clinical studies in research methods

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Understanding Clinical Trials

Clinical research: what is it.

Your doctor may have said that you are eligible for a clinical trial, or you may have seen an ad for a clinical research study. What is clinical research, and is it right for you?

Clinical research is the comprehensive study of the safety and effectiveness of the most promising advances in patient care. Clinical research is different than laboratory research. It involves people who volunteer to help us better understand medicine and health. Lab research generally does not involve people — although it helps us learn which new ideas may help people.

Every drug, device, tool, diagnostic test, technique and technology used in medicine today was once tested in volunteers who took part in clinical research studies.

At Johns Hopkins Medicine, we believe that clinical research is key to improve care for people in our community and around the world. Once you understand more about clinical research, you may appreciate why it’s important to participate — for yourself and the community.

What Are the Types of Clinical Research?

There are two main kinds of clinical research:

Observational Studies

Observational studies are studies that aim to identify and analyze patterns in medical data or in biological samples, such as tissue or blood provided by study participants.

Clinical Trials

Clinical trials, which are also called interventional studies, test the safety and effectiveness of medical interventions — such as medications, procedures and tools — in living people.

Clinical research studies need people of every age, health status, race, gender, ethnicity and cultural background to participate. This will increase the chances that scientists and clinicians will develop treatments and procedures that are likely to be safe and work well in all people. Potential volunteers are carefully screened to ensure that they meet all of the requirements for any study before they begin. Most of the reasons people are not included in studies is because of concerns about safety.

Both healthy people and those with diagnosed medical conditions can take part in clinical research. Participation is always completely voluntary, and participants can leave a study at any time for any reason.

“The only way medical advancements can be made is if people volunteer to participate in clinical research. The research participant is just as necessary as the researcher in this partnership to advance health care.” Liz Martinez, Johns Hopkins Medicine Research Participant Advocate

Types of Research Studies

Within the two main kinds of clinical research, there are many types of studies. They vary based on the study goals, participants and other factors.

Biospecimen studies

Healthy volunteer studies.

Clinical trials study the safety and effectiveness of interventions and procedures on people’s health. Interventions may include medications, radiation, foods or behaviors, such as exercise. Usually, the treatments in clinical trials are studied in a laboratory and sometimes in animals before they are studied in humans. The goal of clinical trials is to find new and better ways of preventing, diagnosing and treating disease. They are used to test:

Drugs or medicines

New types of surgery

Medical devices

New ways of using current treatments

New ways of changing health behaviors

New ways to improve quality of life for sick patients

 Goals of Clinical Trials

Because every clinical trial is designed to answer one or more medical questions, different trials have different goals. Those goals include:

Treatment trials

Prevention trials, screening trials, phases of a clinical trial.

In general, a new drug needs to go through a series of four types of clinical trials. This helps researchers show that the medication is safe and effective. As a study moves through each phase, researchers learn more about a medication, including its risks and benefits.

Is the medication safe and what is the right dose?   Phase one trials involve small numbers of participants, often normal volunteers.

Does the new medication work and what are the side effects?   Phase two trials test the treatment or procedure on a larger number of participants. These participants usually have the condition or disease that the treatment is intended to remedy.

Is the new medication more effective than existing treatments?  Phase three trials have even more people enrolled. Some may get a placebo (a substance that has no medical effect) or an already approved treatment, so that the new medication can be compared to that treatment.

Is the new medication effective and safe over the long term?   Phase four happens after the treatment or procedure has been approved. Information about patients who are receiving the treatment is gathered and studied to see if any new information is seen when given to a large number of patients.

“Johns Hopkins has a comprehensive system overseeing research that is audited by the FDA and the Association for Accreditation of Human Research Protection Programs to make certain all research participants voluntarily agreed to join a study and their safety was maximized.” Gail Daumit, M.D., M.H.S., Vice Dean for Clinical Investigation, Johns Hopkins University School of Medicine

Is It Safe to Participate in Clinical Research?

There are several steps in place to protect volunteers who take part in clinical research studies. Clinical Research is regulated by the federal government. In addition, the institutional review board (IRB) and Human Subjects Research Protection Program at each study location have many safeguards built in to each study to protect the safety and privacy of participants.

Clinical researchers are required by law to follow the safety rules outlined by each study's protocol. A protocol is a detailed plan of what researchers will do in during the study.

In the U.S., every study site's IRB — which is made up of both medical experts and members of the general public — must approve all clinical research. IRB members also review plans for all clinical studies. And, they make sure that research participants are protected from as much risk as possible.

Earning Your Trust

This was not always the case. Many people of color are wary of joining clinical research because of previous poor treatment of underrepresented minorities throughout the U.S. This includes medical research performed on enslaved people without their consent, or not giving treatment to Black men who participated in the Tuskegee Study of Untreated Syphilis in the Negro Male. Since the 1970s, numerous regulations have been in place to protect the rights of study participants.

Many clinical research studies are also supervised by a data and safety monitoring committee. This is a group made up of experts in the area being studied. These biomedical professionals regularly monitor clinical studies as they progress. If they discover or suspect any problems with a study, they immediately stop the trial. In addition, Johns Hopkins Medicine’s Research Participant Advocacy Group focuses on improving the experience of people who participate in clinical research.

Clinical research participants with concerns about anything related to the study they are taking part in should contact Johns Hopkins Medicine’s IRB or our Research Participant Advocacy Group .

Learn More About Clinical Research at Johns Hopkins Medicine

For information about clinical trial opportunities at Johns Hopkins Medicine, visit our trials site.

• Open access
• Published: 07 September 2020

A tutorial on methodological studies: the what, when, how and why

• Lawrence Mbuagbaw   ORCID: orcid.org/0000-0001-5855-5461 1 , 2 , 3 ,
• Daeria O. Lawson 1 ,
• Livia Puljak 4 ,
• David B. Allison 5 &
• Lehana Thabane 1 , 2 , 6 , 7 , 8  

BMC Medical Research Methodology volume  20 , Article number:  226 ( 2020 ) Cite this article

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Methodological studies – studies that evaluate the design, analysis or reporting of other research-related reports – play an important role in health research. They help to highlight issues in the conduct of research with the aim of improving health research methodology, and ultimately reducing research waste.

We provide an overview of some of the key aspects of methodological studies such as what they are, and when, how and why they are done. We adopt a “frequently asked questions” format to facilitate reading this paper and provide multiple examples to help guide researchers interested in conducting methodological studies. Some of the topics addressed include: is it necessary to publish a study protocol? How to select relevant research reports and databases for a methodological study? What approaches to data extraction and statistical analysis should be considered when conducting a methodological study? What are potential threats to validity and is there a way to appraise the quality of methodological studies?

Appropriate reflection and application of basic principles of epidemiology and biostatistics are required in the design and analysis of methodological studies. This paper provides an introduction for further discussion about the conduct of methodological studies.

Peer Review reports

The field of meta-research (or research-on-research) has proliferated in recent years in response to issues with research quality and conduct [ 1 , 2 , 3 ]. As the name suggests, this field targets issues with research design, conduct, analysis and reporting. Various types of research reports are often examined as the unit of analysis in these studies (e.g. abstracts, full manuscripts, trial registry entries). Like many other novel fields of research, meta-research has seen a proliferation of use before the development of reporting guidance. For example, this was the case with randomized trials for which risk of bias tools and reporting guidelines were only developed much later – after many trials had been published and noted to have limitations [ 4 , 5 ]; and for systematic reviews as well [ 6 , 7 , 8 ]. However, in the absence of formal guidance, studies that report on research differ substantially in how they are named, conducted and reported [ 9 , 10 ]. This creates challenges in identifying, summarizing and comparing them. In this tutorial paper, we will use the term methodological study to refer to any study that reports on the design, conduct, analysis or reporting of primary or secondary research-related reports (such as trial registry entries and conference abstracts).

In the past 10 years, there has been an increase in the use of terms related to methodological studies (based on records retrieved with a keyword search [in the title and abstract] for “methodological review” and “meta-epidemiological study” in PubMed up to December 2019), suggesting that these studies may be appearing more frequently in the literature. See Fig.  1 .

Trends in the number studies that mention “methodological review” or “meta-

epidemiological study” in PubMed.

The methods used in many methodological studies have been borrowed from systematic and scoping reviews. This practice has influenced the direction of the field, with many methodological studies including searches of electronic databases, screening of records, duplicate data extraction and assessments of risk of bias in the included studies. However, the research questions posed in methodological studies do not always require the approaches listed above, and guidance is needed on when and how to apply these methods to a methodological study. Even though methodological studies can be conducted on qualitative or mixed methods research, this paper focuses on and draws examples exclusively from quantitative research.

The objectives of this paper are to provide some insights on how to conduct methodological studies so that there is greater consistency between the research questions posed, and the design, analysis and reporting of findings. We provide multiple examples to illustrate concepts and a proposed framework for categorizing methodological studies in quantitative research.

What is a methodological study?

Any study that describes or analyzes methods (design, conduct, analysis or reporting) in published (or unpublished) literature is a methodological study. Consequently, the scope of methodological studies is quite extensive and includes, but is not limited to, topics as diverse as: research question formulation [ 11 ]; adherence to reporting guidelines [ 12 , 13 , 14 ] and consistency in reporting [ 15 ]; approaches to study analysis [ 16 ]; investigating the credibility of analyses [ 17 ]; and studies that synthesize these methodological studies [ 18 ]. While the nomenclature of methodological studies is not uniform, the intents and purposes of these studies remain fairly consistent – to describe or analyze methods in primary or secondary studies. As such, methodological studies may also be classified as a subtype of observational studies.

Parallel to this are experimental studies that compare different methods. Even though they play an important role in informing optimal research methods, experimental methodological studies are beyond the scope of this paper. Examples of such studies include the randomized trials by Buscemi et al., comparing single data extraction to double data extraction [ 19 ], and Carrasco-Labra et al., comparing approaches to presenting findings in Grading of Recommendations, Assessment, Development and Evaluations (GRADE) summary of findings tables [ 20 ]. In these studies, the unit of analysis is the person or groups of individuals applying the methods. We also direct readers to the Studies Within a Trial (SWAT) and Studies Within a Review (SWAR) programme operated through the Hub for Trials Methodology Research, for further reading as a potential useful resource for these types of experimental studies [ 21 ]. Lastly, this paper is not meant to inform the conduct of research using computational simulation and mathematical modeling for which some guidance already exists [ 22 ], or studies on the development of methods using consensus-based approaches.

When should we conduct a methodological study?

Methodological studies occupy a unique niche in health research that allows them to inform methodological advances. Methodological studies should also be conducted as pre-cursors to reporting guideline development, as they provide an opportunity to understand current practices, and help to identify the need for guidance and gaps in methodological or reporting quality. For example, the development of the popular Preferred Reporting Items of Systematic reviews and Meta-Analyses (PRISMA) guidelines were preceded by methodological studies identifying poor reporting practices [ 23 , 24 ]. In these instances, after the reporting guidelines are published, methodological studies can also be used to monitor uptake of the guidelines.

These studies can also be conducted to inform the state of the art for design, analysis and reporting practices across different types of health research fields, with the aim of improving research practices, and preventing or reducing research waste. For example, Samaan et al. conducted a scoping review of adherence to different reporting guidelines in health care literature [ 18 ]. Methodological studies can also be used to determine the factors associated with reporting practices. For example, Abbade et al. investigated journal characteristics associated with the use of the Participants, Intervention, Comparison, Outcome, Timeframe (PICOT) format in framing research questions in trials of venous ulcer disease [ 11 ].

How often are methodological studies conducted?

There is no clear answer to this question. Based on a search of PubMed, the use of related terms (“methodological review” and “meta-epidemiological study”) – and therefore, the number of methodological studies – is on the rise. However, many other terms are used to describe methodological studies. There are also many studies that explore design, conduct, analysis or reporting of research reports, but that do not use any specific terms to describe or label their study design in terms of “methodology”. This diversity in nomenclature makes a census of methodological studies elusive. Appropriate terminology and key words for methodological studies are needed to facilitate improved accessibility for end-users.

Why do we conduct methodological studies?

Methodological studies provide information on the design, conduct, analysis or reporting of primary and secondary research and can be used to appraise quality, quantity, completeness, accuracy and consistency of health research. These issues can be explored in specific fields, journals, databases, geographical regions and time periods. For example, Areia et al. explored the quality of reporting of endoscopic diagnostic studies in gastroenterology [ 25 ]; Knol et al. investigated the reporting of p -values in baseline tables in randomized trial published in high impact journals [ 26 ]; Chen et al. describe adherence to the Consolidated Standards of Reporting Trials (CONSORT) statement in Chinese Journals [ 27 ]; and Hopewell et al. describe the effect of editors’ implementation of CONSORT guidelines on reporting of abstracts over time [ 28 ]. Methodological studies provide useful information to researchers, clinicians, editors, publishers and users of health literature. As a result, these studies have been at the cornerstone of important methodological developments in the past two decades and have informed the development of many health research guidelines including the highly cited CONSORT statement [ 5 ].

