneous cohorts (Fig. 1 c), as studies published later show improvements for large sample sizes (statistical analysis in Supplementary Information) . Current medical-imaging datasets are much smaller than those that brought breakthroughs in computer vision. Although a one-to-one comparison of sizes cannot be made, as computer vision datasets have many classes with high variation (compared to few classes with less variation in medical imaging), reaching better generalization in medical imaging may require assembling significantly larger datasets, while avoiding biases created by opportunistic data collection, as described below.

Data, an imperfect window on the clinic

Datasets may be biased: reflect an application only partly.

Available datasets only partially reflect the clinical situation for a particular medical condition, leading to dataset bias 16 . As an example, a dataset collected as part of a population study might have different characteristics that people who are referred to the hospital for treatment (higher incidence of a disease). As the researcher may be unaware of the corresponding dataset bias is can lead to important that shortcomings of the study. Dataset bias occurs when the data used to build the decision model (the training data), has a different distribution than the data on which it should be applied 17 (the test data). To assess clinically-relevant predictions, the test data must match the actual target population, rather than be a random subset of the same data pool as the train data, the common practice in machine-learning studies. With such a mismatch, algorithms which score high in benchmarks can perform poorly in real world scenarios 18 . In medical imaging, dataset bias has been demonstrated in chest X-rays 19 , 20 , 21 , retinal imaging 22 , brain imaging 23 , 24 , histopathology 25 , or dermatology 26 . Such biases are revealed by training and testing a model across datasets from different sources, and observing a performance drop across sources.

There are many potential sources of dataset bias in medical imaging, introduced at different phases of the modeling process 27 . First, a cohort may not appropriately represent the range of possible patients and symptoms, a bias sometimes called spectrum bias 28 . A detrimental consequence is that model performance can be overestimated for different groups, for example between male and female individuals 21 , 26 . Yet medical imaging publications do not always report the demographics of the data.

Imaging devices or procedures may lead to specific measurement biases. A bias particularly harmful to clinically relevant automated diagnosis is when the data capture medical interventions. For instance, on chest X-ray datasets, images for the “pneumothorax” condition sometimes show a chest drain, which is a treatment for this condition, and which would not yet be present before diagnosis 29 . Similar spurious correlations can appear in skin lesion images due to markings placed by dermatologists next to the lesions 30 .

Labeling errors can also introduce biases. Expert human annotators may have systematic biases in the way they assign different labels 31 , and it is seldom possible to compensate with multiple annotators. Using automatic methods to extract labels from patient reports can also lead to systematic errors 32 . For example, a report on a follow-up scan that does not mention previously-known findings, can lead to an incorrect “negative” labels.

Dataset availability distorts research

The availability of datasets can influence which applications are studied more extensively. A striking example can be seen in two applications of oncology: detecting lung nodules, and detecting breast tumors in radiological images. Lung datasets are widely available on Kaggle or grand-challenge.org , contrasted with (to our knowledge) only one challenge focusing on mammograms. We look at the popularity of these topics, here defined by the fraction of papers focusing on lung or breast imaging, either in literature on general medical oncology, or literature on AI. In medical oncology this fraction is relatively constant across time for both lung and breast imaging, but in the AI literature lung imaging publications show a substantial increase in 2016 (Fig. 2 , methodological details in Supplementary Information ). We suspect that the Kaggle lung challenges published around that time contributed to this disproportional increase. A similar point on dataset trends has been made throughout the history of machine learning in general 33 .

We show the percentage of papers on lung cancer (in blue) vs breast cancer (in red), relative to all papers within two fields: medical oncology (solid line) and AI (dotted line). Details on how the papers are selected are given in the Supplementary Information) . The percentages are relatively constant, except lung cancer in AI, which shows an increase after 2016.

Let us build awareness of data limitations

Addressing such problems arising from the data requires critical thinking about the choice of datasets, at the project level, i.e. which datasets to select for a study or a challenge, and at a broader level, i.e. which datasets we work on as a community.

At the project level, the choice of the dataset will influence the models trained on the data, and the conclusions we can draw from the results. An important step is using datasets from multiple sources, or creating robust datasets from the start when feasible 9 . However, existing datasets can still be critically evaluated for dataset bias 34 , hidden subgroups of patients 29 , or mislabeled instances 35 . A checklist for such evaluation on computer vision datasets is presented in Zendel et al. 18 . When problems are discovered, relabeling a subset of the data can be a worthwhile investment 36 .

At the community level, we should foster understanding of the datasets’ limitations. Good documentation of datasets should describe their characteristics and data collection 37 . Distributed models should detail their limitations and the choices made to train them 38 .

Meta-analyses which look at evolution of dataset use in different areas are another way to reflect on current research efforts. For example, a survey of crowdsourcing in medical imaging 39 shows a different distribution of applications than surveys focusing on machine learning 1 , 2 . Contrasting more clinically-oriented venues to more technical venues can reveal opportunities for machine learning research.

Evaluations that miss the target

Evaluation error is often larger than algorithmic improvements.

Research on methods often focuses on outperforming other algorithms on benchmark datasets. But too strong a focus on benchmark performance can lead to diminishing returns , where increasingly large efforts achieve smaller and smaller performance gains. Is this also visible in the development of machine learning in medical imaging?

We studied performance improvements in 8 Kaggle medical-imaging challenges, 5 on detection of diagnosis of diseases and 3 on image segmentation (details in Supplementary Information) . We use the differences in algorithms performance between the public and private leaderboards (two test sets used in the challenge) to quantify the evaluation noise –the spread of performance differences between the public and private test sets–, in Fig. 3 . We compare its distribution to the winner gap —the difference in performance between the best algorithm, and the “top 10%” algorithm.

The blue violin plot shows the evaluation noise —the distribution of differences between public and private leaderboards. A systematic shift between public and private set (positive means that the private leaderboard is better than the public leaderboard) indicates overfitting or dataset bias. The width of this distribution shows how noisy the evaluation is, or how representative the public score is for the private score. The brown bar is the winner gap , the improvement between the top-most model (the winner) and the 10% best model. It is interesting to compare this improvement to the shift and width in the difference between the public and private sets: if the winner gap is smaller, the 10% best models reached diminishing returns and did not lead to a actual improvement on new data.

Overall, 6 of the 8 challenges are in the diminishing returns category. For 5 challenges—lung cancer, schizophrenia, prostate cancer diagnosis and intracranial hemorrhage detection—the evaluation noise is worse than the winner gap. In other words, the gains made by the top 10% of methods are smaller than the expected noise when evaluating a method.

For another challenge, pneumothorax segmentation, the performance on the private set is worse than on the public set, revealing an overfit larger than the winner gap. Only two challenges (covid 19 abnormality and nerve segmentation) display a winner gap larger than the evaluation noise, meaning that the winning method made substantial improvements compared to the 10% competitor.

Improper evaluation procedures and leakage

Unbiased evaluation of model performance relies on training and testing the models with independent sets of data 40 . However incorrect implementations of this procedure can easily leak information, leading to overoptimistic results. For example some studies classifying ADHD based on brain imaging have engaged in circular analysis 41 , performing feature selection on the full dataset, before cross-validation. Another example of leakage arises when repeated measures of an individual are split across train and test set, the algorithm then learning to recognize the individual patient rather than markers of a condition 42 .

A related issue, yet more difficult to detect, is what we call “overfitting by observer”: even when using cross-validation, overfitting may still occur by the researcher adjusting the method to improve the observed cross-validation performance, which essentially includes the test folds into the validation set of the model. Skocik et al. 43 provide an illustration of this phenomenon by showing how by adjusting the model this way can lead to better-than-random cross-validation performance for randomly generated data. This can explain some of the overfitting visible in challenges (Section Evaluation error is often larger than algorithmic improvements), though with challenges a private test set reveals the overfitting, which is often not the case for published studies. Another recommendation for challenges would be to hold out several datasets (rather than a part of the same dataset), as is for example done in the Decathlon challenge 44 .

Metrics that do not reflect what we want

Evaluating models requires choosing a suitable metric. However, our understanding of “suitable” may change over time. For example, an image similarity metric which was widely used to evaluate image registration algorithms, was later shown to be ineffective as scrambled images could lead to high scores 45 .

In medical image segmentation, Maier-Hein et al. 46 review 150 challenges and show that the typical metrics used to rank algorithms are sensitive to different variants of the same metric, casting doubt on the objectivity of any individual ranking.

Important metrics may be missing from evaluation. Next to typical classification metrics (sensitivity, specificity, area under the curve), several authors argue for a calibration metric that compares the predicted and observed probabilities 28 , 47 .

Finally, the metrics used may not be synonymous with practical improvement 48 , 49 . For example, typical metrics in computer vision do not reflect important aspects of image recognition, such as robustness to out-of-distribution examples 49 . Similarly, in medical imaging, improvements in traditional metrics may not necessarily translate to different clinical outcomes, e.g. robustness may be more important than an accurate delineation in a segmentation application.

Incorrectly chosen baselines

Developing new algorithms builds upon comparing these to baselines. However, if these baselines are poorly chosen, the reported improvement may be misleading.

Baselines may not properly account for recent progress, as revealed in machine-learning applications to healthcare 50 , but also other applications of machine learning 51 , 52 , 53 .

Conversely, one should not forget simple approaches effective for the problem at hand. For example, Wen et al. 14 show that convolutional neural networks do not outperform support vector machines for Alzheimer’s disease diagnosis from brain imaging.

Finally, minute implementation details of algorithms may be important and many are not aware of implementation factors 54 .

Statistical significance not tested, or misunderstood

Experimental results are by nature noisy: results may depend on which specific samples were used to train the models, the random initializations, small differences in hyper-parameters 55 . However, benchmarking predictive models currently lacks well-adopted statistical good practices to separate out noise from generalizable findings.

A first, well-documented, source of brittleness arises from machine-learning experiments with too small sample sizes 56 . Indeed, testing predictive modeling requires many samples, more than conventional inferential studies, else the measured prediction accuracy may be a distant estimation of real-life performance. Sample sizes are growing, albeit slowly 57 . On a positive note, a meta-analysis of public vs private leaderboards on Kaggle 58 suggests that overfitting is less of an issue with “large enough” test data (at least several thousands).

Another challenge is that strong validation of a method requires it to be robust to details of the data. Hence validation should go beyond a single dataset, and rather strive for statistical consensus across multiple datasets 59 . Yet, the corresponding statistical procedures require dozens of datasets to establish significance and are seldom used in practice. Rather, medical imaging research often reuses the same datasets across studies, which raises the risk of finding an algorithm that performs well by chance, in an implicit multiple comparison problem 60 .

But overall medical imaging research seldom analyzes how likely empirical results are to be due to chance: only 6% of segmentation challenges surveyed 61 , and 15% out of 410 popular computer science papers published by ACM used a statistical test 62 .

However, null-hypothesis tests are often misinterpreted 63 , with two notable challenges: (1) the lack of statistically significant results does not demonstrate the absence of effect, and (2) any trivial effect can be significant given enough data 64 , 65 . For these reasons, Bouthiellier et al. 66 recommend to replace traditional null-hypothesis testing with superiority testing , testing that the improvement is above a given threshold.

Let us redefine evaluation

Higher standards for benchmarking.

Good machine-learning benchmarks are difficult. We compile below several recognized best practices for medical machine learning evaluation 28 , 40 , 67 , 68 :

Safeguarding from data leakage by separating out all test data from the start, before any data transformation.

A documented way of selecting model hyper-parameters (including architectural parameters for neural networks, the use of additional (unlabeled) dataset or transfer learning 2 ), without ever using data from the test set.

Enough data in the test set to bring statistical power, at least several hundreds samples, ideally thousands or more 9 , and confidence intervals on the reported performance metric—see Supplementary Information . In general, more research on appropriate sample sizes for machine learning studies would be helpful.

Rich data to represent the diversity of patients and disease heterogeneity, ideally multi-institutional data including all relevant patient demographics and disease state, with explicit inclusion criteria; other cohorts with different recruitment go the extra mile to establish external validity 69 , 70 .

Strong baselines that reflect the state of the art of machine-learning research, but also historical solutions including clinical methodologies not necessarily relying on medical imaging.

A discussion the variability of the results due to arbitrary choices (random seeds) and data sources with an eye on statistical significance—see Supplementary Information .

Using different quantitative metrics to capture the different aspects of the clinical problem and relating them to relevant clinical performance metrics. In particular, the potential health benefits from a detection of the outcome of interest should be used to choose the right trade off between false detections and misses 71 .

Adding qualitative accounts and involving groups that will be most affected by the application in the metric design 72 .

More than beating the benchmark

Even with proper validation and statistical significance testing, measuring a tiny improvement on a benchmark is seldom useful. Rather, one view is that, beyond rejecting a null, a method should be accepted based on evidence that it brings a sizable improvement upon the existing solutions. This type of criteria is related to superiority tests sometimes used in clinical trials 73 , 74 , 75 . These tests are easy to implement in predictive modeling benchmarks, as they amount to comparing the observed improvement to variation of the results due to arbitrary choices such as data sampling or random seeds 55 .

Organizing blinded challenges, with a hidden test set, mitigate the winner’s curse. But to bring progress, challenges should not only focus on the winner. Instead, more can be learned by comparing the competing methods and analyzing the determinants of success, as well as failure cases.

Evidence-based medicine good practices

A machine-learning algorithm deployed in clinical practice is a health intervention. There is a well-established practice to evaluate the impact of health intervention, building mostly on randomized clinical trials 76 . These require actually modifying patients’ treatments and thus should be run only after thorough evaluation on historical data.

A solid trial evaluates a well-chosen measure of patient health outcome, as opposed to predictive performance of an algorithm. Many indirect mechanisms may affect this outcome, including how the full care processes adapts to the computer-aided decision. For instance, a positive consequence of even imperfect predictions may be reallocating human resources to complex cases. But a negative consequence may be over-confidence leading to an increase in diagnostic errors. Cluster randomized trials can account for how modifications at the level of care unit impact the individual patient: care units, rather than individuals are randomly allocated to receive the intervention (the machine learning algorithm) 77 . Often, double blind is impossible: the care provider is aware of which arm of the study is used, the baseline condition or the system evaluated. Providers’ expectations can contribute to the success of a treatment, for instance via indirect placebo or nocebo effects 78 , making objective evaluation of the health benefits challenging, if these are small.

Publishing, distorted incentives

No incentive for clarity.

The publication process does not create incentives for clarity. Efforts to impress may give rise to unnecessary “mathiness” of papers or suggestive language 79 (such as “human-level performance”).

Important details may be omitted, from ablation experiments showing what part of the method drives improvements 79 , to reporting how algorithms were evaluated in a challenge [ 46 ]. This in turn undermines reproducibility: being able to reproduce the exact results or even draw the same conclusions 80 , 81 .

Optimizing for publication

As researchers our goal should be to solve scientific problems. Yet, the reality of the culture we exist in can distort this objective. Goodhart’s law summarizes well the problem: when a measure becomes a target, it ceases to be a good measure . As our academic incentive system is based publications, it erodes their scientific content via Goodhart’s law.

Methods publication are selected for their novelty. Yet, comparing 179 classifiers on 121 datasets shows no statistically significant differences between the top methods [ 82 ]. In order to sustain novelty, researchers may be introducing unnecessary complexity into the methods, that do not improve their prediction but rather contribute to technical debt, making systems harder to maintain and deploy 83 .

Another metric emphasized is obtaining “state-of-the-art” results, which leads to several of the evaluation problems outlined in Section Evaluations that miss the target. The pressure to publish “good” results can aggravate methodological loopholes 84 , for instance gaming the evaluation in machine learning 85 . It is then all too appealing to find after-the-fact theoretical justifications of positive yet fragile empirical findings. This phenomenon, known as HARKing (hypothesizing after the results are known) 86 , has been documented in machine learning 87 and computer science in general 62 .

Finally, the selection of publications creates the so-called “file drawer problem” 88 : positive results, some due to experimental flukes, are more likely to be published than corresponding negative findings. For example, in 410 most downloaded papers from the ACM, 97% of the papers which used significance testing had a finding with p -value of less than 0.05 62 . It seems highly unlikely that only 3% of the initial working hypotheses—even for impactful work—turned out not confirmed.

Let us improve our publication norms

Fortunately there are various alleys to improve reporting and transparency. For instance, the growing set of open datasets could be leveraged for collaborative work beyond the capacities of a single team 89 . The set of metrics studied could then be broadened, shifting the publication focus away from a single-dimension benchmark. More metrics can indeed help understanding a method’s strengths and weaknesses 41 , 90 , 91 , exploring for instance calibration metrics 28 , 47 , 92 or learning curves 93 . The medical-research literature has several reporting guidelines for prediction studies 67 , 94 , 95 . They underline many points raised in previous sections: reporting on how representative the study sample is, on the separation between train and test data, on the motivation for the choice of outcome, evaluation metrics, and so forth. Unfortunately, algorithmic research in medical imaging seldom refers to these guidelines.

Methods should be studied on more than prediction performance: reproducibility 81 , carbon footprint 96 , or a broad evaluation of costs should be put in perspective with the real-world patient outcomes, from a putative clinical use of the algorithms 97 .

Preregistration or registered reports can bring more robustness and trust: the motivation and experimental setup of a paper are to be reviewed before empirical results are available, and thus the paper is be accepted before the experiments are run 98 . Translating this idea to machine learning faces the challenge that new data is seldom acquired in a machine learning study, yet it would bring sizeable benefits 62 , 99 .

More generally, accelerating the progress in science calls for accepting that some published findings are sometimes wrong 100 . Popularizing different types of publications may help, for example publishing negative results 101 , replication studies 102 , commentaries 103 and reflections on the field 68 or the recent NeurIPS Retrospectives workshops. Such initiatives should ideally be led by more established academics, and be welcoming of newcomers 104 .

Conclusions

Despite great promises, the extensive research in medical applications of machine learning seldom achieves a clinical impact. Studying the academic literature and data-science challenges reveals troubling trends: accuracy on diagnostic tasks progresses slower on research cohorts that are closer to real-life settings; methods research is often guided by dataset availability rather than clinical relevance; many developments of model bring improvements smaller than the evaluation errors. We have surveyed challenges of clinical machine-learning research that can explain these difficulties. The challenges start with the choice of datasets, plague model evaluation, and are amplified by publication incentives. Understanding these mechanisms enables us to suggest specific strategies to improve the various steps of the research cycle, promoting publications best practices 105 . None of these strategies are silver-bullet solutions. They rather require changing procedures, norms, and goals. But implementing them will help fulfilling the promises of machine-learning in healthcare: better health outcomes for patients with less burden on the care system.

Data availability

For reproducibility, all data used in our analyses are available on https://github.com/GaelVaroquaux/ml_med_imaging_failures .

Code availability

For reproducibility, all code for our analyses is available on https://github.com/GaelVaroquaux/ml_med_imaging_failures .