Where can we find methodological studies?

Methodological studies can be found in most common biomedical bibliographic databases (e.g. Embase, MEDLINE, PubMed, Web of Science). However, the biggest caveat is that methodological studies are hard to identify in the literature due to the wide variety of names used and the lack of comprehensive databases dedicated to them. A handful can be found in the Cochrane Library as “Cochrane Methodology Reviews”, but these studies only cover methodological issues related to systematic reviews. Previous attempts to catalogue all empirical studies of methods used in reviews were abandoned 10 years ago [ 29 ]. In other databases, a variety of search terms may be applied with different levels of sensitivity and specificity.

Some frequently asked questions about methodological studies

In this section, we have outlined responses to questions that might help inform the conduct of methodological studies.

Q: How should I select research reports for my methodological study?

A: Selection of research reports for a methodological study depends on the research question and eligibility criteria. Once a clear research question is set and the nature of literature one desires to review is known, one can then begin the selection process. Selection may begin with a broad search, especially if the eligibility criteria are not apparent. For example, a methodological study of Cochrane Reviews of HIV would not require a complex search as all eligible studies can easily be retrieved from the Cochrane Library after checking a few boxes [ 30 ]. On the other hand, a methodological study of subgroup analyses in trials of gastrointestinal oncology would require a search to find such trials, and further screening to identify trials that conducted a subgroup analysis [ 31 ].

The strategies used for identifying participants in observational studies can apply here. One may use a systematic search to identify all eligible studies. If the number of eligible studies is unmanageable, a random sample of articles can be expected to provide comparable results if it is sufficiently large [ 32 ]. For example, Wilson et al. used a random sample of trials from the Cochrane Stroke Group’s Trial Register to investigate completeness of reporting [ 33 ]. It is possible that a simple random sample would lead to underrepresentation of units (i.e. research reports) that are smaller in number. This is relevant if the investigators wish to compare multiple groups but have too few units in one group. In this case a stratified sample would help to create equal groups. For example, in a methodological study comparing Cochrane and non-Cochrane reviews, Kahale et al. drew random samples from both groups [ 34 ]. Alternatively, systematic or purposeful sampling strategies can be used and we encourage researchers to justify their selected approaches based on the study objective.

Q: How many databases should I search?

A: The number of databases one should search would depend on the approach to sampling, which can include targeting the entire “population” of interest or a sample of that population. If you are interested in including the entire target population for your research question, or drawing a random or systematic sample from it, then a comprehensive and exhaustive search for relevant articles is required. In this case, we recommend using systematic approaches for searching electronic databases (i.e. at least 2 databases with a replicable and time stamped search strategy). The results of your search will constitute a sampling frame from which eligible studies can be drawn.

Alternatively, if your approach to sampling is purposeful, then we recommend targeting the database(s) or data sources (e.g. journals, registries) that include the information you need. For example, if you are conducting a methodological study of high impact journals in plastic surgery and they are all indexed in PubMed, you likely do not need to search any other databases. You may also have a comprehensive list of all journals of interest and can approach your search using the journal names in your database search (or by accessing the journal archives directly from the journal’s website). Even though one could also search journals’ web pages directly, using a database such as PubMed has multiple advantages, such as the use of filters, so the search can be narrowed down to a certain period, or study types of interest. Furthermore, individual journals’ web sites may have different search functionalities, which do not necessarily yield a consistent output.

Q: Should I publish a protocol for my methodological study?

A: A protocol is a description of intended research methods. Currently, only protocols for clinical trials require registration [ 35 ]. Protocols for systematic reviews are encouraged but no formal recommendation exists. The scientific community welcomes the publication of protocols because they help protect against selective outcome reporting, the use of post hoc methodologies to embellish results, and to help avoid duplication of efforts [ 36 ]. While the latter two risks exist in methodological research, the negative consequences may be substantially less than for clinical outcomes. In a sample of 31 methodological studies, 7 (22.6%) referenced a published protocol [ 9 ]. In the Cochrane Library, there are 15 protocols for methodological reviews (21 July 2020). This suggests that publishing protocols for methodological studies is not uncommon.

Authors can consider publishing their study protocol in a scholarly journal as a manuscript. Advantages of such publication include obtaining peer-review feedback about the planned study, and easy retrieval by searching databases such as PubMed. The disadvantages in trying to publish protocols includes delays associated with manuscript handling and peer review, as well as costs, as few journals publish study protocols, and those journals mostly charge article-processing fees [ 37 ]. Authors who would like to make their protocol publicly available without publishing it in scholarly journals, could deposit their study protocols in publicly available repositories, such as the Open Science Framework ( https://osf.io/ ).

Q: How to appraise the quality of a methodological study?

A: To date, there is no published tool for appraising the risk of bias in a methodological study, but in principle, a methodological study could be considered as a type of observational study. Therefore, during conduct or appraisal, care should be taken to avoid the biases common in observational studies [ 38 ]. These biases include selection bias, comparability of groups, and ascertainment of exposure or outcome. In other words, to generate a representative sample, a comprehensive reproducible search may be necessary to build a sampling frame. Additionally, random sampling may be necessary to ensure that all the included research reports have the same probability of being selected, and the screening and selection processes should be transparent and reproducible. To ensure that the groups compared are similar in all characteristics, matching, random sampling or stratified sampling can be used. Statistical adjustments for between-group differences can also be applied at the analysis stage. Finally, duplicate data extraction can reduce errors in assessment of exposures or outcomes.

Q: Should I justify a sample size?

A: In all instances where one is not using the target population (i.e. the group to which inferences from the research report are directed) [ 39 ], a sample size justification is good practice. The sample size justification may take the form of a description of what is expected to be achieved with the number of articles selected, or a formal sample size estimation that outlines the number of articles required to answer the research question with a certain precision and power. Sample size justifications in methodological studies are reasonable in the following instances:

Comparing two groups

Determining a proportion, mean or another quantifier

Determining factors associated with an outcome using regression-based analyses

For example, El Dib et al. computed a sample size requirement for a methodological study of diagnostic strategies in randomized trials, based on a confidence interval approach [ 40 ].

Q: What should I call my study?

A: Other terms which have been used to describe/label methodological studies include “ methodological review ”, “methodological survey” , “meta-epidemiological study” , “systematic review” , “systematic survey”, “meta-research”, “research-on-research” and many others. We recommend that the study nomenclature be clear, unambiguous, informative and allow for appropriate indexing. Methodological study nomenclature that should be avoided includes “ systematic review” – as this will likely be confused with a systematic review of a clinical question. “ Systematic survey” may also lead to confusion about whether the survey was systematic (i.e. using a preplanned methodology) or a survey using “ systematic” sampling (i.e. a sampling approach using specific intervals to determine who is selected) [ 32 ]. Any of the above meanings of the words “ systematic” may be true for methodological studies and could be potentially misleading. “ Meta-epidemiological study” is ideal for indexing, but not very informative as it describes an entire field. The term “ review ” may point towards an appraisal or “review” of the design, conduct, analysis or reporting (or methodological components) of the targeted research reports, yet it has also been used to describe narrative reviews [ 41 , 42 ]. The term “ survey ” is also in line with the approaches used in many methodological studies [ 9 ], and would be indicative of the sampling procedures of this study design. However, in the absence of guidelines on nomenclature, the term “ methodological study ” is broad enough to capture most of the scenarios of such studies.

Q: Should I account for clustering in my methodological study?

A: Data from methodological studies are often clustered. For example, articles coming from a specific source may have different reporting standards (e.g. the Cochrane Library). Articles within the same journal may be similar due to editorial practices and policies, reporting requirements and endorsement of guidelines. There is emerging evidence that these are real concerns that should be accounted for in analyses [ 43 ]. Some cluster variables are described in the section: “ What variables are relevant to methodological studies?”

A variety of modelling approaches can be used to account for correlated data, including the use of marginal, fixed or mixed effects regression models with appropriate computation of standard errors [ 44 ]. For example, Kosa et al. used generalized estimation equations to account for correlation of articles within journals [ 15 ]. Not accounting for clustering could lead to incorrect p -values, unduly narrow confidence intervals, and biased estimates [ 45 ].

Q: Should I extract data in duplicate?

A: Yes. Duplicate data extraction takes more time but results in less errors [ 19 ]. Data extraction errors in turn affect the effect estimate [ 46 ], and therefore should be mitigated. Duplicate data extraction should be considered in the absence of other approaches to minimize extraction errors. However, much like systematic reviews, this area will likely see rapid new advances with machine learning and natural language processing technologies to support researchers with screening and data extraction [ 47 , 48 ]. However, experience plays an important role in the quality of extracted data and inexperienced extractors should be paired with experienced extractors [ 46 , 49 ].

Q: Should I assess the risk of bias of research reports included in my methodological study?

A : Risk of bias is most useful in determining the certainty that can be placed in the effect measure from a study. In methodological studies, risk of bias may not serve the purpose of determining the trustworthiness of results, as effect measures are often not the primary goal of methodological studies. Determining risk of bias in methodological studies is likely a practice borrowed from systematic review methodology, but whose intrinsic value is not obvious in methodological studies. When it is part of the research question, investigators often focus on one aspect of risk of bias. For example, Speich investigated how blinding was reported in surgical trials [ 50 ], and Abraha et al., investigated the application of intention-to-treat analyses in systematic reviews and trials [ 51 ].

Q: What variables are relevant to methodological studies?

A: There is empirical evidence that certain variables may inform the findings in a methodological study. We outline some of these and provide a brief overview below:

Country: Countries and regions differ in their research cultures, and the resources available to conduct research. Therefore, it is reasonable to believe that there may be differences in methodological features across countries. Methodological studies have reported loco-regional differences in reporting quality [ 52 , 53 ]. This may also be related to challenges non-English speakers face in publishing papers in English.

Authors’ expertise: The inclusion of authors with expertise in research methodology, biostatistics, and scientific writing is likely to influence the end-product. Oltean et al. found that among randomized trials in orthopaedic surgery, the use of analyses that accounted for clustering was more likely when specialists (e.g. statistician, epidemiologist or clinical trials methodologist) were included on the study team [ 54 ]. Fleming et al. found that including methodologists in the review team was associated with appropriate use of reporting guidelines [ 55 ].

Source of funding and conflicts of interest: Some studies have found that funded studies report better [ 56 , 57 ], while others do not [ 53 , 58 ]. The presence of funding would indicate the availability of resources deployed to ensure optimal design, conduct, analysis and reporting. However, the source of funding may introduce conflicts of interest and warrant assessment. For example, Kaiser et al. investigated the effect of industry funding on obesity or nutrition randomized trials and found that reporting quality was similar [ 59 ]. Thomas et al. looked at reporting quality of long-term weight loss trials and found that industry funded studies were better [ 60 ]. Kan et al. examined the association between industry funding and “positive trials” (trials reporting a significant intervention effect) and found that industry funding was highly predictive of a positive trial [ 61 ]. This finding is similar to that of a recent Cochrane Methodology Review by Hansen et al. [ 62 ]

Journal characteristics: Certain journals’ characteristics may influence the study design, analysis or reporting. Characteristics such as journal endorsement of guidelines [ 63 , 64 ], and Journal Impact Factor (JIF) have been shown to be associated with reporting [ 63 , 65 , 66 , 67 ].

Study size (sample size/number of sites): Some studies have shown that reporting is better in larger studies [ 53 , 56 , 58 ].

Year of publication: It is reasonable to assume that design, conduct, analysis and reporting of research will change over time. Many studies have demonstrated improvements in reporting over time or after the publication of reporting guidelines [ 68 , 69 ].

Type of intervention: In a methodological study of reporting quality of weight loss intervention studies, Thabane et al. found that trials of pharmacologic interventions were reported better than trials of non-pharmacologic interventions [ 70 ].

Interactions between variables: Complex interactions between the previously listed variables are possible. High income countries with more resources may be more likely to conduct larger studies and incorporate a variety of experts. Authors in certain countries may prefer certain journals, and journal endorsement of guidelines and editorial policies may change over time.

Q: Should I focus only on high impact journals?

A: Investigators may choose to investigate only high impact journals because they are more likely to influence practice and policy, or because they assume that methodological standards would be higher. However, the JIF may severely limit the scope of articles included and may skew the sample towards articles with positive findings. The generalizability and applicability of findings from a handful of journals must be examined carefully, especially since the JIF varies over time. Even among journals that are all “high impact”, variations exist in methodological standards.

Q: Can I conduct a methodological study of qualitative research?

A: Yes. Even though a lot of methodological research has been conducted in the quantitative research field, methodological studies of qualitative studies are feasible. Certain databases that catalogue qualitative research including the Cumulative Index to Nursing & Allied Health Literature (CINAHL) have defined subject headings that are specific to methodological research (e.g. “research methodology”). Alternatively, one could also conduct a qualitative methodological review; that is, use qualitative approaches to synthesize methodological issues in qualitative studies.

Q: What reporting guidelines should I use for my methodological study?

A: There is no guideline that covers the entire scope of methodological studies. One adaptation of the PRISMA guidelines has been published, which works well for studies that aim to use the entire target population of research reports [ 71 ]. However, it is not widely used (40 citations in 2 years as of 09 December 2019), and methodological studies that are designed as cross-sectional or before-after studies require a more fit-for purpose guideline. A more encompassing reporting guideline for a broad range of methodological studies is currently under development [ 72 ]. However, in the absence of formal guidance, the requirements for scientific reporting should be respected, and authors of methodological studies should focus on transparency and reproducibility.

Q: What are the potential threats to validity and how can I avoid them?

A: Methodological studies may be compromised by a lack of internal or external validity. The main threats to internal validity in methodological studies are selection and confounding bias. Investigators must ensure that the methods used to select articles does not make them differ systematically from the set of articles to which they would like to make inferences. For example, attempting to make extrapolations to all journals after analyzing high-impact journals would be misleading.

Many factors (confounders) may distort the association between the exposure and outcome if the included research reports differ with respect to these factors [ 73 ]. For example, when examining the association between source of funding and completeness of reporting, it may be necessary to account for journals that endorse the guidelines. Confounding bias can be addressed by restriction, matching and statistical adjustment [ 73 ]. Restriction appears to be the method of choice for many investigators who choose to include only high impact journals or articles in a specific field. For example, Knol et al. examined the reporting of p -values in baseline tables of high impact journals [ 26 ]. Matching is also sometimes used. In the methodological study of non-randomized interventional studies of elective ventral hernia repair, Parker et al. matched prospective studies with retrospective studies and compared reporting standards [ 74 ]. Some other methodological studies use statistical adjustments. For example, Zhang et al. used regression techniques to determine the factors associated with missing participant data in trials [ 16 ].

With regard to external validity, researchers interested in conducting methodological studies must consider how generalizable or applicable their findings are. This should tie in closely with the research question and should be explicit. For example. Findings from methodological studies on trials published in high impact cardiology journals cannot be assumed to be applicable to trials in other fields. However, investigators must ensure that their sample truly represents the target sample either by a) conducting a comprehensive and exhaustive search, or b) using an appropriate and justified, randomly selected sample of research reports.

Even applicability to high impact journals may vary based on the investigators’ definition, and over time. For example, for high impact journals in the field of general medicine, Bouwmeester et al. included the Annals of Internal Medicine (AIM), BMJ, the Journal of the American Medical Association (JAMA), Lancet, the New England Journal of Medicine (NEJM), and PLoS Medicine ( n  = 6) [ 75 ]. In contrast, the high impact journals selected in the methodological study by Schiller et al. were BMJ, JAMA, Lancet, and NEJM ( n  = 4) [ 76 ]. Another methodological study by Kosa et al. included AIM, BMJ, JAMA, Lancet and NEJM ( n  = 5). In the methodological study by Thabut et al., journals with a JIF greater than 5 were considered to be high impact. Riado Minguez et al. used first quartile journals in the Journal Citation Reports (JCR) for a specific year to determine “high impact” [ 77 ]. Ultimately, the definition of high impact will be based on the number of journals the investigators are willing to include, the year of impact and the JIF cut-off [ 78 ]. We acknowledge that the term “generalizability” may apply differently for methodological studies, especially when in many instances it is possible to include the entire target population in the sample studied.

Finally, methodological studies are not exempt from information bias which may stem from discrepancies in the included research reports [ 79 ], errors in data extraction, or inappropriate interpretation of the information extracted. Likewise, publication bias may also be a concern in methodological studies, but such concepts have not yet been explored.

A proposed framework

In order to inform discussions about methodological studies, the development of guidance for what should be reported, we have outlined some key features of methodological studies that can be used to classify them. For each of the categories outlined below, we provide an example. In our experience, the choice of approach to completing a methodological study can be informed by asking the following four questions:

What is the aim?

Methodological studies that investigate bias

A methodological study may be focused on exploring sources of bias in primary or secondary studies (meta-bias), or how bias is analyzed. We have taken care to distinguish bias (i.e. systematic deviations from the truth irrespective of the source) from reporting quality or completeness (i.e. not adhering to a specific reporting guideline or norm). An example of where this distinction would be important is in the case of a randomized trial with no blinding. This study (depending on the nature of the intervention) would be at risk of performance bias. However, if the authors report that their study was not blinded, they would have reported adequately. In fact, some methodological studies attempt to capture both “quality of conduct” and “quality of reporting”, such as Richie et al., who reported on the risk of bias in randomized trials of pharmacy practice interventions [ 80 ]. Babic et al. investigated how risk of bias was used to inform sensitivity analyses in Cochrane reviews [ 81 ]. Further, biases related to choice of outcomes can also be explored. For example, Tan et al investigated differences in treatment effect size based on the outcome reported [ 82 ].

Methodological studies that investigate quality (or completeness) of reporting

Methodological studies may report quality of reporting against a reporting checklist (i.e. adherence to guidelines) or against expected norms. For example, Croituro et al. report on the quality of reporting in systematic reviews published in dermatology journals based on their adherence to the PRISMA statement [ 83 ], and Khan et al. described the quality of reporting of harms in randomized controlled trials published in high impact cardiovascular journals based on the CONSORT extension for harms [ 84 ]. Other methodological studies investigate reporting of certain features of interest that may not be part of formally published checklists or guidelines. For example, Mbuagbaw et al. described how often the implications for research are elaborated using the Evidence, Participants, Intervention, Comparison, Outcome, Timeframe (EPICOT) format [ 30 ].

Methodological studies that investigate the consistency of reporting

Sometimes investigators may be interested in how consistent reports of the same research are, as it is expected that there should be consistency between: conference abstracts and published manuscripts; manuscript abstracts and manuscript main text; and trial registration and published manuscript. For example, Rosmarakis et al. investigated consistency between conference abstracts and full text manuscripts [ 85 ].

Methodological studies that investigate factors associated with reporting

In addition to identifying issues with reporting in primary and secondary studies, authors of methodological studies may be interested in determining the factors that are associated with certain reporting practices. Many methodological studies incorporate this, albeit as a secondary outcome. For example, Farrokhyar et al. investigated the factors associated with reporting quality in randomized trials of coronary artery bypass grafting surgery [ 53 ].

Methodological studies that investigate methods

Methodological studies may also be used to describe methods or compare methods, and the factors associated with methods. Muller et al. described the methods used for systematic reviews and meta-analyses of observational studies [ 86 ].

Methodological studies that summarize other methodological studies

Some methodological studies synthesize results from other methodological studies. For example, Li et al. conducted a scoping review of methodological reviews that investigated consistency between full text and abstracts in primary biomedical research [ 87 ].

Methodological studies that investigate nomenclature and terminology

Some methodological studies may investigate the use of names and terms in health research. For example, Martinic et al. investigated the definitions of systematic reviews used in overviews of systematic reviews (OSRs), meta-epidemiological studies and epidemiology textbooks [ 88 ].

Other types of methodological studies

In addition to the previously mentioned experimental methodological studies, there may exist other types of methodological studies not captured here.

What is the design?

Methodological studies that are descriptive

Most methodological studies are purely descriptive and report their findings as counts (percent) and means (standard deviation) or medians (interquartile range). For example, Mbuagbaw et al. described the reporting of research recommendations in Cochrane HIV systematic reviews [ 30 ]. Gohari et al. described the quality of reporting of randomized trials in diabetes in Iran [ 12 ].

Methodological studies that are analytical

Some methodological studies are analytical wherein “analytical studies identify and quantify associations, test hypotheses, identify causes and determine whether an association exists between variables, such as between an exposure and a disease.” [ 89 ] In the case of methodological studies all these investigations are possible. For example, Kosa et al. investigated the association between agreement in primary outcome from trial registry to published manuscript and study covariates. They found that larger and more recent studies were more likely to have agreement [ 15 ]. Tricco et al. compared the conclusion statements from Cochrane and non-Cochrane systematic reviews with a meta-analysis of the primary outcome and found that non-Cochrane reviews were more likely to report positive findings. These results are a test of the null hypothesis that the proportions of Cochrane and non-Cochrane reviews that report positive results are equal [ 90 ].

What is the sampling strategy?

Methodological studies that include the target population

Methodological reviews with narrow research questions may be able to include the entire target population. For example, in the methodological study of Cochrane HIV systematic reviews, Mbuagbaw et al. included all of the available studies ( n  = 103) [ 30 ].

Methodological studies that include a sample of the target population

Many methodological studies use random samples of the target population [ 33 , 91 , 92 ]. Alternatively, purposeful sampling may be used, limiting the sample to a subset of research-related reports published within a certain time period, or in journals with a certain ranking or on a topic. Systematic sampling can also be used when random sampling may be challenging to implement.

What is the unit of analysis?

Methodological studies with a research report as the unit of analysis

Many methodological studies use a research report (e.g. full manuscript of study, abstract portion of the study) as the unit of analysis, and inferences can be made at the study-level. However, both published and unpublished research-related reports can be studied. These may include articles, conference abstracts, registry entries etc.

Methodological studies with a design, analysis or reporting item as the unit of analysis

Some methodological studies report on items which may occur more than once per article. For example, Paquette et al. report on subgroup analyses in Cochrane reviews of atrial fibrillation in which 17 systematic reviews planned 56 subgroup analyses [ 93 ].

This framework is outlined in Fig.  2 .

A proposed framework for methodological studies

Conclusions

Methodological studies have examined different aspects of reporting such as quality, completeness, consistency and adherence to reporting guidelines. As such, many of the methodological study examples cited in this tutorial are related to reporting. However, as an evolving field, the scope of research questions that can be addressed by methodological studies is expected to increase.

In this paper we have outlined the scope and purpose of methodological studies, along with examples of instances in which various approaches have been used. In the absence of formal guidance on the design, conduct, analysis and reporting of methodological studies, we have provided some advice to help make methodological studies consistent. This advice is grounded in good contemporary scientific practice. Generally, the research question should tie in with the sampling approach and planned analysis. We have also highlighted the variables that may inform findings from methodological studies. Lastly, we have provided suggestions for ways in which authors can categorize their methodological studies to inform their design and analysis.

Availability of data and materials

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Abbreviations

Consolidated Standards of Reporting Trials

Evidence, Participants, Intervention, Comparison, Outcome, Timeframe

Grading of Recommendations, Assessment, Development and Evaluations

Participants, Intervention, Comparison, Outcome, Timeframe

Preferred Reporting Items of Systematic reviews and Meta-Analyses

Studies Within a Review

Studies Within a Trial

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LM conceived the idea and drafted the outline and paper. DOL and LT commented on the idea and draft outline. LM, LP and DOL performed literature searches and data extraction. All authors (LM, DOL, LT, LP, DBA) reviewed several draft versions of the manuscript and approved the final manuscript.

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Mbuagbaw, L., Lawson, D.O., Puljak, L. et al. A tutorial on methodological studies: the what, when, how and why. BMC Med Res Methodol 20 , 226 (2020). https://doi.org/10.1186/s12874-020-01107-7

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Received : 27 May 2020

Accepted : 27 August 2020

Published : 07 September 2020

DOI : https://doi.org/10.1186/s12874-020-01107-7

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Transforming Clinical Research to Meet Health Challenges

• 1 Office of the Director, National Institutes of Health, Bethesda, Maryland

The COVID-19 pandemic made “clinical trials” a household phrase, highlighting the critical value of clinical research in creating vaccines and treatments and demonstrating the need for large-scale, well-designed, and rapidly deployed clinical trials to address the public health emergency. As the largest public funder of clinical trials, the National Institutes of Health (NIH) launched a high-level effort to absorb the lessons of the pandemic and to assess and build on ongoing initiatives to improve efficiency, accountability, and transparency in clinical research.