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Acknowledgements

We would like to thank Alexandra Elbakyan for help with the literature review. We thank Pierre Dragicevic for providing feedback on early versions of this manuscript, and Pierre Bartet for comments on the preprint. We also thank the reviewers, Jack Wilkinson and Odd Erik Gundersen, for excellent comments which improved our manuscript. GV acknowledges funding from grant ANR-17-CE23-0018, DirtyData.

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Trends and hot topics in radiology, nuclear medicine and medical imaging from 2011–2021: a bibliometric analysis of highly cited papers

• Original Article
• Published: 28 March 2022
• Volume 40 , pages 847–856, ( 2022 )

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• Sheng Yan 1 ,
• Huiting Zhang 2 &
• Jun Wang 3  

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To spotlight the trends and hot topics looming from the highly cited papers in the subject category of Radiology, Nuclear Medicine & Medical Imaging with bibliometric analysis.

Materials and methods

Based on the Essential Science Indicators, this study employed a bibliometric method to examine the highly cited papers in the subject category of Radiology, Nuclear Medicine & Medical Imaging in Web of Science (WoS) Categories, both quantitatively and qualitatively. In total, 1325 highly cited papers were retrieved and assessed spanning from the years of 2011 to 2021. In particular, the bibliometric information of the highly cited papers based on WoS database such as the main publication venues, the most productive countries, and the top cited publications was presented. An Abstract corpus was built to help identify the most frequently explored topics. VoSviewer was used to visualize the co-occurrence networks of author keywords.

The top three active journals are Neuroimage, Radiology and IEEE T Med Imaging . The United States, Germany and England have the most influential publications. The top cited publications unrelated to COVID-19 can be grouped in three categories: recommendations or guidelines, processing software, and analysis methods . The top cited publications on COVID-19 are dominantly in China . The most frequently explored topics based on the Abstract corpus and the author keywords with the great link strengths overlap to a great extent. Specifically, phrases such as magnetic resonance imaging, deep learning, prostate cancer, chest CT, computed tomography, CT images, coronavirus disease, convolutional neural network(s) are among the most frequently mentioned.

The bibliometric analysis of the highly cited papers provided the most updated trends and hot topics which may provide insights and research directions for medical researchers and healthcare practitioners in the future.

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Introduction

Citation distributions are extremely skewed. Most scientific papers are seldom cited, if ever, in the subsequent scientific literature while some papers receive an unusually high citation counts [ 1 ]. In the past decade, there has been a growing interest in using highly cited papers as indicators in research assessments. There may be two reasons for this tendency. First, the increasing focus on scientific excellence in science policy in the context of the enormous quantities of scientific outputs makes it imperative to screen out the most successful or influential work. “Many countries are moving towards research policies that emphasize excellence; consequently; they develop evaluation systems to identify universities, research groups, and researchers that can be said to be “excellent” [ 2 ]. Second, for visibility issues, academic professionals are consistently interested in pursuing high citations for their own work and also tend to follow the research with higher citations. In this way, they can stay current regarding research trends and make informed decisions on potential research topics. High citations imply more visibility, generally accompanied by more supports from public or private funders. Therefore, scientific researchers will be very much proud if their publications are selected as highly cited papers (HCPs).

Incites Essential Science Indicators (ESI), an analytic tool provided by Clarivate Analytics for identifying the top-charting research in Web of Science (WoS)-indexed journals, is widely used to evaluate HCPs, providing information such as the countries/regions [ 3 , 4 ], institutes [ 5 ], and researchers [ 6 ], etc. ESI-HCPs, representing the top 1% in each of the 22 ESI subject fields, vary by fields and by years in a 10 years’ rolling. A paper is selected as a HCP only if its citation count exceeds the 1% citation threshold of the corresponding research fields and publication year.

Over recent years, a number of studies have been conducted on HCPs based on data from ESI [ 7 , 8 , 9 ]. For example, Ioannidis Boyack et al. surveyed the most-cited authors of biomedical research for their views on their own influential published work [ 9 ]. Aksnes found that HCPs are typically authored by a large number of scientists, often involving international collaboration [ 10 ]. Some studies even try to predict the HCPs by mathematical models [ 11 ], implying “the first mover advantage in scientific publication” [ 12 , 13 ]. That is, the first papers in a field will, essentially regardless of content, receive citations at a rate enormously higher than papers published later.

Bibliometrics, a term coined by Pritchard A [ 14 ], is a statistical method used to evaluate scientific development, determine research impacts, compare research performance and identify emerging fronts [ 15 , 16 ]. There have been many bibliometric studies on natural science or social science as a general field [ 17 , 18 ]. There have also been a few subject-specific ones on computer science [ 19 , 20 ], on applied linguistics [ 21 ], and on operations research and management Science [ 22 ]. In this regard, bibliometrics has been applied to summarize the development of a specific subject, generating valuable information such as the most cited publications/journals and the most frequently explored topics, etc. Such information is of great importance and interest to researchers as well as academic institutions and government/private agencies in making funding and science policy decisions. However, to our knowledge, there has not been one bibliometric study on the specific subject “ Radiology, Nuclear Medicine & Medical Imaging ” (RNMI) , a subject that covers resources on radiation research in biology and biophysics. Of the five broad research areas ( Arts & Humanities, Life Sciences & Biomedicine, Physical Sciences, Social Sciences, technology ) in Web of Science database, Life Sciences & Biomedicine has the most number of subject categorizations (76 in total), implying the complexity and richness as well as importance of this research line. As an important subject area in Life Sciences & Biomedicin e in response to the rapidly evolving healthcare industry, the research productivity in this RNMI has been tremendous. A thorough investigation of the existing literature especially the HCPs will help keep researchers informed about the state of the arts and research trends in this subject.

The purpose of this study is to spotlight the trends and hot topics in the subject category of Radiology, Nuclear Medicine & Medical Imaging with the bibliometric analysis of highly cited papers to help researchers get the most updated information in the future study.

A bibliometric approach was used in the present study to map the HCPs in RNMI in WoS. As one of the biggest bibliometric databases, WoS is the most frequently used database in bibliometric studies [ 23 ]. The methods for data retrieval are described as follows.

We searched in WoS Core Collection at the portal of the University library. We filtered the results by clicking the “ Highly Cited in Field ” trophy icon. We then downloaded all the bibliometric data for further analysis including publication years, authors and affiliations, publication titles, countries/regions, organizations, abstracts, citation reports, etc. After the removal of the publications with incomplete bibliometric information, a total of 1325 HCPs were harvested. The yearly publication distributions of the 1325 HCPs were shown in Figure S1 (Online Resource 1). The data retrieval was completed on 15 December, 2021. We collected the impact factor (IF) of each journal from the 2021 Journal Citation Reports (JCR).Table 1 shows the strategies of the retrieval queries.

Three points are to be mentioned here. First, the WoS Core Collection was searched because it boasts as an important bibliometric database which includes literature and citation information indexed in SCIE, SSCI and A&HCI. More importantly, it has been widely used in bibliometric analysis of previous studies both in natural sciences [ 24 , 25 ] and in social sciences [ 21 , 26 ]. Because RNMI belongs to the natural sciences, we restrict the index in SCI-expanded to retrieve the relevant data. Second, only articles and reviews are considered in HCPs selection. There is no need to restrict the document types in our search. Third, the dataset of ESI-HCPs is automatically updated every 2 months to include the most recent 10 years of publications. Therefore, only the papers in the recent decade will be counted as HCPs. There is no need to set the date range.

To identify the most influential papers, we ranked all the HCPs by the Relative Citation Rate (RCR), a new metric that uses citation rates to measure influence at the paper level [ 27 ]. Since the citation count a paper receives is closely associated with the number of years it is published, it is invalid to rank paper impact solely on Raw Citations (RC). Therefore, RCR, recently endorsed by the National Institutes of Health, has been employed here to pinpoint the most highly cited papers. RCR is based on weighting the number of citations a paper receives to a comparison group within the same field [ 28 ]. The icite tool is used here to generate RCR metrics for all the HCPs ( https://icite.od.nih.gov/ ).

Word frequency analysis based on corpus is a bibliometric method to identify hotspots and developmental trend of one domain. In this study, we built an Abstract corpus with all the abstracts of the HCPs. The n -grams (2–4) in the corpus were retrieved and analyzed to detect the most frequently researched topics in the HCPs. The procedures to retrieve the n-grams were described as follows. First, the abstracts of all the 1325 HCPs from the downloaded bibliometric data were saved in separate files in txt. Formats in one folder to create a mini abstract corpus with a total of 299,810 tokens. Second, Anthony’s AntConc, a freeware corpus analysis toolkit for concordancing and text analysis, was used to extract n-grams that include clusters of two to four continuous words [ 29 ]. AntConc is widely used in previous studies [ 16 , 21 , 26 ]. It automatically ranks all the retrieved n-grams in decreasing order. We also generated a list of individual nouns in case of missing some important topics. The reason to exclude the pronouns, modals and many other functional words is that research topics are usually phrases that do not contain these functional words. For topic candidacy, we adopt both frequency (10) and range criteria (10). That is, a candidate n-gram has to appear at least ten times and in at least ten different abstracts for further consideration. The frequency threshold ensures the significance of the candidate topics while the range threshold ensures the topics are not overly clustered in a limited number of papers. In this process, we actually tested the frequency and range thresholds several rounds for the inclusion of all the potential topics. In total, we got 521 nouns, 205 2 g, 39 3 g, and 5 4 g. Third, concerning the list of n-grams and monograms (nouns here), the authors discussed extensively to decide which should be taken as the potential research topics until full agreements were reached.

Besides the word frequency analysis based on the Abstract corpus, we performed knowledge mapping (i.e., network analysis) using VOSviewer ( www.vosviewer.com ), in which we focused on the network and “link strength” between author keywords. Knowledge mapping can be employed to map the scope and structure of the discipline while revealing key research clusters [ 30 ]. Since fractional counting approach assigns co-authored publications fractionally to each author, proper field-normalized results can be obtained [ 31 ]. Therefore, we used fractional counting in our analysis. This process produced the co-occurrence network of the most frequently used author keywords. Knowledge mapping of the author keywords was an important addition to the corpus based investigation of the abstracts.

Main publication venues of HCPs

The top 20 journals with more than 17 HCPs published are listed in Table 2 . They contributed around 80% of the total HCPs (1039/1325). The highest contribution comes from Neuroimage (207) , followed by Radiology (159) . They are also the only 2 journals with more than 100 HCPs, accounting for almost 30% of the total number of the HCPs, overwhelmingly exceeding the others on the list. As the only Q2 journal (between top 50% and top 25%) among the top five (the other four in the Q1, top 25%) by the Journal Citation Reports (JCR) quantile rankings, Neuroimage tops the list with certain surprise.

Because the total number of papers published in each journal varies greatly per year and the HCPs are also connected with journal circulations, we divide the total number of papers (TP) in the examined years (2011–2021) with the number of the HCPs to acquire the HCP percentage for each journal (HCPs/TP). As we can see, the top six journals with the highest percentage of the HCPs are Med Image Anal (2.91), IEEE T Med Imaging (2.83) , Radiology (2.67) , Neuroimage (1.91) , J Cardiovasc Magn Reson (1.91), JACC-Cardiovasc Imag (1.75). That implies that papers published in these journals have a higher probability to enter the HCPs list. In terms of the latest journal impact factor (IF) in 2021, the top five journals with the highest IF are JACC-Cardiovasc Imag (14.805), Radiology (11.105), J Nucl Med (10.057), IEEE T Med Imaging (10.048) and Eur J Nucl Med Mol I (9.236) . The number of the HCPs in these journals take up a large share of the total HCPs (over 30%), implying a close relationship between the journal IF and the number of the HCPs in the journal.

Countries distribution

The top 16 productive countries with more than 50 HCPs are presented in Fig.  1 . The USA took the lead with 707 HCPs (53.358%), confirming its leading position as a traditional scientific powerhouse in this subject, followed by Germany (20.302%) and England (19.623%). It is to be mentioned that only three Asian countries enter the top 16 list ( China, South Korea, Japan ). China even boasts the fourth position with 196 HCPs (14.792%). However, scholars from outside the traditional publishing countries need to be more visible for their work in RNMI.

Top 16 countries/regions with the most HCPs

Most influential papers by RCR

During the data processing, we found that the papers on COVID-19 published in the year of 2020 had extremely high RCR compared to papers on other subjects. As an unexpected global epidemic starting in late 2019, COVID-19 ignited research interests from all over the world especially in China where the epidemic was first reported. Many papers got quickly published and cited during this period in response to the urgent needs to find treatments. If we mix the papers, paying no attention to this public health incident, the COVID-19-related papers will take up 75% of the top 20 highly cited papers in terms of RCR (15/20), which was unfair for other non-COVID-19-related papers because of the distorted impact image. Therefore, we produced two lists of ranking: one for the non-COVID-19 papers in Table 3 and one for the COVID-19 papers in Table 4 . The yearly citation trends of each listed HCP can be seen in Figure S2 (Online Resource 2).

Table 3 shows some interesting patterns. First, 9 out of the top 20 HCPs were published in Neuroimage , which helps corroborate the findings on the main publication venues. Second, in terms of the document types, reviews (11) slightly outnumber articles (9), which may imply that reviews share the same amount of citation opportunities as the articles in the field of medical studies if not more. Third, three types of research orientations can be discerned from the top 20 HCPs: recommendations or guidelines (#1, 6, 11, 16, 18, 19); processing software (#2, 7, 9); analysis methods (#4, 5, 8, 12, 13, 15, 17, etc.).

The top ten highly cited papers on COVID-19 shows a different picture in Table 4 . 9 out of the top ten HCPs were published in Radiology , which once again testifies its popularity and importance in the field of RNMI . Ai tao ’s (2020) Correlation of Chest CT and …tops the list with RCR at 703.55, three times more than Roberto M Lang (2015) with RCR at 203.92, which shows the enormous attention paid to this unprecedented epidemic outbreak.

Most frequently explored topics

Table 5 presents the top 33 research topics above the observed frequency of 38. The observed frequency count for each topic in the abstract corpus is included in the brackets. Topics such as magnetic resonance imaging (325), deep learning (191), prostate cancer (162), chest CT (145), computed tomography (141), CT images (121), PSMA PET (119), coronavirus disease (115), convolutional neural network(s) (108) and FDG PET (100) were the top ten most frequently mentioned topics based on the corpus analysis of the abstracts. We grouped the topics into five broad categories, including devices, organs, artificial intelligence (AI), images, and others, according to topic relationships.

The first group is mainly about the imaging devices in the RNMI field including MRI (396) , CT (484) and PET (279) .

The second group concerns the human organs such as brains (250), prostate (162), heart (160), lungs (153), and breast (93) . Cancer-related phrases (prostate cancer, and breast cancer) were among the top list in frequency. For the brain, topics such as functional connectivity and white matter were more mentioned.

The third group are all related to AI technology ( artificial intelligence, deep learning, machine learning, convolutional neural networks, etc.).

The fourth group is about image information. Image quality is an important focus in MR/CT/PET scanning because it determines whether the images can been used or not. Imaging features can provide more information and are widely used in AI.

Topics in the last group constitute the core concepts in radiology. Radiation therapy is the most important treatment method for cancers. Especially when combined with MRI and CT, precise radiotherapy will be a promising alternative for cancer treatment in the future. As the method for assessing diagnosis performance of quantitative parameters, receiver operating characteristic (ROC) is also the main technology. Contrast agents is the important part of CT and MRI scans. Polymerase chain reaction is the gold standard in the detection COVID-19. It is no wonder that these topics enter the hot topic list because they are closely connected to the topics in other categories.

Author keywords analysis

A total of 2796 keywords were retrieved. We set the minimum number of occurrences of a keyword at 5. Then, 131 keywords meet the threshold. For each of the 131 keywords, the total strength of the co-occurrence links with other keywords were calculated. The top 15 keywords with the greatest total link strength were shown in decreasing order in Table 6 . VOSviewer classified the 131 keywords into 9 clusters, as shown in Fig.  2 . The link strengths for deep learning, covid-19, mri, machine learning, prostate cancer, computed tomography were 79, 74, 42, 40, 39, 39, respectively. The thickness of the lines which was determined by the frequency of the keywords in HCPs shows the link strength between the keywords.

The co-occurrence of author’s keywords

A comparison between the word frequency analysis of the Abstract corpus and the knowledge mapping of the author keywords shows similar research activities, which can be evidenced by the overlapping of the high frequent topics and the author keywords. These hot terms not only reflects the important research trends up to now, but also points the direction for future research in RNMI. For example, AI is gaining increasing popularity in the healthcare industry especially in handling a huge amount of patient data and recognizing complex disease patterns. In the future, AI-based technology is bound to unfold more hidden information from big data and inform healthcare policymakers and clinicians in making effective clinical decisions. Besides, considering the complex functioning of the human brain, the research is multidisciplinary in nature. Therefore, a collaboration across scientific disciplines will better reveal the intricacies of the human brains.

To our knowledge, this is the first comprehensive bibliometric study of Highly Cited Papers (HCPs) in the subject category of RNMI across the years spanning from 2011 to 2021. The results showed that Neuroimage, Radiology, IEEE T Med Imaging, J Nucl Med had the largest number of HCPs published, accounting for about 40% of the total 1325 HCPs. The traditional academic powerhouses in RNMI such as the USA, Germany and England are leading the publications while countries such as China and Italy are catching up. For the top 20 non-COVID-19 HCPs, 3 types of research orientations can be detected: recommendations or guidelines; processing soft wares; analysis methods . Reviews slightly outnumber articles in terms of document types. Among the top ten COVID-19 HCPs published in the year 2020, nine were published in Radiology, and chest CT was the most frequent used term in the paper titles.

It is interesting to find Neuroimage, the only Q2 journal in the top five, tops the list with the most HCPs. Research on human brains is increasing rapidly since the initiation of the WU-Minn Human Connectome Project in America in September 2010, aiming to map macroscopic human brain circuits and their relationship to behavior[ 32 ]. Many countries/regions follow the lead by starting their own brain projects, such as Human Brain Project in European Union, Brain/Minds in Japan, and Brain Science and Brain-Like Intelligence Technology in China. Therefore, topics such as functional connectivity, white matter, brain regions can be found (Table 5 ), reflecting the scientific enthusiasm in human brains. The surging research interest in brain functioning in the last decade across the globe stimulated more papers in related journals such as Neuroimage , especially after the initiation of the WU-Minn Human Connectome Project in September 2010. Besides, from January 2020, Neuroimage is an open access journal. Authors who publish in Neuroimage can make their work visible immediately, which might encourage more authors to contribute their work. It can be evidenced by more publications in Neuroimage in 2020 compared to those in previous years.

United States, Germany and England are undoubtedly the most impactful in the research area of RNMI. Historically, western countries, especially the United States, have been at the center of academic publishing, supported by huge investments in scholarly research and technical infrastructure. Besides, because the research in RNMI usually involves highly priced facilities such as MRI scanner, the developed countries with more resources clearly stand in a more advantageous position in research and publishing. It should be noted here that a HCP is usually the joint writing of multiple authors from different institutions and/or countries[ 10 ]. Web of Science will generate all the bibliometric information of the papers, not restricted to the information about the first author or the corresponding author. In other words, all the countries and institutions listed on the HCPs will be treated evenly. In this way, a clearer picture about the HCPs distribution across countries can be painted.

Scientific research has always been driven by practical needs. It comes with no surprise that Roberto M Lang ’s (2015) Recommendations for cardiac chamber quantification [ 33 ] … tops the list with RCR at 203.92. The quantification of cardiac chamber size and function is the cornerstone of cardiac imaging. Jointly written by the American Society of Echo cardiography and the European Association of Cardiovascular Imaging , Roberto M Lang ’s (2015) is the updated recommendations for cardiac chamber quantification that guide the echo cardiographic practice with sweeping popularity. Because COVID-19 was first reported in China, most of the studies during this period were conducted in hospitals or universities in China, which can be easily seen from the top ten HCPs list. Sana Salehi’s (2020) Coronavirus Disease 2019 (COVID-19)… stands as the only HCP among the top ten beyond China (in USA). From the titles, Chest CT emerges as one of the hottest phrases. The fact that most patients infected with COVID-19 had pneumonia and characteristic CT imaging patterns helps explain its frequent use.

A great overlap between the most frequently explored topics and author keywords is identified. The hot topics can be generally grouped into five broad categories: devices, organs, artificial intelligence (AI), images, and others . MRI is the most frequently mentioned phrase. Compared to CT which only shows signal attenuation and has ionizing radiation, MRI can obtain the multi-contract images without ionizing radiation, and is widely used in whole human bodies except the lung. Especially, in the human brain projects, MRI is the main device. However, CT showed greater values in the lung disease than MRI, which can be evidenced by frequent use of CT in the COVID-19 publications. The use of PET (positron emission tomography) scan along with CT in clinical practice increases side by side with publications in this regard which can be seen in such frequent topics as PET CT, PSMA PET, FDG PET . Moreover, the clinical value of PET with MR is also increasing proven. In the future, PET will be an important device in the field of nuclear medicine and radiology.

Besides brain, lung, prostate, heart, and breast are the most concerned organ. According to the World Health Statistics released in 2020, an estimated 41 million people worldwide died of NCDs (noncommunicable diseases) in 2016, equivalent to 71% of all deaths. Four NCDs caused most of those deaths: cardiovascular diseases (17.9 million deaths), cancer (9.0 million deaths), and chronic respiratory diseases (3.8 million deaths), and diabetes (1.6 million deaths) ( World health statistics 2020: monitoring health for the SDGs, sustainable development goals. Geneva: World Health Organization; 2020. ). Of different cancer types, breast cancer, lung cancer, and prostate cancer were the top three most prevalent cancers, according to the latest GLOBOCAN2020 report by the International Agency of Research on Cancer, part of World Health Organization.

In recent years, AI has been a hot theme of modern technology and is creeping into almost every facet of modern life including medical research. Up to now, AI has been actively used in medical images recognition, medical intelligent decision-making, medical intelligent voice, and “Internet plus” medical treatment. As one of the first specialty in healthcare to adopt digital technology, radiology is well positioned to deploy AI for diagnostics due to digital images [ 34 ]. Gulshan first reported that AI could automated detected diabetic retinopathy and diabetic macular edema from over 100 thousand retinal fundus photographs, with high sensitivity and specificity [ 35 ]. In 2017, Golden reported that AI can quickly read photos to diagnose breast cancer with lymph mode metastases, greatly improving the speed of diagnosis [ 36 ]. AI also played an important role in detecting COVID-19 [ 37 , 38 , 39 ]. In the future, AI is bound to exert greater influence on the medical field. For example, AI shows great promise in changing treatment models, promoting medicine development, reshaping the medical industry, and even impacting the career paths of the medical practitioners. It is believed that artificial intelligence will bring profound changes to future medical technology and will be a powerful driving force for future medical innovation and reform.

There are several points to be mentioned here as for the most frequently explored topics. Decisions regarding the candidate topics were not easy and involved subjectivity. It was the results of several rounds of discussions from multiple professionals. Some n-grams are discarded because they are too general or not meaningful topics in RNMI. For example, quantitative analysis, high sensitivity, imaging technique and medical image are too general to be included. By meaningful topics, we mean the n-grams can help journal editors and readers to quickly locate their interested fields, as the author keywords such as brain networks, MRI imaging, CT scans. Besides, the examination of the limited 3/4-g and monograms (nouns) revealed that most of them were either not meaningful topics such as cancer detection rate and patients with prostate cancer or they were topics already identified in the 2 g such as weighted MR imaging in MR imaging. Therefore, the final list is mostly 2-g topics.

It should be noted that large numbers of quantitative data have been used here to map the HCPs from different perspectives. Despite the quantitative nature, our study also involves qualitative analysis and hence subjectivity, especially concerning what constitutes the research topics and topic categorization. Given the rapid developments in RNMI, more bibliometric research is needed in the future to help test and enhance the validity and reliability of this research approach and to help keep us accurately informed about the trends in RNMI.

Our study also has some limitations. The subject category of Radiology, Nuclear Medicine & Medical Imaging listed in WoS Categories needs to be further broken down into subcategories and subjects in future analysis. A finer granular subject classification of the research area would have painted a more detailed picture. In additional, the study focuses on the apex of the publishing pyramid in RNMI, the HCPs. And the bibliometric indexes here are all based on the WoS SCI international journals. Although these are the most celebrated and accessible works, some other publications of similar importance or highly localized publications which do not have the chance to enter the list and are not indexed in WoS are not given due attention in our study. This less widely cited research is a rich vein for future study. At last, the study seems to show that the number of citations a review paper receives is higher than that of an original article in RNMI. Therefore, it might be more useful to distinguish the two types of papers in future method design.

In conclusion, our results of the bibliometric analysis provided the updated trends and hot topics in RNMI. And the practitioners and researchers in RNMI can be better aided to locate the relevant literature and keep informed about the hot topics.

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Acknowledgement

This study was funded by the grant from Humanities and Social Sciences Youth Fund of China, Ministry of Education (MOE) (Grant Number 20YJC740076)

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Supplementary file1 (TIF 720 KB) Fig. S1. The yearly publication distribution of the examined 1325 HCPs.

11604_2022_1268_moesm2_esm.tif.

Supplementary file2 (TIF 7093 KB) Fig. S2. The yearly citation distribution of the top 20 HCPs (non-COVID-19) and the top 10 HCPs (COVID-19)

About this article

Yan, S., Zhang, H. & Wang, J. Trends and hot topics in radiology, nuclear medicine and medical imaging from 2011–2021: a bibliometric analysis of highly cited papers. Jpn J Radiol 40 , 847–856 (2022). https://doi.org/10.1007/s11604-022-01268-z

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Accepted : 08 March 2022

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Issue Date : August 2022

DOI : https://doi.org/10.1007/s11604-022-01268-z

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Study finds e-cigarette users now more likely to quit traditional cigarettes

by Oxford University Press

A new paper in Nicotine & Tobacco Research finds that smokers who switch to electronic cigarettes are now more likely to stop smoking regular cigarettes. In the past, smokers who began using electronic cigarettes mostly continued smoking. The paper is titled, "Divergence in cigarette discontinuation rates by use of electronic nicotine delivery systems (ENDS): Longitudinal findings from the U.S. PATH Study Waves 1-6."

Electronic nicotine delivery systems first emerged on the U.S. market in 2007. The first e-cigarettes resembled conventional cigarettes (in appearance) and used fixed low-voltage batteries. Beginning in 2016, manufacturers introduced e-liquids containing nicotine salt formulations. These new e-cigarettes became widely available. These nicotine salts are lower in pH than freebase formulations, which allows manufacturers to increase nicotine concentration while avoiding harshness and bitterness.

Past population-level research provided conflicting findings on whether vaping helps people who smoke combustible cigarettes to quit smoking. Some research suggests improved cigarette quitting-related outcomes with e-cigarette use , while other research suggests the opposite.

Inconsistent findings may be due to differences in the samples and measures considered, differences in the analytic approaches of researchers used, the rapidly changing product environment, or policy contexts.

The researchers here examined differences in real-world trends in population-level cigarette discontinuation rates from 2013 to 2021, comparing U.S. adults who smoked combustible cigarettes and used e-cigarettes with U.S. adults who smoked combustible cigarettes and did not use e-cigarettes.

Using data from among adults (ages 21+) in the Population Assessment of Tobacco and Health (PATH) Study, a national longitudinal study of tobacco use from people from all over the United States, the researchers found that between 2013 and 2016, rates of discontinuing cigarette smoking among adults in the U.S. population were statistically indistinguishable between those who used e-cigarettes and those who did not. Cigarette discontinuation rates were 15.5% for those who used e-cigarettes and 15.6% for those who did not.

But the quit rates changed in subsequent years; the researchers here found that between 2018 and 2021 only 20% of smokers who did not use e-cigarettes stopped smoking combustible cigarettes, but some 30.9% of smokers who used e-cigarettes stopped smoking combustible cigarettes.

The paper notes that the full study period spanned a time in the United States when the e-cigarette marketplace was expanding; salt-based nicotine formulations gained market share in 2016 and vaping products became available with increased nicotine yields over time.

This was also a period in which state and federal governments restricted tobacco in various ways, including increasing the tobacco-purchase age to 21 and restricting flavored e-cigarettes.

"Our findings here suggest that the times have changed when it comes to vaping and smoking cessation for adults in the US," notes study first author, Karin Kasza, an assistant professor of oncology in the Department of Health Behavior at Roswell Park Comprehensive Cancer Center in Buffalo, NY.

"While our study doesn't give the answers as to why vaping is associated with cigarette quitting in the population today when it wasn't associated with quitting years ago, design changes leading to e-cigarettes that deliver nicotine more effectively should be investigated. This work underscores the importance of using the most recent data to inform public health decisions."

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This paper is in the following e-collection/theme issue:

Published on 3.4.2024 in Vol 26 (2024)

Public Discourse, User Reactions, and Conspiracy Theories on the X Platform About HIV Vaccines: Data Mining and Content Analysis

Authors of this article:

Original Paper

• Jueman M Zhang 1 , PhD   ; 
• Yi Wang 2 , PhD   ; 
• Magali Mouton 3   ; 
• Jixuan Zhang 4   ; 
• Molu Shi 5 , PhD  

1 Harrington School of Communication and Media, University of Rhode Island, Kingston, RI, United States

2 Department of Communication, University of Louisville, Louisville, KY, United States

3 School of Rehabilitation Sciences, University of Ottawa, Ottawa, ON, Canada

4 Polk School of Communications, Long Island University, Brooklyn, NY, United States

5 College of Business, University of Louisville, Louisville, KY, United States

Corresponding Author:

Jueman M Zhang, PhD

Harrington School of Communication and Media

University of Rhode Island

10 Ranger Road

Kingston, RI, 02881

United States

Phone: 1 401 874 2110

Email: [email protected]

Background: The initiation of clinical trials for messenger RNA (mRNA) HIV vaccines in early 2022 revived public discussion on HIV vaccines after 3 decades of unsuccessful research. These trials followed the success of mRNA technology in COVID-19 vaccines but unfolded amid intense vaccine debates during the COVID-19 pandemic. It is crucial to gain insights into public discourse and reactions about potential new vaccines, and social media platforms such as X (formerly known as Twitter) provide important channels.

Objective: Drawing from infodemiology and infoveillance research, this study investigated the patterns of public discourse and message-level drivers of user reactions on X regarding HIV vaccines by analyzing posts using machine learning algorithms. We examined how users used different post types to contribute to topics and valence and how these topics and valence influenced like and repost counts. In addition, the study identified salient aspects of HIV vaccines related to COVID-19 and prominent anti–HIV vaccine conspiracy theories through manual coding.

Methods: We collected 36,424 English-language original posts about HIV vaccines on the X platform from January 1, 2022, to December 31, 2022. We used topic modeling and sentiment analysis to uncover latent topics and valence, which were subsequently analyzed across post types in cross-tabulation analyses and integrated into linear regression models to predict user reactions, specifically likes and reposts. Furthermore, we manually coded the 1000 most engaged posts about HIV and COVID-19 to uncover salient aspects of HIV vaccines related to COVID-19 and the 1000 most engaged negative posts to identify prominent anti–HIV vaccine conspiracy theories.

Results: Topic modeling revealed 3 topics: HIV and COVID-19, mRNA HIV vaccine trials, and HIV vaccine and immunity. HIV and COVID-19 underscored the connections between HIV vaccines and COVID-19 vaccines, as evidenced by subtopics about their reciprocal impact on development and various comparisons. The overall valence of the posts was marginally positive. Compared to self-composed posts initiating new conversations, there was a higher proportion of HIV and COVID-19–related and negative posts among quote posts and replies, which contribute to existing conversations. The topic of mRNA HIV vaccine trials, most evident in self-composed posts, increased repost counts. Positive valence increased like and repost counts. Prominent anti–HIV vaccine conspiracy theories often falsely linked HIV vaccines to concurrent COVID-19 and other HIV-related events.

Conclusions: The results highlight COVID-19 as a significant context for public discourse and reactions regarding HIV vaccines from both positive and negative perspectives. The success of mRNA COVID-19 vaccines shed a positive light on HIV vaccines. However, COVID-19 also situated HIV vaccines in a negative context, as observed in some anti–HIV vaccine conspiracy theories misleadingly connecting HIV vaccines with COVID-19. These findings have implications for public health communication strategies concerning HIV vaccines.

Introduction

Vaccination has long been recognized as a crucial preventive measure against diseases and infections, but opposition to vaccines has endured [ 1 ]. HIV vaccination has been regarded as a potential preventive measure to help combat the HIV epidemic in the United States, with research and progress dating back to the mid-1980s but without success thus far [ 2 ]. An estimated 1.2 million people were living with HIV in the United States by the end of 2021, with 36,136 new HIV diagnoses reported in 2021 [ 3 ].

On January 27, 2022, the biotechnology company Moderna announced the initiation of clinical trials for an HIV vaccine using messenger RNA (mRNA) technology [ 4 ]. In March 2022, the National Institutes of Health announced the start of clinical trials for 3 mRNA HIV vaccines [ 5 ]. The mRNA technology had previously been used in the Pfizer-BioNTech and Moderna COVID-19 vaccines, which protected individuals against severe symptoms and fatalities during the pandemic [ 6 ]. Following the successes of mRNA COVID-19 vaccines, which led to the Nobel Prize in Physiology or Medicine being awarded to 2 scientists in October 2023 [ 7 ], researchers have been investigating the potential of mRNA vaccines for various other diseases, including HIV [ 8 , 9 ]. The announcements of clinical trials for mRNA HIV vaccines revived public discussion on the prospect of vaccines to combat HIV [ 9 ] despite >3 decades of unsuccessful research [ 2 ]. Meanwhile, these announcements were made against the backdrop of intense vaccine debates during the COVID-19 pandemic, with misinformation and conspiracy theories fueling vaccine hesitancy [ 10 - 12 ].

The X platform, formerly known as Twitter, has been a significant social media platform and a vital source for text-based public discourse. Posts on X have been studied to understand public discourse about vaccines in general [ 13 - 15 ] and about specific vaccines, such as COVID-19 vaccines in recent years [ 12 , 16 , 17 ]. However, there is a dearth of research about public discourse on HIV vaccines on social media. Given the advancement in mRNA technology in COVID-19 vaccines and heated vaccine debates, it has become especially important to gain insights into public discourse and reactions regarding potential new vaccines.

This study is grounded in the growing field of infodemiology and infoveillance, which investigates the “distribution and determinants of information in an electronic medium,” specifically on the web, by analyzing unstructured text with the aim of informing public health practices or serving surveillance objectives [ 18 ]. In recent infodemiology and infoveillance studies, machine learning algorithms have been increasingly used to examine substantial amounts of social media content, such as posts on X related to COVID-19 vaccines [ 12 , 16 , 17 ] and HIV prevention [ 19 ], to extract insights into public discourse and reactions. These algorithms automatically analyze extensive volumes of posts and capture latent textual information such as topics and sentiments. This study aimed to investigate how users used different post types to contribute original content to topics and valence identified through machine learning algorithms and how these topics and valence affected user reactions on X regarding HIV vaccines. In addition, by manually coding the most engaged posts, similar to an approach used in previous infodemiology and infoveillance research [ 20 ], the study intended to identify salient aspects of HIV vaccines related to COVID-19 as well as prominent anti–HIV vaccine conspiracy theories. Analyzing posts on X about HIV vaccines can shed light on public discourse and information diffusion. These findings have implications for shaping public health communication strategies about HIV vaccines [ 18 ]. Furthermore, the findings may help in understanding the acceptability of the HIV vaccine upon its successful development in comparison with adherence to existing HIV prevention measures. Previous infodemiology and infoveillance research effectively increased the forecast accuracy of COVID-19 vaccine uptake by leveraging insights derived from posts on X [ 21 ].

Literature Review

Public discourse about hiv prevention on x.

Social media platforms have become important channels for HIV communication, enabling the dissemination of and engagement with content encompassing a wide array of issues related to HIV prevention, treatment, coping, and available resources [ 22 , 23 ]. An earlier infodemiology study examined 69,197 posts on the X platform containing the hashtag #HIVPrevention between 2014 and 2019 and categorized these posts into 10 identified topics concerning HIV prevention [ 19 ]. Among them, pre-exposure prophylaxis had the highest representation with 13,895 posts, followed by HIV testing; condoms; harm reduction; gender equity and violence against women; voluntary medical male circumcision; sex work; postexposure prophylaxis; elimination of mother-to-child transmission of HIV; and abstinence, which had the lowest representation with 180 posts. Furthermore, that study suggested a consistency between the volume of posts related to specific HIV prevention measures on X over time and the temporal trends in the uptake of those measures [ 19 ]. It is noteworthy that the topic of HIV vaccines was absent, which suggested minimal public discourse on the topic during these years. This may be associated with the extensive history of unsuccessful research in this area [ 2 ].

Despite the availability of current HIV prevention measures, efforts have been made to develop HIV vaccines, which are considered necessary to bridge the gap between the challenges in adhering to existing HIV prevention measures and the ambitious goal set by United Nations member states to end the HIV epidemic by 2030 [ 24 , 25 ]. The surge in public discussion about HIV vaccines, possibly elicited by the clinical trials for mRNA HIV vaccines [ 9 ], presented an optimal opportunity to investigate public discourse and reactions regarding HIV vaccines. To the best of our knowledge, this is the first study to analyze posts on X about HIV vaccines.