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Wolinetz CD , Tabak LA. Transforming Clinical Research to Meet Health Challenges. JAMA. 2023;329(20):1740–1741. doi:10.1001/jama.2023.3964

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An overview of commonly used statistical methods in clinical research

Affiliations.

• 1 Center for Surgical Outcomes Research, The Research Institute at Nationwide Children's Hospital, Columbus, OH, USA.
• 2 Department of Surgery, Children's Mercy Hospital, 2401 Gillham Road, Kansas City, MO 64108, USA. Electronic address: [email protected].
• PMID: 30473041
• DOI: 10.1053/j.sempedsurg.2018.10.008

Statistics plays an essential role in clinical research by providing a framework for making inferences about a population of interest. In order to interpret research datasets, clinicians involved in clinical research should have an understanding of statistical methodology. This article provides a brief overview of statistical methods that are frequently used in clinical research studies. Descriptive and inferential methods, including regression modeling and propensity scores, are discussed, with focus on the rationale, assumptions, strengths, and limitations to their application.

Keywords: Descriptive statistics; Inferential statistics; Propensity scores; Regression analysis; Survival analysis.

Copyright © 2018 Elsevier Inc. All rights reserved.

Publication types

• Biomedical Research / methods*
• Clinical Trials as Topic / methods*
• Data Interpretation, Statistical*
• Propensity Score
• Regression Analysis
• Research Design*
• Survival Analysis

Research methods & reporting

Guidance on terminology, application, and reporting of citation searching: the tarcis statement, tripod+ai statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods, quantifying possible bias in clinical and epidemiological studies with quantitative bias analysis: common approaches and limitations, assessing robustness to worst case publication bias using a simple subset meta-analysis, regression discontinuity design studies: a guide for health researchers, process guide for inferential studies using healthcare data from routine clinical practice to evaluate causal effects of drugs, updated recommendations for the cochrane rapid review methods guidance for rapid reviews of effectiveness, avoiding conflicts of interest and reputational risks associated with population research on food and nutrition, the estimands framework: a primer on the ich e9(r1) addendum, evaluation of clinical prediction models (part 3): calculating the sample size required for an external validation study, evaluation of clinical prediction models (part 2): how to undertake an external validation study, evaluation of clinical prediction models (part 1): from development to external validation, emulation of a target trial using electronic health records and a nested case-control design, rob-me: a tool for assessing risk of bias due to missing evidence in systematic reviews with meta-analysis, enhancing reporting quality and impact of early phase dose-finding clinical trials: consort dose-finding extension (consort-define) guidance, enhancing quality and impact of early phase dose-finding clinical trial protocols: spirit dose-finding extension (spirit-define) guidance, understanding how health interventions or exposures produce their effects using mediation analysis, a guide and pragmatic considerations for applying grade to network meta-analysis, a framework for assessing selection and misclassification bias in mendelian randomisation studies: an illustrative example between bmi and covid-19, practical thematic analysis: a guide for multidisciplinary health services research teams engaging in qualitative analysis, selection bias due to conditioning on a collider, the imprinting effect of covid-19 vaccines: an expected selection bias in observational studies, a step-by-step approach for selecting an optimal minimal important difference, recommendations for the development, implementation, and reporting of control interventions in trials of self-management therapies, methods for deriving risk difference (absolute risk reduction) from a meta-analysis, transparent reporting of multivariable prediction models for individual prognosis or diagnosis: checklist for systematic reviews and meta-analyses, consort harms 2022 statement, explanation, and elaboration: updated guideline for the reporting of harms in randomised trials, transparent reporting of multivariable prediction models: : explanation and elaboration, transparent reporting of multivariable prediction models: tripod-cluster checklist, bias by censoring for competing events in survival analysis, code-ehr best practice framework for the use of structured electronic healthcare records in clinical research, validation of prediction models in the presence of competing risks, reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence, searching clinical trials registers: guide for systematic reviewers, how to design high quality acupuncture trials—a consensus informed by evidence, early phase clinical trials extension to guidelines for the content of statistical analysis plans, incorporating dose effects in network meta-analysis, consolidated health economic evaluation reporting standards 2022 statement, strengthening the reporting of observational studies in epidemiology using mendelian randomisation (strobe-mr): explanation and elaboration, a new framework for developing and evaluating complex interventions, adapting interventions to new contexts—the adapt guidance, recommendations for including or reviewing patient reported outcome endpoints in grant applications, consort extension for the reporting of randomised controlled trials conducted using cohorts and routinely collected data (consort-routine): checklist with explanation and elaboration, consort extension for the reporting of randomised controlled trials conducted using cohorts and routinely collected data, guidance for the design and reporting of studies evaluating the clinical performance of tests for present or past sars-cov-2 infection, the prisma 2020 statement: an updated guideline for reporting systematic reviews, prisma 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews, preferred reporting items for journal and conference abstracts of systematic reviews and meta-analyses of diagnostic test accuracy studies (prisma-dta for abstracts): checklist, explanation, and elaboration, designing and undertaking randomised implementation trials: guide for researchers, start-rwe: structured template for planning and reporting on the implementation of real world evidence studies, methodological standards for qualitative and mixed methods patient centered outcomes research, grade approach to drawing conclusions from a network meta-analysis using a minimally contextualised framework, grade approach to drawing conclusions from a network meta-analysis using a partially contextualised framework, use of multiple period, cluster randomised, crossover trial designs for comparative effectiveness research, when to replicate systematic reviews of interventions: consensus checklist, reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the consort-ai extension, guidelines for clinical trial protocols for interventions involving artificial intelligence: the spirit-ai extension, preferred reporting items for systematic review and meta-analysis of diagnostic test accuracy studies (prisma-dta): explanation, elaboration, and checklist, non-adherence in non-inferiority trials: pitfalls and recommendations, the adaptive designs consort extension (ace) statement: a checklist with explanation and elaboration guideline for reporting randomised trials that use an adaptive design, machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness, calculating the sample size required for developing a clinical prediction model, spirit extension and elaboration for n-of-1 trials: spent 2019 checklist, synthesis without meta-analysis (swim) in systematic reviews: reporting guideline, alternative approaches for confounding adjustment in observational studies using weighting based on the propensity score: a primer for practitioners, a guide to prospective meta-analysis, rob 2: a revised tool for assessing risk of bias in randomised trials, consort 2010 statement: extension to randomised crossover trials, when and how to use data from randomised trials to develop or validate prognostic models, guide to presenting clinical prediction models for use in clinical settings, a guide to systematic review and meta-analysis of prognostic factor studies, when continuous outcomes are measured using different scales: guide for meta-analysis and interpretation, the reporting of studies conducted using observational routinely collected health data statement for pharmacoepidemiology (record-pe), reporting of stepped wedge cluster randomised trials: extension of the consort 2010 statement with explanation and elaboration, delta,2, guidance on choosing the target difference and undertaking and reporting the sample size calculation for a randomised controlled trial, outcome reporting bias in trials: a methodological approach for assessment and adjustment in systematic reviews, reading mendelian randomisation studies: a guide, glossary, and checklist for clinicians, how to use fda drug approval documents for evidence syntheses, how to avoid common problems when using clinicaltrials.gov in research: 10 issues to consider, tidier-php: a reporting guideline for population health and policy interventions, analysis of cluster randomised trials with an assessment of outcome at baseline, key design considerations for adaptive clinical trials: a primer for clinicians, population attributable fraction, how to estimate the effect of treatment duration on survival outcomes using observational data, concerns about composite reference standards in diagnostic research, statistical methods to compare functional outcomes in randomized controlled trials with high mortality, consort-equity 2017 extension and elaboration for better reporting of health equity in randomised trials, handling time varying confounding in observational research, four study design principles for genetic investigations using next generation sequencing, amstar 2: a critical appraisal tool for systematic reviews that include randomised or non-randomised studies of healthcare interventions, or both, multivariate and network meta-analysis of multiple outcomes and multiple treatments: rationale, concepts, and examples, stard for abstracts: essential items for reporting diagnostic accuracy studies in journal or conference abstracts, statistics notes: percentage differences, symmetry, and natural logarithms, statistics notes: what is a percentage difference, gripp2 reporting checklists: tools to improve reporting of patient and public involvement in research, enhancing the usability of systematic reviews by improving the consideration and description of interventions, how to design efficient cluster randomised trials, consort 2010 statement: extension checklist for reporting within person randomised trials, life expectancy difference and life expectancy ratio: two measures of treatment effects in randomised trials with non-proportional hazards, follow us on, content links.

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We integrate an innovative skills-based curriculum, research collaborations, and hands-on field experience to prepare students.

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Clinical Research Methods

Director: Todd Ogden, PhD

The Mailman School offers the degree of  Master of Science in Biostatistics, with an emphasis on issues in the statistical analysis and design of clinical studies. The Clinical Research Methods track was conceived and designed for clinicians who are pursuing research careers in academic medicine.  Candidacy in the CRM program is open to anyone who holds a medical/doctoral degree and/or has several years of clinical research experience.

Competencies

In addition to achieving the MS in Biostatistics core competencies, graduates of the 30 credit MS Clinical Research Methods Track develop specific competencies in data analysis and computing, public health and collaborative research, and data management. MS/CRM graduates will be able to:

Data Analysis and Computing

• Apply the basic tenets of research design and analysis for the purpose of critically reviewing research and programs in disciplines outside of biostatistics;
• Differentiate between quantitative problems that can be addressed with standard methods and those requiring input from a professional biostatistician.

Public Health and Collaborative Research

• Formulate and prepare a written statistical plan for analysis of public health research data that clearly reflects the research hypotheses of the proposal in a manner that resonates with both co-investigators and peer reviewers;
• Prepare written summaries of quantitative analyses for journal publication, presentations at scientific meetings, grant applications, and review by regulatory agencies;

Data Management

• Identify the uses to which data management can be put in practical statistical analysis, including the establishment of standards for documentation, archiving, auditing, and confidentiality; guidelines for accessibility; security; structural issues; and data cleaning;
• Differentiate between analytical and data management functions through knowledge of the role and functions of databases, different types of data storage, and the advantages and limitations of rigorous database systems in conjunction with statistical tools;
• Describe the different types of database management systems, the ways these systems can provide data for analysis and interact with statistical software, and methods for evaluating technologies pertinent to both; and
• Assess database tools and the database functions of statistical software, with a view to explaining the impact of data management processes and procedures on their own research. 

Required Courses

The required courses enable degree candidates to gain proficiency in study design, application of commonly-used statistical procedures, use of statistical software packages, and successful interpretation and communication of analysis results. A required course may be waived for students with demonstrated expertise in that field of study. If a student places out of one or more required courses, that student must substitute other courses, perhaps a more advanced course in the same area or another elective course in biostatistics or another discipline, with the approval of the student’s faculty advisor.

The program, which consists of 30 credits of coursework and research, may be completed in one year, provided the candidate begins study during the summer semester of his or her first year. If preferred, candidates may pursue the MS/CRM on a part-time basis. The degree program must be completed within five years of the start date.

The curriculum, described below, is comprised of 24 credits of required courses, including a 3-credit research project (the “Master’s essay”) to be completed during the final year of study, and two electives of 6 credits. Note that even if a course is waived, students must still complete a minimum of 30 credits to be awarded the MS degree.

Commonly chosen elective courses include:

Master's Essay

As part of MS/CRM training, each student is required to register for the 3-credit Master's essay course (P9160). This course provides direct support and supervision for the completion of the required research project, or Master's essay, consisting of a research paper of publishable quality. CRM candidates should register for the Master's essay during the spring semester of their final year of study. Students are required to come to the Master's essay course with research data in hand for analysis and interpretation.

CRM graduates have written excellent Master's essays over the years, many of which were ultimately published in the scientific literature. Some titles include:

• A Comprehensive Analysis of the Natural History and the Effect of Treatment on Patients with Malignant Pleural Mesothelioma
• Prevalence and Modification of Cardiovascular Risk Factors in Early Chronic Kidney Disease: Data from the Third National Health and Nutrition Examination Survey
• Perspectives on Pediatric Outcomes: A Comparison of Parents' and Children's Ratings of Health-Related Quality of Life
• Clinical and Demographic Profiles of Cancer Discharges throughout New York State Compared to Corresponding Incidence Rates, 1990-1994

Sample Timeline

Candidates may choose to complete the CRM program track on a part-time basis, or complete all requirements within one year (July through May). To complete the degree in one year, coursework must commence during the summer term. 

Note that course schedules change from year to year, so that class days/times in future years will differ from the sample schedule below; you must check the current course schedule for each year on the course directory page .