Public Discourse and Post Types on X

On the X platform, public discourse featuring original content can be observed through 3 post types: self-composed posts, quote posts, and replies [ 26 ]. X users can compose a post. They can also create a quote post, which entails reposting a post while adding their comments. In addition, they can reply to a post to share their comments [ 26 ]. While self-composed posts initiate new conversations, quote posts and replies enable users to join existing conversations by contributing their own comments [ 27 ]. The Pew Research Center’s analysis of survey respondents’ posts on X from October 2022 to April 2023 revealed the composition of different types of posts. Regarding the 3 types of posts containing original content, replies accounted for the highest proportion at 40%, followed by self-composed posts at 15% and quote posts at 9%. The remaining 35% were reposts [ 28 ].

Machine learning algorithms have been increasingly used in recent years to identify latent message features, including textual topics and sentiment valence, among vast numbers of social media posts, as exemplified by previous research analyzing posts on X about COVID-19 vaccines [ 12 , 16 , 17 ] and HIV prevention [ 19 ]. However, the patterns of public discourse in social media conversations are unclear. Specifically, there is a scarcity of research on how people contribute their original content to topics and valence related to a public health issue. This study aimed to address this gap by examining the relationship between post types and message features, specifically topics and valence uncovered using machine learning algorithms, with a focus on HIV vaccines as the subject matter. The findings will advance our knowledge of user contributions to social media conversations about HIV vaccines.

Message Features Influencing User Reactions on X

Examining message diffusion on social media has been a multifaceted challenge, especially with vaccines being a contentious issue debated fervently during the COVID-19 pandemic [ 16 ]. Another contribution of this study is to advance this research area by using machine learning to investigate the synergistic impact of content and account features on user reactions regarding a potential new vaccine amid the context of intense vaccine debates.

The extent to which a message results in optimal diffusion on social media can be gauged by user reactions [ 16 , 29 - 31 ]. On X, a user can engage with posts—be it a self-composed post, quote post, or reply—in 2 primary 1-click reactions: liking and reposting [ 26 ]. An X user can like a post to show appreciation for it or repost it to share it publicly. Compared to liking, reposting is a more social behavior [ 16 , 32 ]. Unlike X’s old timeline, which mostly displayed posts from accounts that a user followed, its current “For you” timeline also shows posts that those accounts have engaged with along with other posts recommended based on user reactions [ 33 ]. The nature of promoting posts based on user reactions makes it more important to investigate the factors that influence user reactions.

This study investigated 2 categories of message-level features that, according to previous research, can drive user interactions: content features in terms of topics and valence and account features in terms of user verification and follower count. Post topics affect likes and reposts on X [ 16 , 30 , 34 ]. Previous research on COVID-19 vaccine posts on X has indicated that posts containing useful information garner more likes and reposts [ 16 ]. This is likely because information utility fills people’s knowledge gaps and serves their utilitarian needs in the face of health risks [ 16 , 32 , 34 - 36 ]. In addition, previous studies have suggested that the novelty of useful information further facilitates sharing of digital health information [ 32 , 36 ], such as updates about COVID-19 vaccine development [ 12 ]. Given the initial success of mRNA technology in COVID-19 vaccines, mRNA HIV vaccine candidates may possess the inherent features of prospective usefulness and ongoing novelty. As a result, posts presenting pertinent information have the potential to generate more likes and reposts. Meanwhile, the announcements of clinical trials for mRNA HIV vaccines were made amidst intense vaccine debates during the COVID-19 pandemic [ 12 ]. Previous research has shown that perceived controversiality in health information increases viewership but not sharing on social media [ 32 ]. In the context of the heated controversy surrounding vaccines, it is crucial to understand user reactions to new potential vaccines.

In addition to post topics, post valence can play a role in user reactions [ 34 ]. Past research has generally revealed that there are more positive than negative posts on X about vaccines in general [ 13 - 15 ] and, more recently, about COVID-19 vaccines in particular [ 12 , 16 , 17 ]. However, the influence of post valence on user reactions remains unclear. One study on COVID-19 vaccines showed that positive posts on X received more likes but not more reposts [ 16 ]. Another study on vaccines regardless of their type revealed that antivaccine posts garnered more reposts than provaccine posts on X [ 13 ]. A psychological rationale supporting the social transmission of positive content is the motivation of individuals to present themselves positively and shape their self-identity [ 35 , 37 ]. In comparison, social transmission of negative content can be attributed to the idea that certain negative content triggers activation, which drives user reactions [ 35 ].

Furthermore, previous research has shown that account features such as verification status and follower count affect user reactions on social media [ 13 , 16 , 34 ]. Given the vast amounts of information available in the digital age, the authenticity of user accounts becomes crucial in the diffusion of health information. One study revealed that account verification enhanced the number of likes and reposts for posts about COVID-19 vaccines on X [ 16 ]. Another study indicated that follower counts increased the number of reposts for posts about vaccines on X regardless of vaccine type [ 13 ].

Conspiracy Theories

A conspiracy theory refers to the belief that a coalition of powerholders forms secret agreements with malevolent intentions [ 38 , 39 ]. It differs from other types of misinformation by hypothesizing a pattern in which people, objects, or events are interconnected in a causal manner [ 39 ]. Previous research has revealed conspiracy theories as a salient theme in antivaccine discourse on social media, along with other themes such as side effects and inefficacy [ 40 , 41 ]. For HIV vaccines, conspiracy theories are crucial in understanding public discourse against them given the limited information about side effects and inefficacy until future success. An additional contribution of this study is the identification of prominent anti–HIV vaccine conspiracy theories through manual coding of the most engaged with negative posts.

Antivaccine conspiracy theories contribute to vaccine hesitancy [ 42 - 44 ], as observed recently with COVID-19 vaccines [ 10 , 11 ]. Understanding the themes and reasoning behind antivaccine conspiracy theories will provide vital implications for deploying evidence-based and logic-driven strategies to counter them [ 45 - 47 ]. A systematic review of antivaccine discourse on social media from 2015 to 2019 revealed pre–COVID-19 conspiracy theories [ 41 ]. These theories claimed that powerholders promoted vaccines for self-serving interests, including hiding vaccine side effects for financial gain and controlling society and the population [ 40 , 41 ]. During the COVID-19 pandemic, antivaccine conspiracy theories thrived on social media. Some theories claimed that the pandemic was invented for pharmaceutical companies’ profit from vaccines [ 44 ], whereas others linked mRNA COVID-19 vaccines to infertility and population control [ 10 , 11 , 44 , 48 , 49 ]. Another conspiracy theory claimed that Bill Gates and the US government aimed to implant trackable microchips into people through mass vaccination [ 11 , 27 , 49 ]. This aligns with conspiracy theories from earlier years. In particular, the Big Pharma conspiracy theory claims that pharmaceutical companies, together with politicians and other powerholders, conspire against the public interest [ 50 ]. The New World Order conspiracy theory alleges that a power elite with a globalization agenda colludes to rule the world [ 51 ]. Conspiracy theories have also linked other vaccines, such as poliovirus vaccines in the past [ 52 , 53 ] and COVID-19 vaccines in recent years, to HIV infection [ 54 , 55 ]. These conspiracy theories were based on the claims that alleged vaccines contained HIV.

Research Questions

To understand public discourse and reactions surrounding HIV vaccines on the X platform, we put forward the following research questions (RQs):

• What are the topics of the posts about HIV vaccines? (RQ 1)
• What is the valence of the posts about HIV vaccines? (RQ 2)
• How do topics and valence vary across different types of posts? (RQ 3)
• How do content features (topics and valence) and account features (verification status and follower count) affect 1-click reactions in terms of likes and reposts, respectively? (RQ 4)
• What are the prominent anti–HIV vaccine conspiracy theories that receive the most reactions? (RQ 5)

Data Source

We collected English-language original posts about HIV vaccines on the X platform from January 1, 2022, to December 31, 2022, using Netlytic [ 56 ]. The selected time frame began in January 2022 with the initiation of mRNA HIV vaccine clinical trials fueling public discussion and concluded in December 2022, a significant month for HIV and AIDS awareness marked by World AIDS Day on the first day of the month. Posts, excluding reposts, that contained both keywords (case insensitive)—“HIV” and “vaccine”—were extracted, resulting in a total of 36,424 posts across 365 days. Posts were collected weekly. Posts published from the last ending time point to at least 24 hours before each collection time point were included in the data set, allowing for a substantial reaction time.

The unit of analysis was a post. For each post, automated extraction produced data for user reactions (the number of likes and reposts) as well as account features (account verification status and follower count). All 36,424 posts underwent topic modeling using latent Dirichlet allocation (LDA) to identify latent topics, as well as sentiment analysis using Valence Aware Dictionary and Sentiment Reasoner (VADER) to access valence. LDA generated topic-specific loadings and identified the dominant topic for each post. VADER generated a valence compound score for each post, which was also categorized as positive, neutral, or negative based on standard VADER classification values.

LDA revealed 3 topics. As the topic of HIV and COVID-19 dominated in a large proportion of posts, we manually coded the 1000 most engaged posts containing the words “HIV” and “COVID” to uncover the salient aspects of HIV vaccines related to COVID-19. To develop coding for subtopics, 2 researchers initially reviewed and coded the top 200 posts with the most reactions. Subtopics were categorized by adapting existing categories from the literature [ 16 , 34 ] and integrating newly identified subtopics from the posts. The Scott π was 0.80 for categorizing subtopics. Subsequently, each researcher independently coded half of the remaining 800 posts.

We then conducted cross-tabulation analyses among all posts to examine the distribution of topics and valence among different types of posts. Furthermore, we conducted linear regression analyses among all posts to assess the influence of content and account features on these 1-click reactions. Of all 36,424 posts, 19,284 (52.94%) received ≥1 like, and 9155 (25.13%) received ≥1 repost. We added a constant value of 1 to all data points for likes and reposts before applying the natural logarithm. This was done to include posts with 0 likes or reposts and to mitigate the skewness of the data distribution.

Of the 28,439 posts that received likes or reposts, 6176 (21.72%) were negative. We manually coded the top 1000 negative posts with the most reactions to uncover prominent anti–HIV vaccine conspiracy theories. To develop coding for conspiracy theories, 2 researchers initially reviewed and coded the top 200 negative posts that received the most reactions. Posts containing conspiracy theories were identified based on expressions of postulated causal connections between people, objects, or events with malevolent intent [ 38 , 39 ]. Conspiracy theories were then classified based on the existing ones from the literature [ 50 , 51 ] and the emerging ones observed in the posts. Coding discrepancies were resolved through a further review of questionable posts and refinement of the conspiracy theories following the approach used in previous social media content analyses [ 40 , 57 ]. The procedure identified conspiracy theories and established intercoder reliability. The Scott π was 0.83 for identifying conspiracy theories and 0.81 for categorizing them. Each researcher then independently coded half of the remaining 800 negative posts.

User Reactions

One-click reactions were measured by the number of likes and reposts, which were automatically extracted. Because a small number of posts garnered significant 1-click reactions, the distribution of likes and reposts was right skewed. To reduce right skewness, we used the natural logarithm of the number of likes and reposts in linear regression analyses, as done in previous research [ 16 , 30 , 34 ].

Post Topics

All posts underwent topic modeling using LDA [ 58 ]. Topic modeling is a commonly used unsupervised learning method that generates a probabilistic model for a corpus of text data [ 59 ]. As a widely used topic model [ 59 ], LDA has been applied to discover topics within rich sources of digital health information, such as electronic health records [ 60 ], reviews on the web [ 61 ], and posts on X [ 16 , 34 ].

LDA relies on 2 matrices to define the underlying topical structure: the word-topic matrix and the document-topic matrix [ 62 ]. In this study, a post was considered a document. The general idea is that a post is represented by a Dirichlet distribution of latent topics, with each latent topic being represented by a Dirichlet distribution of words [ 59 ]. In the word-topic matrix, where the rows represent words and the columns represent topics, each element reveals the conditional probability of a word appearing within a topic [ 62 ]. A topic can be interpreted by examining a list of the most probable words ranked by their frequencies within a given topic using 3 to 30 words [ 63 ]. In the document-topic matrix, where rows represent posts and columns represent topics, each element reveals the conditional probability of a topic underlying a post [ 62 ]. In other words, it reveals the topic-specific loadings for each post.

When interpreting each topic, we reviewed the word-topic matrix as well as sample posts with high topic-specific loadings and significant reactions. LDA generated topic-specific loadings for each post ranging from 0 to 1, with values closer to 1 indicating a higher probability of a topic being associated with a post. Furthermore, LDA determined the dominant topic for each post by selecting the topic with the highest topic-specific loading among all topics. In the cross-tabulation analysis examining the distribution of topics across post types, the dominant topic for each post was entered for analysis. In the linear regression models assessing message-level drivers of user reactions, topic-specific loadings for each post were entered as topic values following previous research [ 16 , 34 ].

Post Valence

We used VADER to analyze the sentiment valence of each post. VADER is a rule-based model specifically attuned for assessing sentiments expressed in social media text [ 64 ]. VADER generated a compound valence score for each post ranging from –1 to 1, with a value of –1 indicating the most negative sentiment and a value of 1 indicating the most positive sentiment [ 65 ]. The standard VADER compound value thresholds for classifying valence categories are as follows: 0.05 to 1 for positive, −0.05 to 0.05 for neutral, and −0.05 to −1 for negative [ 65 ]. In the cross-tabulation analysis examining the distribution of valence among post types, the valence category for each post was entered for analysis. In the linear regression models assessing message-level drivers of user reactions, the VADER compound valence score for each post was used.

This study collected original posts excluding reposts. For each original post, it was automatically extracted whether it was a self-composed post, a quote post with comments, or a reply.

In total, 2 researchers manually coded the top 1000 out of 6176 negative posts with the highest total number of likes and reposts to uncover highly engaged conspiracy theories. They distinguished conspiracy theories from other types of negative information, particularly other types of misinformation, by recognizing the presence of a hypothesized pattern of causal connections between people, objects, or events for malicious intent [ 38 , 39 ]. Conspiracy theories were then categorized based on the existing ones from the literature and the emerging ones observed in the posts.

As an example, consider a post paraphrased as follows:

Image using condoms consistently, only to contract HIV from a COVID vaccine.

It was posted on February 9, 2022, and received 783 likes and 296 reposts. This post was not coded as displaying a conspiracy theory as it only presented misinformation suggesting that COVID-19 vaccines caused HIV. In comparison, another post was paraphrased as follows:

The COVID vaccine contained a spike protein derived from HIV. I was banned from saying this and ridiculed for months. Also, pharmacies stock up HIV self-tests.

It was posted on February 8, 2022, with 147 likes and 48 reposts. This post was coded as displaying a conspiracy theory. It was further classified within the category of conspiracy theories linked to COVID-19 vaccines containing, causing, or increasing HIV. This post suggested a hypothesized pattern of maliciously intended causal connections between the claim that the COVID-19 vaccine contained HIV and the stocking of HIV self-tests in pharmacies. As another example, a post was paraphrased as follows:

Scientists uncover a “highly virulent” strain of HIV in the Netherlands.

It was posted on February 12, 2022, and received 11 likes and 11 reposts. This post conveyed negative information but did not present a conspiracy theory. In comparison, another post was paraphrased as follows:

By coincidence again, the development of a new mRNA HIV vaccine began just before the emergence of the new HIV strain.

It was posted on February 8, 2022, and received 102 likes and 4 reposts. This post was coded as presenting a conspiracy theory and further classified into the category of conspiracy theories linked to the identification of a new highly virulent HIV strain. This post emphasized the speculative timing of the discovery of the new highly virulent HIV strain occurring shortly after the announcement of the development of a new mRNA HIV vaccine.

Account Features

For each post, the posting account’s verification status and follower count were automatically extracted.

Data Analysis

We used cross-tabulation analyses to investigate the distribution of topics and valence across different post types, in which the dominant topic and valence category for each post were entered, respectively, alongside the post type. We used linear regression models to examine the message-level drivers of user reactions among posts that received likes or reposts. In the linear regression models, a constant value of 1 was added to all data points of like and repost counts. The natural log-transformed values for each post were then regressed on 3 topic-specific loadings generated from LDA, the valence compound score generated from VADER, and 2 autoextracted account features—account verification status and follower count. The “plus one” technique was used to include posts that received 0 likes or reposts and to address the skewness of the data distribution.

Ethical Considerations

Following Long Island University’s institutional review board determination process, an institutional review board review was deemed unnecessary for this study, which collected and analyzed publicly available social media data. All referenced posts were paraphrased to avoid association with any particular user on the X platform.

RQ 1 asked about the topics present in all the posts. We trained a topic model using LDA exploring topic numbers ranging from 2 to 20. The optimal number of topics ( k ) was selected considering both the coherence score ( C v ) and the topic model visualization in a Python library called pyLDAvis , as done in previous research [ 16 , 66 ]. C v is a metric that reflects the semantic coherence of topics by evaluating the word co-occurrence likelihood within topics [ 67 ]. A higher C v indicates a better classification achieved by the topic model. In this study, the model with 2 topics ( k =2) yielded the highest C v (0.42), whereas the model with 3 topics ( k =3) yielded the second highest C v (0.35). The pyLDAvis chart depicts each topic as a circle. Overlapping areas between circles suggest similarities in topics. Thus, a chart without overlapping circles is preferable for k . The pyLDAvis chart for this study showed that, when the value of k was 2 or 3, the circles did not overlap. However, when k reached 4, the circles began to overlap, and overlapping circles persisted for values of k ranging from 4 to 20. Between the k values of 2 and 3, we opted for a model comprising 3 topics ( k =3) considering that a smaller number of topics tends to result in overly broad meanings for each topic [ 68 ].

Table 1 summarizes the 3 topics and lists their representative posts. Each topic was interpreted by examining the top 10 probable words ranked by frequency, along with sample posts exhibiting high topic-specific loadings and 1-click reactions. Topic 1 was HIV and COVID-19, covering 78% of the tokens [ 69 ] and dominating in 92.46% (33,678/36,424) of the posts. Topic 2 was mRNA HIV vaccine trials, covering 14% of the tokens and dominating in 5.91% (2151/36,424) of the posts. Topic 3 was HIV vaccine and immunity, covering 8% of the tokens and dominating in 1.63% (595/36,424) of the posts.

Figure 1 illustrates the daily numbers of original posts about HIV vaccines throughout 2022, in total and categorized into 3 topics. Moderna’s announcement of clinical trials for its first mRNA HIV vaccine on January 27, 2022, likely triggered the initial surge, culminating in a daily peak when the number of posts reached 805 on January 29, 2022. The daily number of posts about mRNA HIV vaccine trials (topic 2) in the week following Moderna’s announcement was higher than on other days throughout the year. Nevertheless, even during that week, there were higher daily numbers of posts about HIV and COVID-19 (topic 1), which remained dominant among the 3 topics during the entire year. The year’s second and highest daily peak occurred on February 8, 2022, recording a total of 1603 posts, most of which focused on HIV and COVID-19 (topic 1). This could be attributed to the emergence of new HIV-related events in early February 2022, including the promotion of HIV tests by public figures [ 64 ] and the discovery of a new highly virulent HIV strain [ 65 ]. The third highest daily peak, comprising 1085 posts, occurred on May 18, 2022, which has marked HIV Vaccine Awareness Day since 1998. Most of the posts centered on HIV and COVID-19 (topic 1). The remainder of the year did not reach such high peaks, with the largest daily volume of 205 posts occurring on December 2, 2022, the day following World AIDS Day, observed since 1988. Similar to previous daily peaks, most of the posts revolved around HIV and COVID-19 (topic 1).

The results revealed the dominance of HIV and COVID-19 (topic 1) in 92.46% (33,678/36,424) of the posts, with HIV as the most frequent word and COVID as the fourth most frequent word. To gain a deeper understanding of salient aspects of HIV vaccines related to COVID-19, we manually coded the top 1000 posts with the highest total number of likes and reposts that contained both HIV and COVID . Table 2 summarizes the subtopics and their representative posts with like and repost counts.

The first major subtopic, comprising 24% (240/1000) of the posts, focused on the reciprocal influence of HIV vaccines and COVID-19 vaccines on each other’s development. Years of HIV vaccine research facilitated the rapid development of mRNA COVID-19 vaccines, and the success of COVID-19 vaccines might accelerate the development of mRNA HIV vaccines. The second major subtopic, comprising 17.6% (176/1000) of the posts, involved comparisons between HIV and COVID-19 in various aspects. Specifically, the development speed of HIV vaccines compared to COVID-19 vaccines was a major point of comparison. In addition, some posts questioned whether potential HIV vaccines could be comparable to COVID-19 vaccines in terms of cost and accessibility during rollout. Others raised concerns about efficacy, safety, and inequality for both vaccines. The third major subtopic, comprising 26.5% (265/1000) of the posts, connected COVID-19 vaccines with HIV. One issue discussed was whether COVID-19 vaccines contained, caused, or increased HIV. Another issue raised was distinguishing between HIV symptoms and COVID-19 vaccine side effects, such as a fabricated condition called VAIDS , short for vaccine-acquired immunodeficiency syndrome. The fourth major subtopic, comprising 13.6% (136/1000) of the posts, featured conspiracy theories that presented hypothesized patterns linking COVID-19, HIV, and their vaccines with malicious intent. Prominent conspiracy theories in this subtopic included connecting misinformation that COVID-19 vaccines contain, cause, or increase HIV with the ongoing development of HIV vaccines; associating HIV and AIDS symptoms with side effects of COVID-19 vaccines; and claiming that COVID-19 originated from unsuccessful HIV vaccine research. As this study also manually coded the 1000 most engaged negative posts to identify prominent conspiracy theories, additional results pertaining to conspiracy theories will be discussed further in another subsection. The remaining posts related to HIV and COVID-19 included those that generally mentioned research on them or made connections without specifying details.

a mRNA: messenger RNA.

a The reaction count is the total number of likes and reposts.

b PrEP: pre-exposure prophylaxis.

c VAIDS: vaccine-acquired immunodeficiency syndrome.

d The categories labeled as “other” contain various topics. Thus, no representative post is displayed.

RQ 2 asked about the sentiment valence present in all the posts. According to the standard VADER classification values, valence is categorized by compound scores as follows: positive (0.05 to 1), neutral (−0.05 to 0.05), and negative (−0.05 to −1) [ 65 ]. On average, all posts had a marginally positive score of 0.053. HIV and COVID-19 (topic 1) had a slightly positive average score of 0.055. The mRNA HIV vaccine trials (topic 2) had a neutral average score of 0.040, leaning toward the positive side. HIV vaccine and immunity (topic 3) had a more neutral average score of −0.0008. Moreover, 42.78% (15,584/36,424) of the posts were positive, 25.64% (9338/36,424) of the posts were neutral, and 31.58% (11,502/36,424) of the posts were negative.

Topics and Valence Across Post Types

Of the 36,424 posts, 18,580 (51.01%) were replies, making up over half of the overall count. Self-composed posts totaled 41.6% (15,151/36,424), whereas the remaining 7.39% (2693/36,424) were quote posts. RQ 3 asked about the distribution of topics and valence among the 3 post types. As Table 3 shows, the distribution of topics varied by post type (N=36,424, χ 2 4 =2511.4, P <.001). Of the self-composed posts, 85.36% (12,933/15,151) focused on HIV and COVID-19 (topic 1) and 13.21% (2001/15,151) focused on mRNA HIV vaccine trials (topic 2). In comparison, quote posts and replies exhibited a different pattern, in each case >97% of posts centering on HIV and COVID-19 (topic 1; 2616/2693, 97.14% and 18,129/18,580, 97.57%, respectively).

As Table 4 shows, the distribution of valence also varied by post type (N=36,424, χ 2 4 =911.7, P <.001). The proportion of positive posts was slightly higher among self-composed posts at 44.95% (6810/15,151) compared to replies at 41.09% (7634/18,580) and quote posts at 42.33% (1140/2693). Self-composed posts had a smaller proportion of negative posts at 23.56% (3570/15,151) compared to replies at 37.64% (6994/18,580) and quote posts at 34.83% (938/2693). The proportion of neutral posts was larger for self-composed posts at 31.49% (4771/15,151) compared to quote posts at 22.84% (615/2693) and replies at 21.27% (3952/18,580).

Regarding the distribution of topics and valence among the 3 types of posts, quote posts and replies displayed similarities, whereas self-composed posts diverged. Compared to self-composed posts, which initiate new conversations, there was a higher proportion of HIV and COVID-19-related posts (topic 1) and a greater proportion of negative posts among quote posts and replies, which contribute to existing conversations.

a N=36,424, χ 2 4 =2511.4, P <.001.

b mRNA: messenger RNA.

a N=36,424, X 2 4 =911.7, P <.001.

Content and Account Features Influencing User Reactions

RQ 4 asked about the influence of content and account features on likes and reposts.

Liking is more common than reposting. While 52.94% (19,284/36,424) of posts received an average of 24.83 likes, ranging from 1 to 102,843, a total of 25.13% (9155/36,424) posts received an average of 11.38 reposts, ranging from 1 to 10,572. Table 5 reveals the influence of content features (topics and valence) and account features (verification status and follower count) on the natural log-transformed number of likes and reposts. Both linear regression models were significant at P <.001. The adjusted  R 2 was 0.072 for the like model and 0.090 for the repost model.

Among the 3 topics identified using LDA, HIV and COVID-19 (topic 1) did not affect like counts but decreased repost counts. In comparison, mRNA HIV vaccine trials (topic 2) decreased like counts while increasing repost counts. Positive valence increased like and repost counts. Account verification status and follower count increased like and repost counts.

a The natural logarithm, ln (Y i +1), was calculated on like and repost counts. This transformation was conducted to include posts receiving 0 likes and reposts, as well as to account for the skewness of the data distribution.

b F (model significance): P <.001; adjusted R 2 =0.072.

c F (model significance): P <.001; adjusted R 2 =0.090.

d mRNA: messenger RNA.

e The models excluded topic 3 on HIV vaccine and immunity to address multicollinearity issues arising from its correlations with topics 1 and 2. The reported standard β for topic 3 represents a possible β value if it had been included in the models.

Posts With Most Reactions

Table 6 summarizes posts ranked within the top 5 for the number of likes and reposts presented in chronological order. It is worth noting that all posts in the top 5 for likes and reposts were self-composed. One particular post, which garnered the most likes (n=102,843) and reposts (n=10,572), expressed the incredible feeling of witnessing the development of an HIV vaccine within our lifetimes. It was posted by an unverified account on January 28, 2022, the day after Moderna’s announcement of clinical trials for its first mRNA HIV vaccine.

a Ranks beyond the fifth were not indicated.

Anti–HIV Vaccine Conspiracy Theories

RQ 5 asked about prominent anti–HIV vaccine conspiracy theories. Of the 1000 negative posts that received the most reactions, 227 (22.7%) contained conspiracy theories. As Table 7 shows, we classified these prominent anti–HIV vaccine conspiracy theories into 4 categories and presented their representative posts and the number of reactions.

The first category, comprising 44.9% (102/227) of the posts, formulated conspiracy theories by connecting COVID-19, COVID-19 vaccines, HIV, and HIV vaccines. For instance, 52.9% (54/102) of these posts connected the misinformation regarding COVID-19 vaccines containing, causing, or increasing HIV with the ongoing efforts to develop HIV vaccines. This misinformation may have arisen from past occurrences resurfacing following Moderna’s initiation of its mRNA HIV vaccine trials. One incident occurred at the end of 2020, when an Australian COVID-19 vaccine, which used a small fragment of protein from HIV to clamp SARS-CoV-2’s spike proteins, was abandoned due to false HIV-positive results [ 70 ]. Another incident occurred in October 2020, when 4 researchers sent a letter to a medical journal expressing concerns about the potential increased risk of HIV acquisition among men receiving COVID-19 vaccines using adenovirus type-5 vectors without supporting data from COVID-19 vaccines [ 71 ]. The misinformation typically interpreted the incidents out of context and generally suggested that COVID-19 vaccines contained, caused, or increased HIV without specifying details. In addition, there were conspiracy theories linking HIV and AIDS to COVID-19 vaccine side effects, including a fabricated condition known as VAIDS. VAIDS falsely suggests that COVID-19 vaccines caused immune deficiency [ 72 ]. Furthermore, there were claims that COVID-19 originated from unsuccessful HIV vaccine research.

The second category, comprising 38.3% (87/227) of the posts, suggested that the alignment of concurrent events with Moderna’s start of mRNA HIV vaccine trials in late January 2022 was intentional to manipulate the market for HIV vaccines. These events included the rising HIV discussion and fear; promotion of HIV tests by public figures [ 73 ]; the discovery of a new highly virulent HIV strain [ 74 ]; and the passing away of HIV researchers, including Luc Montagnier, codiscoverer of HIV with an antivaccine stance during the COVID-19 pandemic [ 75 ], all occurring in early February 2022.

The third category, with 11.5% (26/227) of the posts, revealed conspiracy theories based on the distrust of powerholders [ 76 ]. Some posts extended existing conspiracy theories, such as the Big Pharma conspiracy theory [ 50 ] and the New World Order conspiracy theory [ 51 ], into the context of HIV vaccines, emphasizing the intent of powerholders, including major pharmaceutical companies and governments, behind vaccine promotion for financial profits and society control. Other posts created conspiracy theories about the government’s research on HIV vaccines. The remaining posts generally stated that HIV vaccines were a scam. The final category comprised the remaining 5.3% (12/227) of the posts with other conspiracy theories.

It is worth noting that, of the 227 posts containing conspiracy theories, 39 (17.2%) were posted by accounts that had already been suspended at the time of manual coding. For these posts, the X platform displays the following message—“This post is from a suspended account”—and the content of the post is not visible. The X platform suspends accounts that violate its rules [ 77 ]. However, specific details of the violations are not accessible on the platform. The invisibility of these posts halted their spread when the suspension was enacted. For our manual coding of these posts, we used the text obtained during the data collection process.

b The posts were from suspended accounts.

d The categories labeled as “other” contain various conspiracy theories. Thus, no representative post is displayed.

Principal Findings

This study investigated the patterns of public discourse and the message-level drivers of user reactions on the X platform regarding HIV vaccines through the analysis of posts using machine learning algorithms. We examined the distribution of topics and valence across different post types and assessed the influence of content features (topics and valence) and account features (account verification status and follower count) on like and repost counts. In addition, we manually coded the 1000 most engaged posts about HIV and COVID-19 to understand the salient aspects of HIV vaccines related to COVID-19 and the 1000 most engaged negative posts to identify prominent anti–HIV vaccine conspiracy theories.

The results revealed that COVID-19 plays a substantial role as a context for public discourse and reactions regarding HIV vaccines. Of the 3 topics identified using LDA, the leading topic was HIV and COVID-19, covering 78% of tokens and dominating in 92.46% (33,678/36,424) of the posts. Furthermore, on each of the top 4 days with the highest post counts, most of the posts were about HIV and COVID-19. This comprehensive topic included important subtopics that linked HIV vaccines with COVID-19 vaccines, as demonstrated through the manual coding of the 1000 most engaged posts about HIV and COVID-19. These subtopics encompassed the reciprocal influence of HIV vaccines and COVID-19 vaccines in advancing each other’s development; comparisons in their development speed; inquiries about the possible alignment of HIV vaccines with COVID-19 vaccines in terms of cost and accessibility during distribution; and concerns about efficacy, safety, and equality for both vaccines.

COVID-19 positioned HIV vaccines in both a positive and negative context. On the one hand, the success of mRNA technology in COVID-19 vaccines [ 6 ] potentially cast mRNA HIV vaccines in a positive light. The topic of HIV and COVID-19 had a marginally positive valence score of 0.055. Moreover, 3 (60%) out of the 5 most liked posts and 2 (40%) out of the 5 most reposted posts expressed excitement about advancements in HIV vaccines that were based on the experience with COVID-19 vaccines. On the other hand, antivaccine discourse, including conspiracy theories, heated up during the COVID-19 pandemic [ 10 , 11 , 27 , 44 , 48 , 49 ], which posed challenges to HIV vaccines. Of the 1000 most engaged posts about HIV and COVID-19, a total of 136 (13.6%) featured conspiracy theories. Of the 1000 most engaged negative posts, 227 (22.7%) contained conspiracy theories, with 102 (44.9%) of them revolving around HIV and COVID-19. For instance, a prominent conspiracy theory connected the misinformation about COVID-19 vaccines containing, causing, or increasing HIV infection [ 55 ] with the initiation of clinical trials for mRNA HIV vaccines [ 4 , 5 ], implying a malevolent intent behind the deliberate connection. The results indicate that conspiracy theories tend to elicit an approach-oriented response, as evidenced by people engaging in liking and reposting, as opposed to an avoidance-oriented approach [ 39 ]. This underscores the need to intensify efforts to counter conspiracy theories in public health communication about HIV vaccines.

According to a study conducted by the Pew Research Center, irrespective of the subject matter, replies constituted the largest portion of original posts on X, followed by self-composed and quote posts [ 28 ]. Specifically, the number of replies was 3 times greater than that of self-composed posts. In this study, although replies constituted slightly more than half (18,580/36,424, 51.01%) of the posts, it is worth noting that the subject of HIV vaccines elicited a higher proportion of self-composed posts at 41.6% (15,151/36,424). Specifically, the number of replies was 23% higher than that of self-composed posts. Moreover, the topic of mRNA vaccine trials was most evident in self-composed posts compared to replies and quote posts. In comparison, there was a higher proportion of focus on the topic of HIV and COVID-19 and a greater proportion of negative posts among quote posts and replies, which contribute to existing conversations. This suggests that users were more likely to initiate new conversations rather than joining existing conversations about mRNA HIV vaccines. In contrast, they were more likely to join existing conversations rather than starting new conversations about HIV and COVID-19. In addition, users were less likely to initiate new conversations negatively but more likely to contribute negatively to existing ones.

As the primary topic, HIV and COVID-19 had no impact on like counts but had a negative impact on repost counts. In comparison, the topic of mRNA HIV vaccine trials had a negative impact on like counts and a positive impact on repost counts. The results should be interpreted while considering that, as revealed in previous research [ 16 , 34 ] and this study, most posts on the X platform are unlikely to receive likes and even less likely to receive reposts. In this study, among the total of 36,424 posts, approximately half (n=19,284, 52.94%) received likes, and approximately one-quarter (n=9155, 25.13%) received reposts. To include all posts and mitigate the data distribution skewness in the linear regression analysis, we applied the “plus one” technique. This involved adding a constant value of 1 to all like and repost data points before taking the natural logarithm. Although most posts were not liked or reposted, it is noteworthy that the topic of mRNA HIV vaccines led to an increase in repost counts, highlighting its positive influence on social sharing. In addition, 2 (40%) out of the 5 most reposted posts were about mRNA HIV vaccine trials. These results correspond to the findings of previous research that suggested the diffusion of novel useful information [ 12 , 16 , 32 , 36 ].

The overall valence of the posts about HIV vaccines was marginally positive. The positivity aligns with the positive sentiment found in posts on X about vaccines in general [ 13 - 15 ] and COVID-19 vaccines in particular [ 12 , 16 , 17 ]. However, the positivity about HIV vaccines was not apparent as the average score of 0.053 placed it on the edge of the neutral range, which goes from −0.05 to 0.05 according to the standard VADER classification values. Positive sentiment had a favorable impact on like and repost counts, partially consistent with findings of previous research on COVID-19 vaccines [ 16 ]. The post that achieved the most likes conveyed the incredible feeling of witnessing the development of an HIV vaccine in our lifetimes. This could be attributed to the psychological rationale that social transmission of positive content fulfills people’s motivation to present a positive image [ 35 , 37 ]. In alignment with the findings of previous research [ 13 , 16 , 34 ], account verification status and follower count increased like and repost counts.

This study has implications for public health communication related to HIV vaccines and potentially other vaccines. Given the massive scale of the COVID-19 vaccination campaign, it is understandable that people will draw comparisons with other vaccines. Topic modeling identified HIV and COVID-19 as the primary topic, and manual coding revealed various intertwined aspects. Leveraging the advantages observed in the COVID-19 vaccine campaign, such as its widespread accessibility, could be valuable. Furthermore, addressing common concerns such as efficacy, safety, and inequality could also prove beneficial.

In the case of HIV vaccines, it is essential to tackle concerns associated with COVID-19 vaccines, especially those related to HIV vaccines. A major subtopic of HIV and COVID-19 involved suspicions about COVID-19 vaccines containing, causing, or increasing HIV. Another major subtopic was the confusion between HIV symptoms and the alleged side effects of COVID-19 vaccines, such as VAIDS. Misinformation concerning both subtopics has been woven into conspiracy theories, further complicating this situation. To combat misinformation and conspiracies that have these elements, efforts could focus on promoting evidence-based factual information [ 45 - 47 ].

Another notable technique in the conspiracy theories was linking concurrent COVID-19 and other HIV-related events in unsubstantiated relationships to create false perceptions, suggesting that these events were intentional to manipulate the market for HIV vaccines. These HIV-related events included rising HIV discussion and fear, promotion of HIV tests by public figures [ 73 ], the discovery of a new highly virulent HIV strain [ 74 ], and the passing away of HIV researchers, all occurring in early February 2022. These findings suggest that refuting false connections among such concurrent events can be an effective strategy to counter these conspiracy theories [ 45 - 47 ]. These occurrences, frequently entwined within conspiracy theories, could be specifically addressed in public health communication efforts.