Paul McCullough Director of Academic Programs Department of Biostatistics Columbia University [email protected] 212-342-3417

More information on Admission Requirements and Deadlines.

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Clinical trials: A significant part of cancer care

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Editor's note: May is National Cancer Research Month.

By Mayo Clinic staff

A cancer diagnosis is an emotional experience. Learning that you have cancer can create feelings of hopelessness, fear and sadness. This is especially true if your cancer is advanced or available treatments are unable to stop or slow its growth.

"Often, when patients are diagnosed with cancer , they feel hopeless and scared. Clinical trials are one way patients can be proactive. They can make a choice in how their care is going to be," says Matthew Block, M.D., Ph.D. , a Mayo Clinic medical oncologist.

Cancer clinical trials help physician-scientists test new and better ways to control and treat cancer. During a clinical trial, participants receive specific interventions, and researchers determine if those interventions are safe and effective. Interventions studied in clinical trials might be new cancer drugs or new combinations of drugs, new medical procedures, new surgical techniques or devices, new ways to use existing treatments, and lifestyle or behavior changes.

Clinical trials provide access to potential treatments under investigation, giving options to people who otherwise may face limited choices. "Clinical trials open the door to a new hope that maybe we can fight their cancer back and give them a better quality of life," says Geoffrey Johnson, M.D., Ph.D. , a Mayo Clinic radiologist, nuclear medicine specialist and co-chair of the Mayo Clinic Comprehensive Cancer Center Experimental and Novel Therapeutics Disease Group.

You will receive cancer treatment if you participate in a clinical trial. "I think one common misperception about clinical trials is that if you enter a clinical trial, you may not get treatment (receive a placebo). And that's actually very much not true. Most clinical trials are looking at one treatment compared to another treatment," says Judy C. Boughey, M.D. , a Mayo Clinic surgical oncologist, chair of Breast and Melanoma Surgical Oncology at Mayo Clinic in Rochester, Minnesota, and chair of the Mayo Clinic Comprehensive Cancer Center Breast Cancer Disease Group.

"I think one common misperception about clinical trials is that if you enter a clinical trial, you may not get treatment (receive a placebo). And that's actually very much not true. Most clinical trials are looking at one treatment compared to another treatment." Judy C. Boughey, M.D.

Watch this video to hear the experiences of people who have participated in cancer clinical trials and to hear Drs. Block, Johnson and Boughey discuss the importance of clinical trials in cancer care:

Clinical trials are a significant part of cancer care at Mayo Clinic Comprehensive Cancer Center. Cancer care teams work together across specialties to make sure the right clinical trials are available to serve the needs of people with cancer who come to Mayo Clinic.

"We are very particular in how we select the clinical trials that we have available for patients," says Dr. Boughey. "We want to have the best trials available for our patients. Some of the clinical trials are evaluating drugs — we are so excited about those drugs, but we can't prescribe those drugs for patients without having that trial. And so we will actually fight to try to get that trial open here to have it available as an opportunity for our patients."

If you choose to participate in a clinical trial, you will continue to receive cancer care. "For most patients that we evaluate, there's always the standard of care treatment option for those patients. And then, in many situations, there's also a clinical trial that the patient can participate in," says Dr. Boughey.

People who participate in clinical trials help make new and better cancer care available for future patients. The treatments available for cancer patients today exist because of the clinical trial participants of yesterday. "We couldn't advance medicine if it wasn't for people volunteering for trials. And the promise from our side is to say we're not going to put patients on trials or offer trials for them to consider unless we think there's a good chance that they'll get a benefit or that society at large will get a benefit," says Dr. Johnson.

"We couldn't advance medicine if it wasn't for people volunteering for trials. And the promise from our side is to say we're not going to put patients on trials or offer trials for them to consider unless we think there's a good chance that they'll get a benefit or that society at large will get a benefit." Geoffrey Johnson, M.D., Ph.D.

Participating in a clinical trial may give you access to cutting-edge treatment, improve your quality of life and extend your time with loved ones.

"It's definitely worth reaching out to your healthcare provider and asking, 'What clinical trials could I be a potential candidate for?'" says Dr. Boughey. "And remember, you can ask this of your surgical oncologist, your medical oncologist, your radiation oncologist, or any of the physicians you're seeing because there are trials in all disciplines. There are also ongoing trials that require the collection of tissue or the donation of blood. They can also be important in trying to help future generations as we continue to work to end cancer."

Participating in a clinical trial is an important decision with potential risks and benefits. Explore these FAQ about cancer clinical trials, and ask your care team if a clinical trial might be right for you.

Learn more about cancer clinical trials and find a clinical trial at Mayo Clinic.

Join the Cancer Support Group on Mayo Clinic Connect , an online community moderated by Mayo Clinic for patients and caregivers.

Read these articles about people who have participated in clinical trials at Mayo Clinic:

• A silent tumor, precancerous polyps and the power of genetic screening
• Mayo Clinic’s DNA study reveals BRCA1 mutations in 3 sisters, prompts life-changing decisions

Read more articles about Mayo Clinic cancer research made possible by people participating in clinical trials.

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Dr. Sujay Vora is studying a new approach to glioblastoma treatment that is improving health outcomes and quality of life for elderly people like Richard Casper.

Dr. S. John Weroha discusses new treatments and research that are helping more people survive ovarian cancer.

Hypothesis-driven AI offers an innovative way to use massive datasets to help discover the complex causes of diseases such as cancer and improve treatment strategies.

• Clinical Trials

Human Carbon Dioxide Delivery Methods and Performance Characteristics

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Tab Title Description

• Observational study — observes people and measures outcomes without affecting results.
• Interventional study (clinical trial) — studies new tests, treatments, drugs, surgical procedures or devices.
• Medical records research — uses historical information collected from medical records of large groups of people to study how diseases progress and which treatments and surgeries work best.
• Rochester, Minnesota: 23-012477

About this study

Thepurpose of this study is to pursue preliminary testing to determine if acute exposure to elevated levels of inspired CO2 with a normal O2 background impacts human exercise performance.

Participation eligibility

Participant eligibility includes age, gender, type and stage of disease, and previous treatments or health concerns. Guidelines differ from study to study, and identify who can or cannot participate. There is no guarantee that every individual who qualifies and wants to participate in a trial will be enrolled. Contact the study team to discuss study eligibility and potential participation.

Inclusion Criteria:

• 18-50 years of age.

Exclusion Criteria:

• Not actively training on a daily or near-daily basis, not engaged in competitive activities, VO2max <120% of age-predicted, any known chronic disease, smoking history within 6 months, orthopedic limitations will be excluded from the study, recent illness or infection or lapse in training of greater than 1 week.

Note: Other protocol defined Inclusion/Exclusion criteria may apply.

Eligibility last updated 12/4/23. Questions regarding updates should be directed to the study team contact.

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Study statuses change often. Please contact the study team for the most up-to-date information regarding possible participation.

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Principal stratification methods and software for intercurrent events in clinical trials

CERSI Collaborators: Triangle CERSI, Duke University: Fan Li, PhD; Laine Thomas, PhD; Anqi Zhao, PhD; Susan Halabi, PhD

FDA Collaborators: Yuan-Li Shen, Dr.P.H; Pallavi Mishra-Kalyani, PhD; Shu Wang, PhD.; Xiaoxue Li, PhD.; Joyce Cheng, PhD

CERSI Subcontractors: Flying Buttress Associates- Jeph Herrin, PhD

CERSI In-Kind Collaborators: OptumLabs - William Crown, PhD; University of San Francisco - Sanket Dhruva, MD

Non-Federal Entity Collaborators: Johnson and Johnson- Karla Childers, MSJ, Paul Coplan, ScD, MBA, Stephen Johnston, MSc

Project Start Date: September 8, 2023

Regulatory Science Challenge

Events that occur post randomization in randomized control trials, known as intercurrent events, can alter the course of the randomized clinical trials and jeopardize comparative effectiveness evaluation and consequently decision making in regulatory science. The standard approach of intention-to-treat analysis ignores intercurrent events and thus preserves the trial validity based on randomization, but it fails to capture treatment effect heterogeneity and the complex causal mechanism. The 2018 ICH E9(R1) addendum suggests principal stratification as an alternative approach to handle intercurrent events, but significant gaps exist between the theory and practice of principal stratification in regulatory science. In particular, there is a lack of transparent and accessible analytical methods, practical guidelines, and software of principal stratification in the context of regulatory science.

Project Description and Goals

This project aims to develop a suite of transparent and accessible analysis tools, software and educational material for applying the principal stratification method to analyze intercurrent events in clinical trials. Investigators will focus on two prevalent types of intercurrent events: (1) nonadherence to assigned treatment, including treatment switching and discontinuation and (2) truncation of the target outcome by a terminal event. For each type, investigators will develop estimand, computational, visualization, and sensitivity analysis tools, with a special emphasis on time-to-event outcomes. They will also develop a companion R package and tutorials with illustrations of clinical trials in oncology and other diseases. The results of this study will impact clinical trials in two ways: (1) produce new methodological tools for addressing a pressing and prevalent complication in clinical trials, (2) provide associated open-source software and educational material to disseminate the methodology to regulatory agencies, health researchers, and industry. Investigators also plan to develop scientific publications describing the outcomes of this research and discuss it at public forums.

Research Outcomes/Results

Two hundred and twenty-three patients with a mean age of 65 years completed the survey. These patients preferred a higher chance of good biopsy outcomes, and a lower chance of erectile dysfunction caused by the treatment and urinary incontinence after treatment. The patients stated in the survey that they are willing to accept:

• a 15.1%-point increase in erectile dysfunction caused by the treatment to achieve a 10%-point increase in a good biopsy outcome after HIFU ablation, and
• an 8.5%-point increase in urinary incontinence for a 10%-point increase in a good biopsy.

Also, further analysis revealed that patients who thought their cancer was more aggressive were more willing to tolerate urinary incontinence. Younger men were willing to tolerate less erectile dysfunction risk than older men. Respondents with a greater than college level of education were less willing to tolerate erectile dysfunction or urinary incontinence.

Research Impacts

Incorporating patient preference information into decisions that FDA makes about regulating devices is one of the major goals of FDA’s Center for Devices and Radiological Health (CDRH). Study findings show that patients prefer specific outcomes related to prostate ablation therapies like HIFU. The study results may help inform the design and regulation of current and future prostate tissue ablation devices by providing information about outcomes that patients most desire.

Publications

• PMID: 34677594; Citation: Wallach JD, Deng Y, McCoy RG, Dhruva SS, Herrin J, Berkowitz A, Polley EC, Quinto K, Gandotra C, Crown W, Noseworthy P, Yao X, Shah ND, Ross JS, Lyon TD. Real-world Cardiovascular Outcomes Associated With Degarelix vs Leuprolide for Prostate Cancer Treatment.  JAMA Netw Open. 2021;4(10):e2130587. doi:10.1001/jamanetworkopen.2021.30587 .
• PMID: 36191949; Citation: Deng Y, Polley EC, Wallach JD, Dhruva SS, Herrin J, Quinto K, Gandotra C, Crown W, Noseworthy P, Yao X, Lyon TD, Shah ND, Ross JS, McCoy RG. Emulating the GRADE trial using real world data: retrospective comparative effectiveness study. BMJ . 2022 Oct 3;379:e070717. doi: 10.1136/bmj-2022-070717 .

Enhancing data integrity in Electronic Health Records: Review of methods for handling missing data

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Introduction Electronic Health Records (EHRs) are vital repositories of patient information for medical research, but the prevalence of missing data presents an obstacle to the validity and reliability of research. This study aimed to review and categorise methods for handling missing data in EHRs to help researchers better understand and address the challenges related to missing data in EHRs. Materials and Methods This study employed scoping review methodology. Through systematic searches on EMBASE up to October 2023, including review articles and original studies, relevant literature was identified. After removing duplicates, titles and abstracts were screened against inclusion criteria, followed by full-text assessment. Additional manual searches and reference list screenings were conducted. Data extraction focused on imputation techniques, dataset characteristics, assumptions about missing data, and article types. Additionally, we explored the availability of code within widely used software applications. Results We reviewed 101 articles, with two exclusions as duplicates. Of the 99 remaining documents, 21 underwent full-text screening, with nine deemed eligible for data extraction. These articles introduced 31 imputation approaches classified into ten distinct methods, ranging from simple techniques like Complete Case Analysis to more complex methods like Multiple Imputation, Maximum Likelihood, and Expectation-Maximization algorithm. Additionally, machine learning methods were explored. The different imputation methods, present varying reliability. We identified a total of 32 packages across the four software platforms (R, Python, SAS, and Stata) for imputation methods. However, it's significant that machine learning methods for imputation were not found in specific packages for SAS and Stata. Out of the 9 imputation methods we investigated, package implementations were available for 7 methods in all four software platforms. Conclusions Several methods to handle missing data in EHRs are available. These methods range in complexity and make different assumptions about the missing data mechanisms. Knowledge gaps remain, notably in handling non-monotone missing data patterns and implementing imputation methods in real-world healthcare settings under the Missing Not at Random assumption. Future research should prioritize refining and directly comparing existing methods.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

Amin Vahdati, Pre-Doctoral Fellow (NIHR 129296), is funded by the National Institute for Health and Care Research (NIHR) for this research project.