Limitations

This study has several limitations. Because we used autoidentified content features (topics and valence) and autoextracted account features (verification status and follower count) in the regression models to predict the autoextracted number of user reactions (likes and reposts), the results were mostly limited to the examined autoidentified and autoextracted factors. For instance, political polarization, which manifested in a wide range of issues, including response to vaccines [ 78 ], could be a factor worth investigating in future studies. Furthermore, manual coding of conspiracy theories revealed a prevalent technique of twisting concurrent events into false relationships. This underscores the significance of refuting unfounded associations among these incidents to counter such conspiracy theories. It will be interesting for future research to assess the impact of this technique on user reactions to conspiracy theories. These findings could provide further insights into public health communication strategies to combat conspiracy theories.

Conclusions

The results highlight COVID-19 as a significant backdrop for public discourse and reactions on the X platform regarding HIV vaccines. COVID-19 situated HIV vaccines in both a positive and negative context. The success of mRNA COVID-19 vaccines shed a positive light on HIV vaccines. However, COVID-19 also situated HIV vaccines in a negative context, as evident in anti–HIV vaccine conspiracy theories falsely linking HIV vaccines to COVID-19. The findings provide implications for public health communication strategies concerning HIV vaccines.

Acknowledgments

This study was supported in part by the College of Arts and Sciences and the Harrington School of Communication and Media at the University of Rhode Island. The authors express their appreciation for the support. The funders had no role in study design, data collection, data analysis, data interpretation, or writing of the report.

Data Availability

The data sets collected and analyzed during this study are available from the corresponding author upon request.

Conflicts of Interest

None declared.

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Abbreviations

Edited by G Eysenbach; submitted 04.10.23; peer-reviewed by X Ma, J Zhang; comments to author 18.10.23; revised version received 08.11.23; accepted 28.02.24; published 03.04.24.

©Jueman M Zhang, Yi Wang, Magali Mouton, Jixuan Zhang, Molu Shi. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 03.04.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

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Modern Diagnostic Imaging Technique Applications and Risk Factors in the Medical Field: A Review

Shah hussain.

1 Department of Biochemistry, Faculty of Biological Sciences, Quaid-i-Azam University Islamabad, Pakistan

Iqra Mubeen

Niamat ullah.

2 Department of Pharmacy, Faculty of Biological Sciences, University of Malakand, Pakistan

Syed Shahab Ud Din Shah

Bakhtawar abduljalil khan.

3 Women's Wellness and Research Center, Hamad Medical Corporation, Doha, Qatar

Muhammad Zahoor

4 Department of Biochemistry, University of Malakand, Chakdara, Dir Lower, KPK, Pakistan

5 Department of Pharmacognosy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia

Farhat Ali Khan

6 Department of Pharmacy, Shaheed Benazir Bhutto University, Sheringal, Dir Upper, KPK, Pakistan

Mujeeb A. Sultan

7 Department of Pharmacy, Faculty of Medical Sciences, Aljanad University for Science and Technology, Taiz, Yemen

Associated Data

This is a review article. All data are taken from published research papers and available online.

Medical imaging is the process of visual representation of different tissues and organs of the human body to monitor the normal and abnormal anatomy and physiology of the body. There are many medical imaging techniques used for this purpose such as X-ray, computed tomography (CT), positron emission tomography (PET), magnetic resonance imaging (MRI), single-photon emission computed tomography (SPECT), digital mammography, and diagnostic sonography. These advanced medical imaging techniques have many applications in the diagnosis of myocardial diseases, cancer of different tissues, neurological disorders, congenital heart disease, abdominal illnesses, complex bone fractures, and other serious medical conditions. There are benefits as well as some risks to every imaging technique. There are some steps for minimizing the radiation exposure risks from imaging techniques. Advance medical imaging modalities such as PET/CT hybrid, three-dimensional ultrasound computed tomography (3D USCT), and simultaneous PET/MRI give high resolution, better reliability, and safety to diagnose, treat, and manage complex patient abnormalities. These techniques ensure the production of new accurate imaging tools with improving resolution, sensitivity, and specificity. In the future, with mounting innovations and advancements in technology systems, the medical diagnostic field will become a field of regular measurement of various complex diseases and will provide healthcare solutions.

1. Introduction

Medical imaging is the process of visual representation of the structure and function of different tissues and organs of the human body for clinical purposes and medical science for detailed study of normal and abnormal anatomy and physiology of the body. Medical imaging techniques are used to show internal structures under the skin and bones, as well as to diagnose abnormalities and treat diseases [ 1 ]. Medical imaging has changed into healthcare science. It is an important part of biological imaging and includes radiology which uses the imaging technologies like X-ray radiography, X-ray computed tomography (CT), endoscopy, magnetic resonance imaging (MRI), magnetic resonance spectroscopy (MRS), positron emission tomography (PET), thermography, medical photography, electrical source imaging (ESI), digital mammography, tactile imaging, magnetic source imaging (MSI), medical optical imaging, single-photon emission computed tomography (SPECT), and ultrasonic and electrical impedance tomography (EIT) [ 2 ].

Imaging technologies play a vital role in the diagnosis of abnormalities and therapy, the refined process of visual representation which contributes to medical personnel access to awareness about their patient's situation [ 3 , 4 ]. Electroencephalography (EEG), magnetoencephalography (MEG), and electrocardiography (ECG) are recording and measurement techniques that are not responsible to produce images, but these represent the data as a parameter graph vs. time or maps which shows the susceptible information with less accuracy. Therefore, these technologies can be said to form medical imaging on a limited scale. Worldwide, up until 2010, approximately 5 billion medical imaging techniques studies have been shown [ 5 ].

In the United States, approximately 50% of total ionizing radiation exposure is composed of radiation exposure from medical imaging [ 6 ]. Medical imaging technologies are used to measure illnesses, manage, treat, and prevent. Nowadays, imaging techniques have become a necessary tool to diagnose almost all major types of medical abnormalities and illnesses, such as trauma disease, many types of cancer diseases, cardiovascular diseases, neurological disorders, and many other medical conditions. Medical imaging techniques are used by highly trained technicians like medical specialists, from oncologists to internists [ 1 ].

Medical imaging technologies are mostly used for medical diagnoses. Medical diagnosis is the process of identification of patient disease and its symptoms. The medical diagnosis gives the information about the disease or condition needed for treatment that is collected from patient history and physical checkups or surveys. Due to no specificity of the many signs and symptoms of a disorder, its diagnosis becomes a challenging phase in medical science. For example, the case of erythema (redness of the skin) gives a sign of many diseases. Thus, there is a need for different diagnostic procedures the determination the causes of different diseases and their cure or prevention [ 7 ].

Historically, the first medical diagnosis composed by humans was dependent upon the observation of ancient doctors with their eyes, ears, and sometimes examination of human specimens. For example, the oldest methods were used to test on body fluids like urine and saliva (before 400 B.C). In ancient Egypt and Mesopotamia, doctors were able to measure the problem of the digestive tract, blood circulation, heartbeat, spleen, liver menstrual problems, etc. But unfortunately, medicine for curing diseases was only for wealthy and royal people.

At around 300 B.C., the use of the mind and senses as diagnostic tools was promoted by Hippocrates. He got a reputation as the “Father of Medicine.” Hippocrates supported a diagnostic protocol by testing the patient urine, observing the skin color, and listening to the lungs and other outward appearances. The link between disease and heredity also had been recorded by them [ 8 ]. In the Islamic world, Abu al-Qasim al-Zahrawi (Arabic physician) provided the first report on a hereditary genetic disease referred as hemophilia. In this report, he wrote about a family of Andalusia, whose males died due to hemophilia [ 9 ]. In the Middle Ages, many different techniques were used by physicians to detect the causes of imbalance function of the body. Uroscopy was the most common method of diagnosis. The patient's urine was collected in a special type of flask known as “Matula.” Urine was checked on the basis of color, smell, density, and presence of precipitate [ 10 ]. The viscosity and color of the blood were also examined by physicians to detect chronic or acute diseases [ 11 ]. The pulse rate, power, and tempo of a patient's artery were observed by physicians through a technique known as palpation [ 12 ]. In Middle Ages, physicians were also used to combining the study of medicine and zodiac signs [ 13 ].

In the 19 th century, X-rays and microscopes were the diagnostic tools that helped to diagnose and treat illnesses. At the beginning of the 19 th century, medical doctors diagnosed diseases by the examination of symptoms and signs. By the 1850s, many diagnostic tools such as ophthalmoscopes, stethoscopes, and laryngoscopes lead to evoke the medical doctors with the sensory power to develop other novel methods and techniques for diagnosing different illnesses. And in this way, a series of diagnostic tools including chemical tests, bacteriological tests, microscopic tests, X-ray tests, and many other medical tests were generated [ 8 ].

Medical imaging techniques are developed after the discovery of X-rays. In November 1895, Wilhelm Conrad Roentgen discovered X-ray. He got the Nobel Prize in 1901 for his discovery. Radiologists gave names to X-ray basis as “X-rays” or “plane film” used for diagnosing bone fractures and chest abnormalities. Fluoroscopy was developed due to a more powerful beam of X-ray for diagnosing the patient abnormalities. In 1920s, radiologists started giving information about various diseases like cancer of the esophagus, ulcers, and stomach. Fluoroscopy is now converted into computed tomography (CT).

Today CT scan is commonly used to diagnose many diseases. The mammography technique also uses an X-ray beam, to generate high-resolution breast images, monitoring breast cancer. In the 1940s, the X-ray tomography technique was developed, looking for a desired part of the tissue. In this technique, the whole process was accomplished by rotating the tube of X-ray focus on part of the tissue. Today, tomography is replaced with advanced imaging techniques such as CT scanning or computerized axial tomography (CAT) scanning. X-ray is also a source of a technique known as “angiography,” which is used to obtain images of blood vessels. In 1950s, diagnostic imaging tests along with nuclear medicine were started. Radioactive compounds are used as X-ray sources rather than X-ray tubes. Radioactive compounds produce gamma rays. They are joined with other complexes that are an essential part of the disease analysis to study a certain illness. For an instant, technetium 99m is combined with methylene diphosphonate, which is absorbed by bone tumors. In this way, breast or lung cancer spread to other body parts such as bone can be detected from this type of nuclear bone scan technique [ 14 ].

2. Advance Modalities in Medical Imaging

Many advanced techniques are developed and can be explained with their principle of work, application in medical labs, and development in imaging techniques. Computed tomography (CT), positron emission tomography (PET), magnetic resonance imaging (MRI), single-photon emission computed tomography (SPECT), digital mammography, and sonography are included in advanced medical imaging techniques. These are all mentioned below to understand their advantages and applications in the diagnosis, management, and treatment of different diseases such as cardiovascular disease, cancer, neurological illnesses, and trauma. These techniques are readily used by clinicians because through images, they can easily choose how to manage diseases.

3. Computed Tomography (CT)

In the 1969s, Hounsfield invented the first CT-scanner prototype [ 15 ]. Computed tomography is also known as X-ray CT. A CT scan is used by radiologists, biologists, archaeologists, and many other scientists to generate cross-sectional images of different scanned objects. A modern CT scanner system is shown in Figure 1 . In the medical field, technicians use CT scanners, machines to produce the images that lead to diagnosing the abnormalities and other therapeutic measurements. In this technology, X-rays are produced from different angles that are eventually processed by computers to create tomographic images. This computer-based technology has been greatly improved, developing reconstructed images with high revolution [ 16 ]. In the pharmaceutical industry, it has been used to study and improve the medicine manufacturing process to generate good quality products [ 17 ].

CT scanner.

Positron emission tomography (PET) and single-photon emission computed tomography (SPECT) are the types of CT scan. An X-ray generator is used to generate the X-rays that rotate nearby the object to be scanned. X-rays are detected by an X-ray detector located on the opposite side of the source of X-rays. A sonogram is obtained, which is a visual representation of raw form data. This scanned data is processed in the form of tomographic reconstruction that leads to generating a series of cross-sectional photos. CT scan is performed by special individuals called radiology technologists or radiographers. Over the last two decades, CT is used largely in many clinical labs in different countries [ 18 ]. According to an estimated study, almost a 72 million scans were achieved in the US in 2007 and 80 million scans in 2015 [ 19 ].

CT is an effective technique for monitoring various types of cancers such as cancer of the bladder, kidneys, skeleton, neck, and head and for diagnosing infection [ 20 – 22 ]. CT also identifies distant metastases to the lungs, skeleton, liver, and brain. CT has made a high impact on the brain and lungs [ 23 , 24 ]. CT scan is the best method than other techniques in detection as well as recording modifications in tumor mass during treatment [ 25 ]. It may show a bloated belly with enlarged lymph nodes in patients with bronchus carcinoma. In this way, CT scan help in performing before surgery [ 26 ]. Another major application of CT scans is the detection of heart diseases like myocardial disease, congenital heart disease, and coronary artery bypass grafts [ 27 ]. Gastroenterologists mostly use computed tomography for the analysis of the liver or pancreas of patients. Tumors of size 1.5-2.0 cm in diameter can be detected by CT scan. Furthermore, biliary obstruction caused by lesions can also be monitored by this technology [ 28 ]. One of the rewarding roles of technology is to study suspected intra-abdominal abnormalities with 95% accuracy, and treatment decisions can be easily made [ 29 ].

A big drawback of computed tomography is that large masses within the gastrointestinal tract may not be visible during the abdominal investigation. There is also no finding of some of the mucosal abnormalities by it. CT scan is highly useful to manage abdominal disorders such as carcinoma of the stomach, esophagus, and rectum more accurately as compared to other modalities [ 30 , 31 ] The middle column of the spine can be visualized by using the computed tomography technique during dislocation type of fractures in many thoracolumbar fractures. CT also detects lesions and provides nonsurgical management of some disorders, for example, unstable burst damages [ 31 ].

All spinal injuries are unstable and known as translational injuries. Before surgery of such patients, complete information about the site of ligament discontinuity of respective vertebrae is provided by computed tomography. It also gives the prediction of whether Harrington-rod stabilization is possible or not. CT scan can provide detailed evidence of distraction injuries and fractures. For example, flexion-distraction injury between the 11 th and 12 th thoracic vertebrae and spinal injury between the 2 nd and 3 rd lumbar vertebrae have been scanned by computed tomography scan as shown in Figures ​ Figures2 2 and ​ and3, 3 , respectively [ 32 ].

Distraction injury scanned by CT scan (showing damage occurrence at the 11 th and 12 th thoracic vertebrae).

An axial scan of a spinal injury by computed tomography (CT) at the 2 nd and 3 rd lumbar vertebrae.

Advanced CT bone imaging techniques include volumetric quantitative CT (QCT), high-resolution CT (CT), and micro-CT. High-resolution CT and high-resolution MR are generally used in vivo ; micro-CT and micro-MR are usually used in vitro systems. These advanced modalities are used for bone imaging to investigate bone diseases especially osteoporosis and bone cancer. In osteoporosis, disorder advanced CT bone imaging provides information about bone mineral density (BMD), bone strength, a risk factor for osteoporosis, and recovery factors after medication or bone therapy.

Dual-energy X-ray absorptiometry (DXA) and volumetric QCT are the quantitative methods used for weighing the macrostructure of suspected bone. High-resolution CT and micro-CT methods are applicable for measuring the microstructure of trabecular bone without any invasiveness or destructiveness. CT and MRI have been used to obtain bone structure. However, the CT field has been more developed as compared to other techniques, because there are more advantages of CT-based modalities; for example, QCT generates three-dimensional (3D) images in such a way that trabecular and cortical bone can be distinctly measured. vQCT technology is quicker than MRI [ 33 ].

4. Volumetric Quantitative CT (vQCT)

Initially, QCT has been used to measure trabecular BMD of the forearm and lumber midvertebrae through a particular transverse CT slice. The measurement of BMD is a static property of the advanced spiral QCT [ 34 ]. Trabecular bone in the spine and cortical bone in the hip may be indicated by this technology to estimate the fracture risk [ 35 ].

For the improvement of the 3D structure of the cortex, almost 0.5 mm isotropic spatial resolution is required, but still, almost 1.5 to 2 mm resolution is provided by QCT which is not adequate to make accurate images. This is a drawback of QCT. In general, the measurement of accurate cortical thickness for the femur is easier than the thickness in the spine, especially in aged people. Researchers have shown that women grow faster not only with small vertebrae as well as reduced bone mass but also with a slow rate of increase in cross-sectional area as compared to men [ 35 ]. QCT is a CT imaging technique that can provide information about bone density. For example, a QCT scan of the femur for the measurement of macrostructure and bone mineral density (BMD) has been shown in Figure 4(a) [ 33 ].

(a) Femora undergo vQCT to determine BMD and macrostructure. (b) Ultradistal forearm undergoes CT to measure the structure of the trabecular complex network and its texture.

5. High-Resolution CT (hrCT)

High-resolution CT is a modern CT scanner that usually requires a high radiation dose but produces high-resolution images of bone such as forearm bone submitted to CT to determine the trabecular and cortical network and texture as shown in Figure 4(b) [ 33 ]. According to many different cross-sectional studies, CT gave better imaging results in distinguishing fractured vertebral trabecular structures from nonfractured structures as compared to DXA measurements of BMD [ 36 ].

6. Micro-CT ( μ CT)

Micro-CT with 1-100  μ m spatial resolution is typically known as microscopy. Micro-CT has abilities to replace the standing techniques used in in vivo measurements in rats and mice like animals. Initially, the micro-CT technique used synchrotron radiations to obtain ultra-high-resolution applications [ 37 ].

Mostly, now, the convenient method of X-ray tube-based micro-CT is used in university-based research laboratories and special clinical centers. To make 3D structures of bone, some special software (for example, FEM) is attached with a micro-CT scanner. Finite element modeling (FEM) is a software mostly used in engineering. Its goal is to help information of 3D structures of bone for analysis of fractured bone part structures from nonfractured bone structures. Currently, structural models are generated by volumetric QCT, and computer-based programs give the element elastic properties from the bone density at the site of elements [ 38 ].

Arlot and coworkers determined the 3D microstructure of bone of postmenopausal women with osteoporotic disease; they have accomplished treatment with proper strontium ranelate therapy for 36 months [ 39 ]. Researchers investigate these 3D micro-bone structures as shown in Figure 5 [ 33 ]. Over the past two decades, considerable development occurred in imaging technologies for osteoporosis bone disease analysis. Despite the development in these technologies, there are many challenges for bone imaging, such as the sample size, spatial resolution, complexity, radiation exposure, time, and cost. Finally, there is still a requirement of high accuracy, availability, reproducibility, and proper monitoring procedure for better bone imaging [ 33 ].

Microstructure of transiliac bone biopsies is determined to undergo 3D micro-CT of two postmenopausal women who have accomplished strontium ranelate therapy for 36 months: (a) strontium ranelate therapy; (b) placebo.

CT scan for lung disease is highly used in present days. For example, a low-radiation helical chest CT scan is used to investigate lung cancer (bronchopulmonary cancer) [ 40 , 41 ]. Another lung disease is the most common type of progressive idiopathic interstitial pneumonia mostly in adults known as idiopathic pulmonary fibrosis (IPF). In IPF patients, CT-based methods include density histogram analysis, CT scan of whole lungs, and density mask technique, and other structural or texture classification methods are greatly used to examine the pulmonary function, lung disease progression, and mortality. For example, lung images of a 73-year-old male IPF patient have been taken by CT as shown in Figure 6 . These methods have the property of time efficiency, availability, and reproducibility. Still, there are many issues interrelated to computer-based CT in IPF disease analysis. But it is promising by scientists to develop advanced CT imaging techniques that must play a vital role in the future to manage lung diseases as well as other abdominal diseases [ 42 , 43 ].

Images derived using a system known as GHNC (Gaussian Histogram Normalized Correlation). Lung images of a 73-year-old male IPF patient have been taken by computed tomography (CT). Light blue and yellow color, fibrosis; dark blue color, emphysema; pink color, normal; and light green color is indicated as ground-glass opacity.

There are many possible reasons for the usage of CT scans, for example, to determine or investigate the acute stroke in the patient's head. CT is applicable to establish the diagnosis, investigate the type of stroke, respond to surgery, and finally manage the disease [ 44 ]. CT scan of the head is also responsible for the investigation of dementia disease. Accurate diagnosis is directly related to proper management of symptoms and signs of dementia. Patients with treatable lesions can also be identified by computed tomography [ 45 ]. Abdominal computed tomography is a new technology for identifying fungal infection known as disseminated fungal infection (DFI) in pediatric cancer patients. Currently, abdominal CT is greatly applicable for the diagnosis and management of DFI in cancer patients [ 46 ]. During the past few years, the usage of CT scan has become a national trend in emergency departments, especially in the US. Computed tomography plays an expanding role in diagnosing acute and chronic diseases as well as life-threatening diseases such as stroke, head injury, major trauma, heart disease, abdominal pain, pulmonary embolism, severe chest pain, and renal abnormalities [ 47 ].

7. 3D Ultrasound Computed Tomography (3D USCT)

3D USCT is a promising technology for imaging breast cancer. Simultaneous recording of reproducible reflection, speed of sound volume, fast data collection, attenuation, and high image quality production are all the main advantages of the USCT system. 3D USCT system is a full potential device used for clinical purposes. Only in 4 minutes, the full volume of breast can be picked up [ 48 ].

8. Risks of Computed Tomography

Computed tomography risks are small but if these small risk s are produced by million numbers of scans, they may drive into serious public health concerns in the future, especially from pediatric CT. The risk of cancer due to computed tomography scanning is increasing [ 49 ]. Children are of specific concern due to the sensitivity of radiation-induced cancer as compared to adults. According to a study, the risks of leukemia and brain tumors are mostly revealed after exposure to radiation from CT scans [ 50 ].

According to the authors of a recent report “long-term risks from CT scans directly would require very large-scale studies with lifelong follow-up.” The author gave this statement after the study date on CT scan exposure leading to the risk of future cancer [ 51 ]. Radiologists should be a source of discussion earlier to perform imaging technologies that contain high doses of radiation. They should also know about the risk factors of imaging technologies that can cause more adverse effects than recovery. Both families and patients should also raise the question/answers about the benefits and risks of CT scans [ 52 ].

9. Positron Emission Tomography (PET)

Positron emission tomography is a nuclear medicine functional technique that is used to display the total concentration of radioactive labeled elements in the body with clear images. It has the potential to diagnose biological processes within living bodies and is highly applicable for clinical purposes [ 53 ]. 3D images of positron-emitting radionuclides within the body are made by a computer system. In PET-CT scanners, 3D imaging is created with the help of a CT X-ray scan implemented on the patient body in the same machine and session. Positron emission tomography (PET) and nuclear magnetic resonance (NMR) both are quantitative radiological techniques that display information about biochemistry and physiology, normality, or abnormality. Nuclear magnetic resonance is not more sensitive to give high-resolution images by the distribution of substances except hydrogen. It can measure the total concentration of ATP and creatine phosphate (CP) in particular areas of the brain. Thus, both NMR and PET performed their specific function in the diagnosis. In 1953, the first PET system was established at Massachusetts General Hospital. It was followed by many other devices in a series manner such as tomographic positron camera, PET scanner, and other PET instruments [ 54 ].

10. Working Principle of PET

The PET technique detects radioactivity emission when a small concentration of radioactive tracer is intravenously injected. These tracers are frequently labeled with carbon-11, nitrogen-13, oxygen-15, and fluorine-18, as shown in Figure 7 . There is no positron emitter of hydrogen. The radioactive dose amount is the same as used in CT. 10-40 minutes is required to complete the process to perform a PET scan. The patient is fully clothed during scanning. There are specific steps in PET scan processing, explained in Figure 7 [ 55 ].

Basic principle of PET scan: (1) a positive electron (positron) is emitted by radioactive decay of radioisotope, for example, carbon-11; (2) this positron hits an electron present in the tissue to be analyzed and emits two photons having low energy; (3) scintillation crystals are present in the PET camera to absorb this emitted photon with low energy; (4) the light is produced that is converted into another signal such as electrical signals used by the computer system to produce 3D images.

Two molecular probes are mostly used to explain PET assay: 2-[F-18] fluoro-2-deoxy-D-glucose (FDG) and 3-deoxy-3-[F-18] fluorothymidine (FLT). FDG is the analog of F-18-labeled glucose, and it is used to identify diseases by changing the metabolism of glucose in heart diseases, Alzheimer's disease, and cancer. FLT is the analog of F-18 labeled thymidine and is highly used to estimate processes like cell proliferation and DNA replication by analysis of the phosphorylation process and thymidine transport. Thus, FLT and FDG are considered as best candidate probes/tracers for molecular imaging.

An early diagnosis of Alzheimer's disease can be scanned by PET technology with 93% accuracy. Huntington's disease, a hereditary disease was also detectable by PET scan. The development of PET technology provides accurate whole-body images for examining early primary and metastatic diseases. Imaging of transgenes provides information on the regulation of gene expression during cell proliferation, growth, response to environmental stimuli, the aging process, and gene therapy. Such endogenous gene expression can be monitored through the developed PET approach, which uses F-18-labeled oligodeoxynucleotides having a short single strand of almost fifteen nucleotides. In monkeys with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine- (MPTP-) induced lesions on one side of the brain, the study of restoring dopamine production by gene therapy can be assessed by PET imaging, as shown in Figure 8 [ 56 ].

PET images of gene therapy in unilateral MPTP monkey as Parkinson's model: (a) image of normal dopamine production; (b) the image is representing autonomous dopamine MPTP-induced shortage (before gene therapy); (c) image of the restoration of dopamine production in the caudate and putamen (after gene therapy) [ 56 ].

The combination of PET and computed tomography (PET-CT) forms a hybrid imaging approach that is highly used to gain functional and metabolic information to measure inflammatory and infectious diseases to assess their proper treatment. PET-CT hybrid imaging technique can provide quick information during the diagnosis of a disease and its treatment response [ 57 ].

Haberkorn et al. measured FDG uptake that relates to the proliferation rate of tumor cells in the head and neck with different patterns, in two groups of patients [ 58 ]. The FDG uptake was also measured in the malignant neck and head tumors and metastases process by the use of FDG-PET. Minn and coworkers found that the uptake of FDG is related to the proliferation rate of tumor cells [ 59 ]. Jabour et al. measured the normal anatomy of the neck and head [ 60 ]. In this way, change in uptake FDG as investigated by PET provides necessary information for clinical and anticancer therapeutics [ 61 ].

In the human cerebellum, changes in local neuronal function by voluntary movement and tactile stimulation were also mapped with the help of the PET approach detection of brain blood flow. According to research, finger movement leads to the production of parasagittal and bilateral blood flow enhancement in the superior and anterior hemispheric cortex of the brain human brain cerebellum. The enhancement in midline blood flow in the posterior vermis of the human brain cerebellum is produced by saccadic eye movement. PET also allows the measurement of structural and functional relations in the cerebellum of the human brain [ 62 ]. The development of the PET brain imaging technology makes it possible to advance understanding of the anatomy of brain parts and map of neuroanatomical basis of cognitive processes and memory [ 63 ]. A pathogen SIV (simian immunodeficiency virus) causes infection in rhesus macaques (a type of monkeys) with acute viremia, and progression leads to infection in the solid tissues of lymphoid, and then, cellular degradation becomes a terminal disease, and death occurs in most cases. So, the FDG-PET imaging technique is used to take images from SIV-infected animals. In this way, infected groups can be distinguished from the uninfected control groups [ 64 ].

11. Future of PET Technology

The PET scan can be used to measure the concentration of amino acids, sugar, fatty acids, and receptor in the living body. It is a new diagnostic tool used to detect diseases such as atherosclerosis, aging, cancer, and schizophrenia, although improvement in instrumentation and modeling is still required for future purposes. Emission tomography has also been associated with a small risk of ionizing radiations [ 54 ].

12. Magnetic Resonance Imaging (MRI)

Magnetic resonance imaging (MRI) is primarily an imaging technique that is applicable for noninvasive visualization of the anatomy and physiology of the body in both disease and health conditions. An MRI-related technique known as echo-planar imaging (EPI) was developed by physicists Peter Mansfield and Paul Lauterbur in the late 1970s [ 65 ]. Magnetic fields, electric fields, and radio waves are used in an MRI scanner to produce images of organs and the structure of the body. The SI unit of magnetic flux density (magnetic field strength) is measured in tesla (T).

The most common detections by MRI are multiple sclerosis, CNS tumors, brain and spine infections, stroke, injuries in ligaments and tendons, muscle degradation, bone tumor, and occlusion of blood vessels. MRI uses nonionizing radiation, frequently preferable compared to CT. MRI also provides excellent contrast of soft tissues; for example, the white and gray matter structure of the brain can easily be distinguished through this approach. MRI employs other different techniques such as functional MRI, magnetic resonance angiography (MRA), susceptibility-weighted, diffusion-weighted (PWI), diffusion-weighted (DWI), gradient echo, and spin-echo. It provides an image of good quality without requiring repositioning of the patient [ 66 ]. There are several benefits to MRI such as it is a painless, noninvasive technique with high spatial resolution and nonionizing radiations. MRI is mostly used independently for soft tissue analysis.

13. Working Principle of MRI

An MRI machine consists of multiple components, including a slab for patients to lie on, a superconducting magnet, a protective cage, the operator's console, and computers to analyze the data and product images. During the MRI scanning process, the machine's magnet produces a strong magnetic field. Hydrogen ions align in the target body part of the patient due to a stable magnetic field. Then, bombardment of radiofrequency waves causes the alignment of lined-up hydrogen ions to move out, and then, ions return to their equilibrium state [ 67 ]. An attached computer system converts the spin echoes (signal) of hydrogen ions into the images, after several “shifting” and “working on.” A microphone is also present inside the MRI unit for communication between the patient and technologist during the imaging process. Images of only the target part of the body are created through MRI radiological analysis. The physician chooses which part of the patient's body must be analyzed by imaging, to diagnose the illness of the patient [ 68 ].

14. Applications of MRI

A major application of whole-body MRI is to investigate skeletal metastases. The MRI approach allows for visualization of the tumor because the tumor matrix contains an abundance of the proton. It is a more sensitive imaging technique than skeletal scintigraphy (bone scan) in the measurement of skeletal metastases. The whole-body MRI technique is more effective for detecting lesions in the pelvis, spine, and femur. This technique is also highly used as a primary diagnostic tool for the measurement of soft tissue diseases, whole-body fat, and polymyositis disease [ 69 ].

MRI is different from other diagnostic techniques because MRI has no risk of ionizing radiation. MRI has no side effects unlike CT and PET scans. There is no loss of image quality due to the scanning of body target parts from several angles and viewpoints [ 70 ]. Dynamic contrast-enhanced magnetic resonance (DCE-MRI) has been developed for the detection of the tumor microenvironment and its treatment. It has been supported as a useful method and improved clinical interest [ 71 ].

An advantage of using MRI to diagnose cardiovascular diseases is that examination reveals function structure perfusion, metabolism, and blood flow in the heart. A cardiovascular MRI is a source for the detection of congenital cardiac diseases, abnormalities in the thoracic aorta, and pericardium in heart patients. During the detection of myocardial tumor or right ventricular dysplasia, tissues are differentiated due to varying imaging parameters of the MRI approach. Another application of MRI, cardiovascular MRI, is applicable for determining cardiac prognosis, ischemia in a patient with heart disease, artery arteriosclerosis, and screening the myocardial viability [ 72 ].

Schizophrenia patients show mental abnormality which leads to language processing deficits and abnormal social behavior. Functional MRI has been applicable for remarks of such types of illnesses. The region of hypoactivity can be determined in the frontotemporal cortex of the patient brain. Soft neurological signs and symptoms have also been promoted. Functional MRI can detect abnormalities in the cerebrum; cerebral asymmetry images reveal changes in patients with schizophrenia compared with control as shown in Figure 9 [ 73 ].

Abnormal cerebral asymmetry in schizophrenia patients compared with control is shown by functional-MRI imaging technique.

Microfluidic LOC (lab-on-a-chip) is a device used as an emerging technology in medical laboratories. Sample (consist of suspensions of cells) and reagents react on these devices. For monitoring reaction on LOC, MRI is considered an ideal tool. MRI records the signals from the expended fluid leavings in the device. MRI combined with MRS (magnetic resonance spectroscopy) monitors fluid flow processes, chemical reaction separations, and diffusion processes in LOC. But MRI and MRS both show low sensitivity. In the future, there is a hope that MRI will be applicable for the advancement of microfluidic LOCs with powerful usage in medical diagnostic libraries [ 74 ].

Mutation in BRCA1 and BRCA2 genes leads to losing their ability to repair the damaged DNA, causing cancer, especially breast cancer. MRI diagnoses breast cancer which is due to a genetic mutation. Mostly, these hidden breast cancers are not detected by mammography. For a decade, doctors use MRI imaging tools to detect breast cancer [ 75 ]. According to previous research, 27-37% of patients have shown lesions on MRI, which are not seen through mammography. The researchers noted that mammography had a low value of positive prediction of 52.8%, as compared to MRI which is high at 72.4% [ 74 ].

Molecular MRI employed for specific and early detection of pulmonary metastatic cancer cells can improve its treatment. In research, pulmonary cancer cells are besieged by iron oxide nanoparticles having the ability to bind with ligand expressed on the cells. Then, images were taken by high-resolution hyperpolarized 3 He MRI (HP 3 He MRI). The study confirmed that HP 3 He MRI pooled with targeted superparamagnetic iron oxide nanoparticle (SPION) contrast agent detects specific and early metastatic pulmonary cancer in mice. A researcher used the LHRH-SPION agent to explore new drug procedures. For this purpose, they injected breast adenocarcinoma cells into mice and then detect pulmonary defects as cancer formation in mouse lungs with the help of LHRH-SPIONs and HP 3 He MRI results are shown in Figure 10 [ 76 ].

Breast adenocarcinoma mouse model formed for the detection of lung metastases. (a) High-resolution hyperpolarized 3 He MRI (HP 3 He MRI) images were taken from the control mouse. Screening as normal ventilation forms of lungs. (b) After injection of LHRH-SPIONs, images were produced from human breast adenocarcinoma abnormal mouse (model), showing defects in the right lobe (under circles).

Multinuclear 3D solid-state MRI allows images of tooth bone and calcium phosphate components of bone substances. It also gives information on the bone composition and texture of the bone [ 77 ]. Recent neuroimaging techniques including high-resolution MRI can investigate myeloarchitectural patterns in the cortex of the human brain. The bands of myelination have been revealed by the staining technique. Now, the same band in good quality image form can also be obtained by high-resolution MRI imaging technique. Although the advanced technology has been largely applied in the visual system, further improved methodologies are required for the investigation of another brain region. To overcome high ratio of “signal” to “noise” is a challenge for MRI machines that is produced due to the increased resolution of the image [ 78 ]. In this way, fMRI and other types of MRI have been powerfully used to change our understanding of diseases, their causes, and how to manage the conditions.

15. Simultaneous Imaging with MRI and PET

In in vivo study, imaging of small animals, for example, mouse imaging by combined MRI and PET modalities, produces constant information of different parts of the body. An experimental study reveals that the combined PET/MRI technique improves the understanding of malignant tissues and heterogeneous tumors, edema, and necrosis that are not done by MRI alone. Particular ionic 18 F is also used for PET/MRI combined imaging of small animals as models of bone metastasis, osteoporosis, and arthritis to study the complete skeletal system [ 79 ].

16. Risks of Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is highly expensive, low in sensitivity, and time-consuming for scanning and processing compared to other imaging modalities. A probe with a bulk quantity may be needed for MRI. It cannot detect abnormalities of intraluminal body parts. It gives no real-time information. And it can create a suffocating environment for some people [ 2 ].

17. Single-Photon Emission Computed Tomography (SPECT)

Single-photon emission computed tomography (SPECT) is an advanced imaging technique using gamma rays and provides three-dimensional (3D) representations of objects with high accuracy. In 1963, Kuhl and Edwards [ 80 ] gave the first report about single positron emission computed tomography. Gradually, modification with new instruments such as computer-attached systems and rotating gamma cameras leads to the development of a novel modality of single-photon emission tomography.

SPECT has become a great medical imaging technique used in research and clinical area. A dual-headed single-photon emission tomography (SPECT) system has been shown in Figure 11 [ 81 ]. It monitors the 3D information of an object by producing series of thin slices from tomographic images. These essential tomographic images can improve the ability detection of deep and very small fractures in patients [ 81 ].

Dual-headed single-photon emission computed tomography (SPECT) system.

SPECT assesses the multiple two-dimensional (2D) images from different angles by using high-energy gamma rays. Data is reconstructed and recorded, and 3D images of the target portion of the body are produced by a computer program. SPECT is greatly used in clinics and research laboratories like other tomographic modalities such as PET, MRI, and CT. SPECT and PET both use the radioactive tracer and then measure the emitted gamma rays. In the case of SPECT, emitted gamma rays by radioactive tracers are directly detected by the detector. The computer system analyzes the data from the detector and produces the true image of the area where the radioactive tracers are injected. SPECT imaging technique is less expensive than exclusively used for imaging small animals. It is sensitive to monitoring target bone metabolism, myocardial disorders, and blood flow in the cerebrum [ 82 ].