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I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

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All data produced in the present work are contained in the manuscript.

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Association between periodontitis and dental caries: a systematic review and meta-analysis

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• Published: 10 May 2024
• Volume 28 , article number  306 , ( 2024 )

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• Yixin Li 1   na1 ,
• Yonggang Xiang 2   na1 ,
• Haixia Ren 1   na1 ,
• Chao Zhang 3 ,
• Ziqiu Hu 1 ,
• Weidong Leng 1 &
• Lingyun Xia 1  

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Recent evidence suggested a link between periodontitis (PD) and dental caries, but the trends and nature of this association remained unclear. The overall aim of this study was to critically assess the correlation of two disorders.

A comprehensive search was conducted within the PUBMED and EMBASE databases including grey literatures up to July 5th, 2023. The Newcastle–Ottawa scale was used to qualitatively evaluate the risk of bias.

Overall, 18 studies were included. In terms of caries risk in PD patients, the prevalence of caries was increased by PD (OR = 1.57, 95%CI:1.20–2.07), both in crown (OR = 1.03, 95%CI:1.01–1.05) and root caries (OR = 2.10, 95%CI:1.03–4.29). Odds of caries were also raised by PD severity (OR moderate  = 1.38, 95%CI:1.15–1.66; OR severe  = 2.14, 95%CI:1.74–2.64). Besides, patients with PD exhibited a higher mean number of decayed, missing and filled teeth (DMFT) and decayed and filled root teeth (DFR) [weighted mean difference (WMD) DMFT  = 0.87, 95%CI: -0.03–1.76; WMD DFR  = 1.13, 95%CI: 0.48–1.78]. Likewise, patients with caries had an elevated risk of PD (OR = 1.79, 95%CI:1.36–2.35). However, Streptococcus mutans, one of the main pathogens of caries, was negatively correlated with several main pathogens of periodontitis.

Conclusions

This study indicated a positive correlation between dental caries and periodontitis clinically, while the two disease-associated pathogens were antagonistic.

Clinical relevance

Further research, including clinical cohort studies and mechanisms of pathogens interaction is needed on this link for better prevention and treatment of PD and caries. In addition, innovative prevention strategies need to be developed and incorporated in dental practices to prevent these two highly prevalent oral diseases.

Avoid common mistakes on your manuscript.

Introduction

Periodontitis (PD) is a chronic multifactorial inflammatory disease caused by dysbiosis, resulting in progressive destruction of the dental surrounding tissues and tooth loss [ 1 ]. Its severe form was considered the 6th most prevalent disease of humankind in 2010, which affected 743 million people aged 15 and over worldwide [ 2 , 3 ]. Dental plaque is an initiating factor that first causes gingivitis if accumulated, resulting in the loss of collagen locally [ 4 ]. Destructive periodontitis will occur gradually if the inflammation is not well controlled, which has been associated with dysbiosis where the diversity, richness and relative proportions of species in the subgingival microbiota are altered [ 5 ]. While the periodontal diseases are unquestionably initiated by bacteria, it is the individual’s host inflammatory response and other risk factors that ultimately determine the clinical presentation and outcome of the many and varied forms of periodontal disease [ 6 ]. The epithelium lining periodontal pockets becomes ulcerated, providing a direct entry point for periodontal bacteria into the systemic circulation. Alternatively, the inflammatory response to periodontal bacteria or their by-products may have indirect systemic effects linking periodontitis to a number of chronic systemic diseases [ 7 ], such as diabetes [ 8 ], rheumatoid arthritis [ 9 ] and Alzheimer's disease [ 10 ].

Dental caries, otherwise known as tooth decay, is one of the most prevalent chronic diseases among people worldwide [ 11 ]. According to the World Health Organization (WHO) report in 2022, untreated carious lesions (both deciduous and permanent teeth), severe periodontal disease, edentulism and cancer of the lip and oral cavity were the leading causes of oral disease burden [ 12 ]. It is caused by cariogenic microorganisms in the dental plaque biofilm, which ferment dietary carbohydrates to produce acid, leading to mineral loss from tooth hard tissues and subsequently the destruction of tooth structures [ 13 ]. The interplay between microorganisms, diet and host susceptibility determines whether dental caries will occur [ 11 ]. Dental caries in enamel is typically first seen as white spot lesions. The cavity site provides an ecological niche in which plaque organisms gradually adapt to a reduced pH [ 14 ], followed by bacteria penetrating further into the tissue at an earlier stage of lesion development [ 15 ] and gradually progressing to pulpitis, periapical inflammation, and even tooth loss if not controlled [ 16 ].

Most of the time, a homeostatic balance exists between the host and microbial communities. Distinct microenvironments contain unique microbial communities, which are regulated through sophisticated signal systems and by host and environment [ 5 ]. The dynamic and polymicrobial oral microbiome is a direct precursor of dental caries and periodontitis, two of the most prevalent microbially induced disorders worldwide. As a community develops, microbial metabolism and by-products of the host immune response can cause changes to the local environment that facilitate the outgrowth or over-representation of microorganisms associated with a dysbiotic state [ 17 ]. Mutans streptococci (especially Streptococcus mutans) and Lactobacillus have long been recognized as pathogens that are associated with caries [ 5 ], where previous studies supported a significant correlation between their concentration in saliva and proportion in plaque [ 13 ]. While the flora imbalance of Porphyromonas gingivalis, Fusobacterium nucleatum, Actinobacillus actinomycetemcomitans and others are tied to the progression of periodontitis [ 18 , 19 , 20 ]. Due to the distinction of pathogenic bacteria and clinical manifestations, PD and dental caries were often regarded as two independent diseases and studied separately. Nonetheless, the association between PD and dental caries has been debated in recent years, with some studies showing an inverse association and others showing a positive association between these two diseases [ 21 , 22 ]. However, little was still known about the direction and nature of the association between these two conditions. The overall aim of this study was to conduct a robust critical appraisal of the evidence on the relationship between PD and dental caries, mainly in terms of correlation of the clinical prevalence and the predominant causative organisms between the two diseases.

Search strategy

Studies were selected based on the PECOS question, including observational studies in humans with caries/periodontitis (P—persons) in which periodontitis/caries was present (E—exposure) or absent (C—comparison) to observe the prevalence of caries/periodontitis and the relationship of major pathogenic bacteria (O—outcome). Hence, we addressed several key questions: Is there any association between periodontitis and caries? Is it positive or negative and are there any clinical and bacteriological correlation between these two diseases?

The search string considered alternate terms incorporating several relevant key words and Medical Subject Headings (MeSH). The final Boolean search string was: (periodontal diseases∗ OR periodontitis) AND (dental caries* OR caries) ( Appendix S1 ) . The search string was applied from PubMed and EMBASE databases as well as grey literatures until July 5th 2023 to ensure retrieval of a broad scope of literature.

Eligibility criteria

Eligible studies were examined by two authors independently. The final selection was verified by a third author, and disagreements were resolved through discussions.

Inclusion criteria

Dental caries condition in patients with periodontitis.

Population: human.

Exposure: subjects with periodontitis. The confident case definition for periodontitis were defined as periodontal pocket depth (PPD) ≥ 4 mm, clinical attachment loss (CAL) ≥ 3 mm, and community periodontal index (CPI) ≥ 3 [ 23 ]; The non-confident case definition was considered as ‘Alveolar bone loss’ without other measurements of PPD/CAL; Unclear diagnostic criteria for periodontitis.

Non-exposure: subjects without periodontitis (with periodontal health or gingivitis).

Outcome: the primary outcome was defined as prevalence of dental caries (crown caries or root caries) in individuals with periodontitis, the secondary outcome was mean DMFT (the amount of decayed, missing, filled permanent teeth) or DFR (the amount of decayed, filled root teeth), along with the correlation of pathogenic bacteria related to caries and PD.

Study design: case–control or cohort studies.

Periodontitis condition in patients with caries.

Exposure: subjects with caries, it was defined as DT ≠ 0.

Non-exposure: subjects without caries.

Outcome: the primary outcome was defined as prevalence of periodontitis in individuals with caries; the secondary outcome was the correlation of pathogenic bacteria related to caries and periodontitis.

Exclusion criteria

Studies with groups evaluating periodontitis or caries separately, case reports, review or guidelines, no full text available nor English language used.

Publications were further excluded if periodontal status was only assessed by tooth loss or gingival appearance.

Quality assessment

Newcastle–Ottawa Scale (NOS) [ 24 ] was employed to evaluate the methodological quality of the included studies, which were defined as moderate or high methodological quality with at least five scores.

Data extraction and processing

Data extraction conducted by YX.Li and YG.Xiang was based on a standardized, pre-piloted data extraction form. The extracted information included: authors and year, study design, characteristics of the sample (size, age, location, and study group); evaluation method (periodontitis and caries diagnosis), statistical analysis, the primary outcomes ([RR] or [OR], 95% confidence interval (CI) or those providing absent raw numbers are available for crude calculation) and the secondary outcomes (mean DMFT or DFR, and the association and interaction of pathogenic bacteria associated with caries and periodontitis).

Statistical analysis

The estimates (or adjusted estimates if applicable) and the corresponding 95% confidence interval (CI) between PD and caries were used to calculate the pooled estimates. If no estimates were available in the studies, the numbers of cases (with PD/caries or not) and controls (with PD/caries or not) were used to calculate the pooled estimates. Heterogeneity was evaluated using the Cochrane I 2 statistic [ 25 ]. Descriptive statistics were performed to summarize the evidence retrieved, checking further for study variations in terms of study characteristics and results. For caries condition in patients with PD, subgroups analyses were performed in periodontitis diagnostic criteria, periodontitis severity, type of caries, age and gender. Similarly, for PD risk in patients with caries, the subgroups analyses were performed in age and gender. Funnel plot was generated to assess publication bias for primary outcome by the visual inspection of asymmetry. Rev Man 5.1 was employed to perform all analyses.

Literature selection

The literature search process was summarized in Fig.  1 . Briefly, 10,094 articles were retrieved by an initial database search, including exclusion of 783 duplications. 9,227 publications were excluded after screening the abstracts. 84 publications were eligible for full text screening. Finally, a total of 18 publications were included for final meta-analysis.

PRISMA flowchart: selection process of studies and results of the literature search for meta-analysis

Study characteristics and quality assessment

All included studies were published between 1994 and 2022. The characteristics of these 18 studies were shown in Table  1 , including 10 studies [ 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ] in relative to the prevalence of caries in patients with periodontitis, 5 [ 29 , 30 , 31 , 32 , 36 ] containing the prevalence of periodontitis in patients with caries and 7 studies [ 33 , 37 , 38 , 39 , 40 , 41 , 42 ] covering the association between pathogenic bacteria related to two diseases.

Study quality for observational studies assessed by the Newcastle–Ottawa scale varied across the studies, ranging from a score of 5/9 to 8/9 ( Appendix S2 ) . The assessment revealed several potential sources of bias including the representativeness of the cases and lack of adjustment for potential confounders.

Dental caries risk in patients with periodontitis

To assess the risk of having caries in individuals with periodontitis, all the 10 relative studies [ 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ] were included. The pooled results showed that the risk of having caries was associated with periodontitis (OR = 1.57, 95% CI: 1.20–2.07, P = 0.001) in Fig.  2 .

Forest plot for caries risk of patients with periodontitis

Subgroup analysis

Definition of periodontitis.

Subgroup analysis by the definition of periodontitis indicated that 8 studies [ 26 , 27 , 29 , 30 , 31 , 32 , 34 , 35 ] with standard definition of periodontitis confirmed higher odds of caries (OR = 1.62, 95% CI: 1.20–2.18; P = 0.002) as well as 2 studies [ 28 , 33 ] with a non-standard definition of periodontitis. (OR = 1.38, 95% CI: 1.07–1.79; P = 0.01, Fig.  3 ).