SPECT has also been designed for imaging of the brain known as neurochemical brain imaging. It has a powerful imaging technique to elaborate the neuropsychiatric diseases. It is an essential developmental technique that has great potential to monitor the pathophysiology and many other complex disorders of the brain [ 83 ].

Today, hybrid SPECT and CT are progressively employed and available in the nuclear medicine field. SPECT/CT provides exact abnormal bone turnover during inflammation, bone tumor, bone regeneration, bone infection, and trauma in complex bone joints such as the knee, hip, foot, shoulder, and hand/wrist. In most cases, CT with specificity and SPECT with high sensitivity are performed together for a complete diagnosis. SPECT/CT is also responsible for giving the proper information about therapy planning. For example, it tells us about the decision of using SPECT/CT alone or joint arthroplasty. It was observed that joint arthrography of the knee gives better results as compared to SPECT/CT alone as shown in Figure 12 [ 84 ].

(a) The SPECT/CT scan of the knee joint shows clear articular cartilage. (b) The SPECT/CT arthrography image of the knee joint showing enhance the value of SPECT/CT screening.

SPECT/CT technique has great potential for the measurement of articular cartilage, soft bodies, and synovial structures. It also gives promising results in the examination of osteochondral abnormalities [ 84 ].

18. Risks of Single-Photon Emission Computed Tomography (SPECT)

Single-photon emission computed tomography (SPECT) is expensive and requires additional care for performance with radioactive materials. Like PET, it uses ionizing radiation and creates radiation side effect for the patients [ 2 ].

19. Digital Mammography

Mammography is a technique used to screen and diagnose human breasts. Low-energy X-rays (30 kVp) are used in the mammography in performing early diagnosis and screening of human breast cancer. Ionizing radiations are used in mammograms to create images for analysis of abnormal conditions. Ultrasound is usually employed to give additional information about masses, detected by mammography. MRI, discography, and positron emission mammography (PEM) are also supporters of mammography. Mammography is more accurate for women 50 or above 50 years old as compared to younger women because old women have high breast density [ 85 ]. Today, conventional mammography has been replaced by digital mammography.

An advanced technique is used for creating 3D images of breast tissues for detailed analysis of breast cancer, known as 3D mammography. When 3D mammography is used along with usual mammography, it gives more positive results [ 86 ]. Cost-effectiveness and high radiation exposure are of high concern to 3D mammography [ 87 ].

Digital mammography is a special form of mammography employed to investigate breast tissues for breast tumor study. Digital mammography contrasts with film (conventional) mammography by using a special detector that detects the transmitted X-rays energy and converts it into an image signal by a computer system instead of a film X-ray. Digital mammography is a rapid and advanced modality that has the potential for diagnosis and proper screening of breast cancer. Such new diffusion technologies can alter the health care pattern through many mechanisms. There are different results related to breast health care for digital-screen and film mammography. Digital mammography may also change the application of diagnostic services ensuring mammograms with positive screening [ 88 ].

Digital mammography has been considered a better technique as compared to film mammography in the detection of breast cancer in premenopausal, premenopausal, and young women. A digital system has more cost (approximately 1.5 to 4 times) than a film system. Digital mammography has the advantage of diagnosis in computer-based system that generates images with easy access and better-quality transmission, recovery, and image storage. Advanced digital mammography uses an average low dose of radiation without cooperation with diagnostic accuracy [ 89 ].

A healthy breast tissue mammogram recorded by digital mammography is clear as compared to a mammogram on film as shown in Figure 13 [ 90 ]. Scientists used both digital mammography and film mammography for 42,760 women's breast X-rays. Cancer is almost equally well detected through these techniques. But digital mammography detected 28% more breast cancer in younger women or those under 50, who have dense breast tissues. Digital mammography uses a specific detector that captures the transmitted X-rays and sends the information in the form of energy that converts into an image through a computer system [ 90 ]. Digital mammography screening for breast cancer is not cost-effective, relative to conventional mammography [ 91 ].

(a) A healthy breast tissue mammogram recorded by digital mammogram; (b) the same breast tissue mammogram recorded by film mammography. White spots shown in the above images are deposits of calcium which can consider the mark of cancer when they form clusters.

Now, screening MRI is highly used as an adjunct to mammography, recommended for women to ensure 20-25% or more threat of breast cancer [ 92 ]. Currently, breast cancer diagnostic programs have been recognized widely at least in 22 countries. Collective struggles lead to the formation of the Breast Cancer Screening Network (IBSN), an international program for policymaking, administration, funding, and handling of results that come from breast cancer screening of huge populations [ 93 ]. The ultrasound technology combined with mammography is used to detect elevated risk factors for breast cancer. According to research on elevated risk factors, by using ultrasound with mammography, more than 90% of cancer risk was seen in over 50% of women having dense breast tissues, and 25% of cancer risk factors were determined in women having just 26%-40% dense tissues of the breast. It is suggested that screening with ultrasound may be beneficial for those women having other risk factors and less dense breast tissues [ 94 ].

20. Medical Ultrasound

The ultrasound imaging technology was used earlier as a diagnostic tool for brain images. Today, ultrasound is a widespread imaging technology used in diagnostic laboratories and clinics. It is free from radiation exposure risk, comparatively less expensive, and highly portable as compared to other imaging techniques like MRI and CT [ 95 ]. This system is used in different fields. In the medical field, ultrasound uses sound waves of high frequency, to diagnose the organs and structure of the body. The ultrasound system is performed with high frequency. Special technicians or doctors use it to observe the kidney, heart, liver, blood vessels, and other organs of the body. The most critical component of ultrasound is a transducer. An ultrasound transducer can convert an electrical signal into sound waves and sound waves into an electrical signal. An ultrasonic image or sonogram is formed by transmitting pulses of sound waves into tissues using a special probe. Different tissues reflect these sounds to a different degree. These reflected sound waves (echoes) are detected and presented as an image with the help of the operator. In the medical field, there are many applications of ultrasound. Ultrasound scanning is a very effective and reliable technique that is greatly used for monitoring normal pregnancy, placenta previa, multiple pregnancies, and different abnormalities during pregnancy and rest [ 96 ].

An ultrasound imaging technique also known as transvaginal ultrasound (TVS) alters our understanding of the management and diagnosis of pregnancy. The TVS gives clear knowledge of early pregnancy problems. It investigates the pregnancy location as well as viability. In utero , the TVs have determined the fetal heart activity that is initial proof of pregnancy viability. Abnormal development in fetal heart rate pattern indicates the subsequent miscarriage. Less fetal heart rate especially at 6 to 8 weeks demonstrates subsequent fetal disease. Fetal heart pulsation can be seen on TVS. The routine use of TVS also leads to development in managing early pregnancy failure. Awareness of pregnant women and improvement in early pregnancy units can directly manage miscarriage. TVS is a very sensitive approach for diagnosing early miscarriage. It detects trophoblastic tissue and blood flow in the intervillous space, and the use of color Doppler images leads to estimate the level of expectant management [ 97 ].

Functional ultrasound (fUS) is highly applicable for imaging the brain and detects transient alternation of blood volume in the brain at high resolution than other brain imaging modalities. The blood volume in small vessels can be measured by functional ultrasound (fUS), which uses plane-wave illumination with a high frame rate. Functional ultrasound can detect the brain's active portion [ 98 ]. Ultrasound has major advantages as noninvasive and out-patient scanning in children to investigate neuromuscular disorders. Ultrasonography has been used for finding the normal function of muscle, muscle contraction, muscle thickness, and muscle fiber length. Real-time ultrasound is used for muscle imaging. When ultrasound applies to neuromuscular patients, different muscle disorders can be detected by a pattern of muscle echo, such as a bright spotted pattern of increase in muscle echo obtained in muscular dystrophy patients and a moderate increase in echo showed in spinal muscular atrophy [ 99 ].

Currently, early care physicians can easily understand complex patient conditions with the help of advanced 3D ultrasound algorithms. And high-speed networks are used in special health centers to enhance patient care facilities [ 100 ]. 3D ultrasound is widely used due to the reason of 2D ultrasound limitations. Clinical 3D ultrasound experience has an advantage in the diagnosis of disorders and produces a 3D image that guides invasive therapy. Further improvement in 3D imaging software, as well as hardware, will lead to routine usage of this tool [ 101 ].

High-resolution ultrasound which is also known as ultrasound biomicroscopy (UBM) has clinical applications in imaging the human eye. UBM uses 35 MHz or above frequencies to provide images of high resolution as compared to conventional ophthalmic ultrasound techniques. UBM can be used to diagnose ocular trauma and complex hypotony. It can determine eye lens displacement, iridodialysis, zonular flaw, cataract, lens subluxation, and hyphemia. Hyphemia is a condition in which blood diffuses the anterior chamber of the eye due to injury. Hyphemia scan image by Sonomed UBM is shown in Figure 14 [ 102 ].

(a) Hyphemia is due to injury; blood diffuses the anterior chamber of the eye. (b) Hyphemia scan image by Sonomed UBM.

UBM can detect several eye abnormalities, as it allows for the diagnosis of trauma, glaucoma, and foreign bodies. It can evaluate eyelids, neoplasms, normal eye anatomy, and extraocular muscles during strabismus surgery. Currently, for high-resolution diagnostic eye imaging, new advanced technologies such as pulse encoding, transducer array, and ultrasound combination with light are developed [ 102 ].

A high-frequency (40 MHz) ultrasound imaging technique has been developed for checking the programmed cell death process known as apoptosis. It is a noninvasive procedure used to monitor the apoptosis process that happens because of agents especially anticancer agents in cells of body tissue in vitro or in vivo. The procedure of detection monitored alternations in subcellular nuclear such as the condensation process after proper destruction of the cell during the programmed cell death process. The high-frequency ultrasound technique shows a high scattering rate due to these intense alternations (approximately 25-50-fold) in apoptotic cells as compared to normal tissue cells. As a result, the apoptotic tissues show greatly brighter areas as compared to normal tissue. In the future, this noninvasive imaging technique will use to check the effect of anticancer treatment and chemotherapeutic agents in laboratory model systems and then in patients [ 103 ].

Another application of ultrasound technology is the successful detection of renal masses. According to research results, 86% carcinomas and 98% renal cysts were accurately determined among 111 patients by the ultrasound imaging technique. Ultrasound is a safe, simple, and cheap diagnostic tool to diagnose complex renal masses [ 104 ]. Ultrasound screening allows imaging the cartilage for checking instability, abnormal location of the femoral head inside the acetabulum, and developmental dysplasia in newborns [ 105 ]. A powerful low-frequency ultrasound system can also be used as a noninvasively drug delivery system. Many drugs and proteins having high molecular weight can be delivered easily with excessive permeability into human skin with the help of a low-frequency ultrasound modality. For example, insulin, erythropoietin, and interferon-gamma molecules are easily and safely delivered across human skin [ 106 ].

A novel molecular imaging technique known as molecular ultrasound has been used by researchers in the molecular biology field to monitor the alternation in the expression rate of molecular markers located on intravascular targets. Contrast agents used in the advanced molecular ultrasound imaging technology are mostly micro- or nanosized particles having ligands on the surface also known as microbubbles. Specific molecular markers are targeted by these microbubbles such as selectin, vascular cell adhesion molecule 1, and integrin. In this way, these agents lead to detect specific molecular markers on intravenous targets by gathering at that specific tissue site.

Molecular ultrasound has many advantages such as low-effective cost, high resolution, portable, noninvasiveness, absence of ionizing radiation, real-time imaging potential, and high availability. It has the potential for regular investigations of different abnormalities at the molecular level, such as inflammation, tumor angiogenesis, and thrombus. In addition, improvement is still required in the field of molecular ultrasound to design the novel targeting ligand to form a more effective contrast agent. In the future, advancements in molecular ultrasound imaging technology will play a clinical role with high sensitivity and accuracy for imaging complex abnormalities at the molecular level [ 107 , 108 ].

21. Disadvantages of Medical Ultrasound

There are many useful applications of ultrasound in the medical field. But medical ultrasonography also had some side effects such as hormone change effect, breakage of chromosomes with very low frequency, chemical effects, and other health problems. It was examined that for 10-week gestation, chorionic gonadotropin hormone in humans increased after routine usage of ultrasound modality [ 109 ].

It was observed in an experiment that no chromosome damage occurred after diagnostic ultrasound exposure to human lymphocyte culture. But experimental results suggest that ultrasound can cause chromosome breakage with very low frequency [ 110 ]. Similarly, lots of experiments have been done to check fetal ultrasound safety. In vivo study demonstrates no major neurological defects, fetal growth problems, birth flaws, or childhood cancer caused by ultrasound imaging. But in vitro study demonstrates the possibility of some health problems that can occur due to diagnostic ultrasonography. For example, the effect of diagnostic ultrasound on the normal architecture of the mouse fibroblast cell with the production of fingerlike projections on the fine surface of the cell is demonstrated in Figure 15 [ 111 ].

Images of mouse fibroblast cell (a) normal smooth form and (b) abnormal cell in its rough shape. Due to the diagnostic ultrasound effect, fingerlike projections are produced on the smooth surface of the fibroblast cell.

Another disadvantage of an ultrasound system in the medical field is generating significant heat. When strong ultrasound waves migrate through the liquid environment, small cavities are produced, and these cavities expand, then collapse to each other, and generate heat. This heat-generating condition leads to create an unnecessary chemical environment [ 112 ]. Dyslexia, growth limitation, non-right handedness, and late speech-like effects are also examined after diagnostic ultrasound exposure. Continuous research is required to find the side effects of medical ultrasonography on human health [ 113 ].

22. Radiation Exposure Risk from Medical Imaging and Its Management

Radiological imaging by X-ray radiology, positron emission tomography (PET), single-photon emission computed tomography (SPECT), mammography, and computed tomography (CT) uses high-energy X-rays that leads to high radiation dose in some patients. Pregnant women and children are mostly affected by radiation exposure from radiological imaging. Possibility of cancer, genetic mutations, growth and developmental retardation in the fetus, and cardiovascular abnormalities can occur by the exposure to radiation after radiotherapy. Direct radiation exposure can cause hair loss, cataracts, skin redness, and skin damage. Radiation exposure risks can be reduced by making “National Guidelines” that aid the physician to manage their effects on patients.

Many online tools have been developed to enable physicians or technicians to record the calculation of radiation exposure from each radiological imaging technique. For this purpose, a Thermoluminescent dosimeter (TLD) is used to calculate radiation dose through different software depending on the modality being used such as Monte Carlo PENRADIO which is used for CT [ 114 ]. Magnetic resonance imaging (MRI) and ultrasound techniques are free from ionizing radiation. To minimize the risk, MRI and ultrasound can be used instead of radiological imaging techniques. Reduction in unnecessary computed tomography screening leads to the direct reduction of radiation exposure risks. Today, advanced and safe technologies are used that allow measuring signals with a low dose of radiation; e.g., low-dose computed tomography scanners permit less radiation exposure [ 115 ].

23. Advanced Machine Medical Image Analysis

4D medical imaging analysis is an advanced technology that is used in combination with different modalities such as 4D CT, 4D US, and 4D MRI. 4D CT is an excellent choice for radiation oncology, which is prone to motion artifacts. Similarly, 4D ultrasound is particularly useful in prenatal research. 4D flow MRI can help doctors diagnose and treat heart issues more precisely. For big data integration in medical imaging, researchers need to develop algorithms to store images by converting them into numerical format which will be helpful for physicians in diagnosis.

24. Artificial Intelligence in Medical Imaging

Machine learning and deep learning are branches of artificial intelligence (AI) that solves problems in medical imaging applications such computer-aided diagnosis, lesion segmentation, medical image analysis, image-guided treatment, annotation, and retrieval. AI assesses image quality, interprets image, and analyzes biomarkers, and finally, reporting is done. AI has impact on oncologic imaging. Lung cancer is one of the most prevalent and severe tumors in thoracic imaging. AI can assist in recognizing and classifying these nodules as benign or cancerous [ 116 ]. Machine learning focuses on pattern recognition. The traditional AI systems relied on preset engineering feature algorithms with specified parameters based on expert knowledge. Such features were intended to assess certain radiographic characteristics such as a tumor's 3D shape and intratumoral texture. Following that, a selection process ensures that only the most important features are used. The data is then fed into statistical machine learning models to find potential imaging-based biomarkers [ 117 ]. Deep learning algorithms learn by navigating the data space and by providing them with greater problem-solving capabilities. Convolutional neural networks (CNNs) are the most used deep learning architectural typologies in medical imaging today, even though many deep learning designs have been researched to handle diverse objectives [ 118 ].

25. Critical Analysis

Medical imaging often contains several techniques that are noninvasive to make images of different parts of the body. New imaging techniques such as computed tomography (CT), positron emission computed tomography (PET), magnetic resonance imaging (MRI), single-photon emission computed tomography (SPECT), ultrasound (US), and digital mammography reveal the internal anatomy and physiology of the body [ 2 ].

Advanced imaging technologies are used to diagnose various external as well as internal human illnesses that can also minimize diagnostic errors and produce novel and better information about the target object. There are benefits and risks to every imaging technique. Ultrasound is also employed in the medical field to look at the kidneys, heart, liver, blood vessels, and other organs of the body [ 95 ]. Computed tomography (CT) measures cancer and various abnormalities in the heart, abdomen, bone, and spinal cord with high resolution [ 33 ]. 3D ultrasound computed tomography (3D USCT) is a promising technology for the investigation of breast cancer [ 48 ]. PET is a powerful technique to visualize, characterize, and quantify the biological processes and pathological changes at the cellular and subcellular levels within a living body. The development of MRI is now employed to examine several musculoskeletal, neurologic problems, and cancer. It can be used for both soft and hard tissues [ 1 ]. Digital mammography is a rapid and computer-based modality. It is used for the diagnosis and screening of breast cancer [ 89 ].

Some imaging techniques, such as CT, PET, SPECT, and digital mammography using X-rays, lead to high ionizing radiation exposure risk in some patients. There are some management steps for minimizing the radiation exposure risks from imaging techniques [ 114 ]. The development in medical imaging techniques such as the use of PET/CT hybrid, SPECT/CT hybrid, 3D USCT, and simultaneous PET/MRI leads to an increase in our understanding of diseases and their treatment [ 84 ]. With advanced medical imaging techniques, detection of early-stage diseases is possible and then eventually aids patients to live longer and better lives. In the future, with mounting innovations and advancements in technology systems, the medical diagnostic field would become a field of regular measurement of various complex diseases and will provide healthcare solutions [ 1 ].

Acknowledgments

The authors wish to thank Research Center College of Pharmacy at King Saud University, Riyadh, Saudi Arabia, for their financial support and for providing free access to digital library and laboratory.

Abbreviations

Data availability, conflicts of interest.

The authors declare no conflicts of interest.

Authors' Contributions

All authors contributed equally.