Forest plot by definition of periodontitis subgroup analysis for caries risk of patients with periodontitis

Periodontitis severity

Subgroup analysis by severity of periodontitis demonstrated that caries was significantly associated with moderate (OR = 1.38, 95% CI: 1.15–1.66, P = 0.0006) and severe periodontitis (OR = 2.14, 95% CI: 1.74–2.64, P = 0.0002). One study [ 29 ] reported the risk of mild periodontitis with caries (OR = 1.65, 95% CI: 0.45–6.05) in Fig.  4 .

Forest plot by severity of periodontitis subgroup analysis for caries risk of patients with periodontitis

Different surfaces of caries

Subgroup analysis by different surfaces of caries showed that the risk of the site where caries occurs varied in crown caries (OR = 1.03, 95% CI: 1.01–1.05, P = 0.003) and root caries (OR = 2.10, 95% CI: 1.03–4.29, P = 0.04). Especially, an increased risk of mixed-type caries defined as covering crown and root surfaces in one study [ 35 ] was reported (OR = 3.40, 95% CI: 3.10–3.73, P  < 0.00001, Fig.  5 ).

Forest plot by different surfaces of caries subgroup analysis for caries risk of patients with periodontitis

Age and gender

Additionally, subgroup analyses were also performed by gender and age in Figs.  6 and 7 . The risk of caries was both higher in 30 to 64 years old (OR = 1.67, 95% CI: 1.37–2.03, P  < 0.00001) and 65 to over 75(OR = 1.69, 95% CI: 1.44–1.98, P  < 0.00001). Two studies [ 27 , 32 ] reported the risk of dental caries between male and female, respectively, which varied in male (OR = 1.48, 95% CI: 1.26–1.74, P  < 0.00001) and female (OR = 1.36, 95% CI: 0.74–2.49, P = 0.32).

Forest plot by age subgroup analysis for caries risk of patients with periodontitis

Forest plot by gender subgroup analysis for caries risk of patients with periodontitis

OR adjusted or not

Subgroup analysis by OR adjusted or not showed that 2 studies [ 26 , 34 ] reported adjusted ORs, where the precision of the estimate based on the confidence interval bounds was not significant (OR = 1.28, 95% CI: 0.85–1.93, P = 0.24). 8 studies [ 27 , 28 , 29 , 30 , 31 , 32 , 33 , 35 ] provided absent raw numbers available for crude calculation (OR = 1.69, 95% CI: 1.41–2.01, P = 0.0005, Fig.  8 ).

Forest plot by OR adjusted or not subgroup analysis for caries risk of patients with periodontitis

Mean DMFT or DFR

Four studies [ 26 , 33 , 34 , 43 ] reported mean DMFT or DFR in patients with periodontitis. One study [ 37 ] indicated significant positive associations between the periodontal disease severity (CDC-AAP) [ 23 ] and the amount of decayed, missing and filled teeth surfaces (DMFS) (OR adjusted  = 1.03, 95% CI: 1.01–1.05) and the decayed surfaces (DS) indices (OR adjusted  = 1.18, 95% CI: 1.05–1.32) as well as between the percentage of sites with CAL ≥ 3 mm and DMFS (OR adjusted  = 1.03, 95% CI: 1.00–1.05) and DS indices (OR adjusted  = 1.12, 95% CI: 1.00–1.25). Overall meta-analysis showed Patients with periodontitis exhibited higher DMFT [weighted mean difference (WMD) = 0.87, 95% CI: -0.03–1.76, P = 0.06] and DFR (WMD = 1.13, 95% CI: 0.48–1.78, P = 0.0007) when compared with patients without periodontitis (Appendix S4 ).

Periodontitis risk in patients with caries

To assess the risk of having periodontitis in individuals with caries, all the 5 relative studies [ 29 , 30 , 31 , 32 , 36 ] were included. Statistically significant heterogeneity was confirmed with I 2 test (I 2  = 82%, P = 0.0002). The pooled results in Fig.  9 showed that the risk of periodontitis was associated with caries (OR = 1.79, 95% CI: 1.36–2.35, P = 0.0002), both for crude ORs (2.35, 95% CI: 2.04–2.70, P  < 0.00001) and adjusted ORs (1.57, 95%CI: 1.32–1.86, P  < 0.00001, Fig.  10 ). Other subgroups, such as types of caries, severity of periodontitis, age and gender, were not analyzed due to limited data.

Forest plot for periodontitis risk of patients with caries

Forest plot by OR adjusted or not subgroup analysis for periodontitis risk of patients with caries

Bacterial association between periodontitis and caries

Seven studies [ 33 , 37 , 38 , 39 , 40 , 41 , 42 ] were included as reporting the correlation between the dominant pathogenic bacteria associated with PD and caries. An increased risk of detectable high level (> 10 4  CFU/ml) of P. gingivalis was reported in the saliva when PD occurred (OR = 3.38, 95% CI: 1.46–7.83, P = 0.005), while S. mutans, one of the main pathogens of caries, showing a declining risk in PD (OR = 0.82, 95% CI: 0.44–1.51), although no significant difference (P = 0.470, Appendix S5 ). Three studies [ 37 , 38 , 40 ] reported the relationship between levels of bacteria in saliva samples and loss of attachment. One of them [ 40 ], showed that patients with high Lactobacillus (> 10 5  CFU/ml) in saliva had significantly higher average attachment loss (P = 0.014) and rate of sites with > 4 mm of attachment loss (P = 0.014). S. mutans followed the same trend as Lactobacillus. Similarly, another study by Robert et al. [ 37 ] showed that high levels of S. mutans (> 10 5  CFU/ml) in saliva were negatively correlated with a decrease in the sites of loss of attachment > 3 mm, OR adjusted  = 0.74 (0.35–1.60), P = 0.447. Besides, this study reported a significant positive correlation between levels of F. nucleatum and S. mutans [OR adjusted  = 6.03 (1.55–23.45), P = 0.009] as well as Actinobacillus [OR adjusted  = 2.39 (1.00–5.71), P = 0.051]. What’s more, one study [ 38 ] reported the relationship of pathogenic bacteria in the saliva of patients with aggressive periodontitis, which found that the level of Actinobacillus in saliva was positively correlated with the proportion of deep periodontal pockets (> 7 mm), P = 0.03, and negatively correlated with the level of S. mutans. Among them, Actinobacillus was detectable in 25 out of 30 aggressive periodontitis (AgP) patients, 15 of which showed low levels of S. mutans in their saliva (< 10 3  CFU/ml), indicating a negative correlation (OR = 0.08, 95% CI: 0.01–0.85, P = 0.005) between them. Moreover, the level of Actinobacillus in saliva of patients with low level of S. mutans (< 10 3  CFU/ml) was significantly higher than that of patients with high level of S. mutans (> 10 5  CFU/ml) and medium level of S. mutans (10 3 –10 5  CFU/ml), P = 0.009. Three studies [ 39 , 41 , 42 ] reported the effect of periodontal treatment on periodontitis and caries bacteria. As summarized in Appendix S6 , compared to pre-treatment, the prevalence of high levels (> 10 4  CFU/ml) of S. mutans increased after scaling and root planning (OR = 1.54, 95% CI: 0.47–5.02), but there was no statistically significant difference. (P = 0.470). Nevertheless, the mean level of S. mutans was significantly higher ( P  < 0.05) after periodontal therapy. Another study [ 42 ] assessed levels of saliva and supragingival plaque during periodontal treatment. The amount of S. mutans in saliva increased significantly at 8th month ( P  < 0.05). For supragingival plaque, the CFUs of S. mutans remained basically unchanged, but the frequency of detection increased from 4/10 to 6/10. lactobacilli decreased significantly at 8th month compared to preoperative ( P  < 0.05).

Publication bias

Study publication bias was examined using funnel plot for caries risk in patients with PD in Appendix S7 . Visual assessment of the Funnel plot revealed studies was displayed nearly symmetrical appearance, indicating no significant bias.

This systematic review supported a positive association between PD and caries. In terms of caries condition in patients with PD, patients with moderate to severe periodontitis had a higher (57%) chance of having caries compared to non-periodontitis patients. In addition, a positive linear relationship was observed, confirming that the more severe the periodontitis was, the higher the likelihood of having caries. Based on the classification of caries between coronal and root caries, the study found that patients with periodontitis had an elevated likelihood of having root caries.

This systematic review also attempted for the first time to provide estimates of the mean DMFT and DFR in periodontitis patients. Interestingly, 50% of the included studies reported an increased DMFT in patients with periodontitis. However, all of the included studies showed an elevated mean DFR in patients with periodontitis. The association between periodontitis and root caries observed in this study was in agreement with previous studies [ 44 ], in which periodontitis was correlated with the prevalence of root caries.

In terms of the periodontal condition of patients with caries, patients also had a higher (79%) risk of having periodontitis compared to controls. This result was in line with the findings of the study [ 29 ], which showed that people with three or more untreated carious lesions were more likely to develop periodontal disease. The explanation for this phenomenon may be that untreated carious lesions may increase plaque retention and susceptibility to periodontal disease [ 45 ]. This was observed in a 3-year longitudinal study in which untreated carious lesions in adolescents had a significant negative impact on periodontal health [ 46 ].

The positive correlation between the two diseases can be explained by common risk factors, including oral hygiene practices [ 47 ], the presence of dental biofilms, lifestyle habits [ 48 ] and social factors [ 49 ]. Dental plaque biofilms, which are the initiators of caries and periodontitis, constituted distinct polymicrobial communities. With dynamic changes in community composition, microbial metabolism and by-products that triggered host immune responses can cause changes in the local environment, thereby affecting the growth or colonization of single or multiple microbial populations, promoting the growth or over-activity of microorganisms associated with ecological maladies, and thus influencing the development of the disease [ 17 ].

In the same oral environment, the positive correlation between caries and periodontitis was found to be closely related to its oral hygiene environment. Jiang et al. [ 50 ] analyzed the periodontal status of 86 pairs of carious teeth and found that the degree of gingival recession, the depth of periodontal probing, and calculus index were significantly higher in the carious group than in the no-caries group, regardless of whether they suffered only from crown caries or crown-root caries. This may be due to the fact that caries tends to occur on tooth surfaces that are not easily cleaned and where food debris tends to linger, and these areas are often where plaque tends to accumulate. As caries progressed, pain from food embedded in the cavity or temperature stimulation can occur, leading patients to unconsciously avoid these sensitive areas when brushing and rinsing, which in turn made plaque and calculus more likely to accumulate. This provided a favorable environment for the survival of periodontal pathogenic bacteria, leading to the development of periodontitis. Similarly, periodontitis caused the gums to recede, leading to root exposure. Due to the temperature sensitivity of the exposed roots, the teeth on the affected side were disused and prone to food debris accumulation. At the same time, the lack of periodontal tissue allowed for increased loss of teeth in severe periodontitis, resulting in larger gaps between the teeth, which made it easier for food to become embedded. The environment of the affected side favored the growth and metabolism of cariogenic bacteria to form caries. Therefore, the close relationship between oral hygiene, plaque biofilm and the two diseases can be used to support the positive correlation between caries and periodontitis.

In particular, one of the explanations for the strong association between periodontitis and root caries was that the exposure of cementum/dentine due to recession caused by PD may provide a new substrate for bacterial adhesion, and favor colonization of Gram-negative proteolytic species that can degrade the endogenous collagen and other organic components of these tissues [ 51 ]. Furthermore, bacteria metabolized sugar into organic acids, which initiated root surface demineralization by removing calcium and phosphate ions from surface apatite crystals. For enamel, this process took place as the pH reached the critical value of 5.5; however, pH 6.4 was sufficient for cementum and dentin demineralization, due to their lower degree of mineralization [ 52 ] as well as limited amount of fluoride and poorer caries resistance than enamel. Traditional periodontal treatment reducing the resistance to caries may be another reason that mechanical removal of plaque, scaling and root planning affected the outer part of the root surface and exposed cementum and dentin surfaces with low caries resistance [ 42 ]. In addition, gingival recession enables saliva to access the root surface, which is believed to be one of the most important host factors and an essential mediator controlling the speed and direction of the cariogenic pathway. Saliva was shown to neutralize the pH level on the root surface by a salivary buffering action and changed nutritional conditions, affecting the environment and nutrients of bacteria [ 13 ].

Apart from the correlation at the clinical level, this study also summarized the correlation between periodontitis and caries-related causative organisms. The results showed that patients with periodontitis had decreased salivary levels of S. mutans, one of the main caries-causing organisms, compared to those without periodontitis. Besides, several studies demonstrated that patients with high salivary levels of S. mutans (> 10 5  CFU/ml) had reduced mean attachment loss and the number of sites with attachment loss > 4 mm. These results could be explained by the fact that periodontitis mediates changes in the microecological community, leading to increased inflammation and alterations in the microbiological composition of oral biofilms [ 53 ]. Some of these reports suggested an antagonistic relationship between bacteria associated with the oral microbiota of both diseases. Numerous clinical trials have confirmed a negative correlation between P. gingivalis and S. mutans both in subgingival plaque and saliva before and after systemic treatment of periodontal disease patients [ 39 ]. Different degrees of antagonism were found between the two bacteria when they were co-cultured, in which P. gingivalis was found to interfere with the quorum sensing (QS) system of S. mutans, thereby diminishing its biofilm-forming ability, acid tolerance, and bacteriocin-producing ability [ 54 ]. However, some studies [ 37 ] have also shown a positive correlation between other periodontal pathogens such as F. nucleatum, Actinobacillus and S. mutans, which may be mediated by the co-aggregation of F. nucleatum [ 55 ] and the high genetic variability of Actinobacillus strain [ 56 ]. Therefore, further long-term cohort and elementary research on the bacterial association between periodontitis and caries should be carried out, in order to better understand the pathogenetic relationship between the two diseases, which can facilitate innovative strategies for the prevention and treatment of these two diseases with highly prevalence.

In this systematic review, for the first time, we conducted the correlation between PD and dental caries at the clinical and bacterial levels. Interestingly, either periodontitis or caries as independent variable, the correlation between the two diseases was positive and significant. In addition, the pooled results of all the included studies indicated that patients with periodontitis had a higher DMFT/DFR index. However, at the bacterial level, there seemed to be a negative correlation between the levels of the main dominant pathogenic bacteria related to two diseases in the same microecological environment. Studies have shown that environmental acidification was the main cause of phenotypic and genotypic changes in the microbiota during the development of dental caries [ 57 ], with S. mutans and Lactobacillus being identified as specific caries pathogens [ 11 ]. However, S. mutans was not only present at high levels in the early stages of dental caries, but also in healthy humans, even at very low levels [ 58 , 59 ]. This seemingly contradictory results seemed to explain the trend related to the clinical and bacterial dimensions in this study, where both caries and periodontitis were based on a multiple microbiological community and a variety of other factors influencing the course of the disease, rather than being determined by a single or a select few bacterial species [ 4 , 60 ]. In other words, dysbiosis contributed to diseases means an alteration in the abundance or influence of individual species within the polymicrobial community, relative to their abundance or influence in health. Whereas dysbiosis can lead to destructive periodontal inflammation, the reverse is also true. In this respect, inflammatory tissue breakdown products such as degraded collagen and heme-containing compounds are released into the gingival crevicular fluid. In the gingival crevice/pocket, these inflammatory spoils can be used as nutrients to fuel the selective expansion of a subset of bacterial species like proteolytic and asaccharolytic pathobionts, thereby exacerbating the imbalance of the dysbiosis [ 61 , 62 , 63 ]. In oral ecosystems, the proximity of microorganisms promoted a series of biochemical interactions that may be favorable to one organism and antagonistic to the other. Therefore, further studies combining clinical and microbiological measures of both these diseases are necessary for better understanding of the possible relationship between these two diseases which may lead to improved management and prevention.

Strengths and weaknesses

This systematic review was designed to comprehensively investigate the trends between caries and periodontitis. As a result, the bidirectional relationship between dental caries and periodontitis both in clinical and bacterial levels was first analyzed, and more precise conclusions through subgroup analysis were drawn. Meanwhile, this study analyzed the DMFT/DFR index of patients with periodontitis for the first time, and especially the strong correlation with root caries was important for the treatment and prevention of patients with periodontitis. For example, the inclusion of caries prevention treatment in periodontal sequence therapy or the adjustment of the follow-up time for patients with severe periodontitis was important in PD therapy. Furthermore, the correlation between two diseases at the bacterial level suggested that symbiosis or antagonism between one or several pathogenic bacteria may not directly determine the progression of the disease, but may also cause the disease through affecting the oral microecological balance, bacterial quorum sensing (QS) mechanism and host-community interaction. Therefore, further research in identifying the interplay between pathogens in each individual and their relative contribution on each other is needed. However, a number of limitations should be highlighted starting with the limited value of this systematic reviews of observational studies for ascertaining causality. Moreover, the definition of periodontitis in several studies seemed a slight discrepancy as well as bias in clinical examination, which may lead to selection bias. Not all included studies were adjusted for potential confounders such as diet, smoking habit and socio-economic level. The true value could be distorted by these confounding factors. Further intervention studies and long-term are needed to establish this causal relationship. What’s more, Due to the limited number of included studies and subjects, no further subgroup analysis between sexes or ages were performed.

This study demonstrated that both of two disorders were linked by two-way relationships. Especially, the risk of dental caries was increased by the severity of periodontitis. The presence of periodontitis was also associated with an increased DMFT and DFR. At the bacterial level, however, the pathogens associated with two disorders were negatively correlated. Our findings highlighted the necessity to improve caries prevention during periodontal treatment. Longer and larger studies are needed however to determine whether periodontal treatment with concomitant caries management facilitates disease therapy and prognosis, and how intrinsic bacterial interactions or immune regulation affect the disease, ultimately resulting in reduced morbidity.

Data Availability

The authors confirmed that the data supporting the findings of this study were available in Table  1 of this manuscript and supplementary materials.

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This work was supported by the Science and Technology Innovation Project for postgraduate of Hubei University of Medicine (No. YC2023055), the Scientific Research funding Project of Hubei Provincial Department of Education (No. Q20222114), the Scientific Research guiding Project of Hubei Provincial Department of Education (No. B2022499) and the Scientific Research Project of Shiyan Taihe hospital (No. 2023JJXM038).

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Yixin Li, Yonggang Xiang and Haixia Ren have contributed equally to this work and share first authorship.

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Department of Stomatology, Taihe Hospital, Hubei University of Medicine, Shiyan, 442000, Hubei, China

Yixin Li, Haixia Ren, Ziqiu Hu, Weidong Leng & Lingyun Xia

Department of Ophthalmology, Taihe Hospital, Hubei University of Medicine, Shiyan, 442000, Hubei, China

Yonggang Xiang

Center for Evidence-Based Medicine and Clinical Research, Taihe Hospital, Hubei University of Medicine, Shiyan, 442000, Hubei, China

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Y.Li, Y.Xiang and H.Ren contributed to methodology, data analysis and interpretation, and to manuscript drafting. C.Zhang and Z.Hu contributed to conceptualization, Investigation, Software and reviewed the manuscript. W.Leng and L.Xia contributed to study conception and design, data analysis and interpretation, and critically revised the manuscript. All the authors gave their final approval of the version to be published and agreed to be accountable for all aspects of the work.

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Li, Y., Xiang, Y., Ren, H. et al. Association between periodontitis and dental caries: a systematic review and meta-analysis. Clin Oral Invest 28 , 306 (2024). https://doi.org/10.1007/s00784-024-05687-2

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Clinical application of high-resolution spiral CT scanning in the diagnosis of auriculotemporal and ossicle

• Qinfang Cai 1 , 3 ,
• Peishan Zhang 3 ,
• Fengmei Xie 3 ,
• Zedong Zhang 3 &

BMC Medical Imaging volume  24 , Article number:  102 ( 2024 ) Cite this article

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Precision and intelligence in evaluating the complexities of middle ear structures are required to diagnose auriculotemporal and ossicle-related diseases within otolaryngology. Due to the complexity of the anatomical details and the varied etiologies of illnesses such as trauma, chronic otitis media, and congenital anomalies, traditional diagnostic procedures may not yield accurate diagnoses. This research intends to enhance the diagnosis of diseases of the auriculotemporal region and ossicles by combining High-Resolution Spiral Computed Tomography (HRSCT) scanning with Deep Learning Techniques (DLT). This study employs a deep learning method, Convolutional Neural Network-UNet (CNN-UNet), to extract sub-pixel information from medical photos. This method equips doctors and researchers with cutting-edge resources, leading to groundbreaking discoveries and better patient healthcare. The research effort is the interaction between the CNN-UNet model and high-resolution Computed Tomography (CT) scans, automating activities including ossicle segmentation, fracture detection, and disruption cause classification, accelerating the diagnostic process and increasing clinical decision-making. The suggested HRSCT-DLT model represents the integration of high-resolution spiral CT scans with the CNN-UNet model, which has been fine-tuned to address the nuances of auriculotemporal and ossicular diseases. This novel combination improves diagnostic efficiency and our overall understanding of these intricate diseases. The results of this study highlight the promise of combining high-resolution CT scanning with the CNN-UNet model in otolaryngology, paving the way for more accurate diagnosis and more individualized treatment plans for patients experiencing auriculotemporal and ossicle-related disruptions.

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Introduction

Today, otolaryngology, the specialist area dedicated to the comprehensive evaluation of ear, nose, and throat ailments, grapples with a significant challenge [ 1 ]. It’s at a turning point in its quest for accurate and in-depth knowledge of middle ear disorders related to the auricle, temporal bone, and ossicles [ 2 ]. Due in large part to the complex anatomical components of the middle ear, diagnosing these conditions can be a challenging jigsaw puzzle [ 3 ]. Despite their value, current diagnostic approaches frequently fail to provide complete diagnoses, highlighting the pressing need for a game-changing alternative [ 4 ]. One of the most significant difficulties in otolaryngology stems from the middle ear’s complex anatomy. While conventional methods of diagnosis can be helpful in many scenarios, they often need to improve when trying to decipher issues involving the auriculotemporal region and the ossicles [ 5 ]. Due to the complexity of these disorders and the wide variety of their causes (which can range from trauma to chronic otitis media to congenital anomalies), a thorough and multi-pronged approach is required for an appropriate diagnosis [ 6 ].

The primary worry with CT scanning right now is the level of radiation exposure it exposes patients to in everyday clinical scenarios; however, with the implementation of CT technology, this problem will go away [ 7 ]. Future CT imaging evaluations of patients in all clinical contexts will be more robust and trustworthy because to a mixture of dual-energy appropriation, X-ray dose reduction, and acquisition time velocity implementation methods.

Cone beam computed tomography scans have little direct dangers. Some examples include allergic responses, nephritis, and the potential for radiation-induced cancer in the long run. There are other factors to think about, such as whether or not the patient is pregnant and the potential effects of radiation on the unborn child.

Computed tomography (CT) pictures [ 8 ] inevitably contain noise since all measurements of substance are subject to statistical error. Consequently, to improve the quality of CT images, edge-preserving denoising techniques are necessary. Noise reduction and the retention of genuine medically relevant contents are not mutually exclusive, though.It is possible to minimize or eliminate noise in CT images during the reconstruction process by employing suitable denoising filters. Consequently, denoising is necessary to enhance picture quality for better diagnosis.

Recent studies have investigated several obstacles and emerging areas in medical imaging, including the resolution of CT image noise and the creation of novel denoising algorithms to enhance image quality and diagnostic precision [ 9 ]. Several novel methodologies have been suggested for merging multimodal medical images, focusing on safeguarding data privacy and security [ 10 ]. CT scans can benefit from advanced denoising approaches, such as edge-guided filtering and collaborative feature representation networks, which have demonstrated potential in reducing noise and maintaining edge details, improving interpretability [ 11 ]. Another potentially effective method involves utilizing convolutional neural networks and fractional order total generalized variation algorithms for multimodal picture fusion and denoising in Non-Subsampled Contourlet Transform [ 12 ]. These strategies aim to address the constraints associated with particular modalities and improve the overall diagnostic efficacy of medical imaging data by using data from other imaging modalities.

This study is driven by a number of separate motivations, primarily driven by the urgent need for accurate and all-encompassing diagnostic strategies to deal with the intricacies of auriculotemporal [ 13 ] and ossicle-related disorders [ 14 ]. The first compelling force is the immediate requirement for thorough and precise diagnostic methods for auriculotemporal [ 13 ] and ossicle-related diseases [ 14 ]. These diseases frequently pose perplexing puzzles, prompting patients and medical professionals to search for better diagnostic techniques [ 15 ]. Second, there is a promising new way to deal with these diagnostic difficulties due to the development of High-Resolution Spiral Computed Tomography scanning and Deep Learning Techniques (HRSCT-DLT). This research aims to use HRSCT-DLT to advance otolaryngology by overcoming current diagnostic constraints and providing new levels of precision and insight. This research represents a paradigm change that has the potential to rethink the current system of diagnosis for disorders affecting the temporal and auricular bones. The high-resolution spiral CT scanning technique is renowned for its exceptional spatial resolution and capacity to image intricate bony structures within the temporal bone effectively. In contrast, Magnetic Resonance Imaging (MRI) offers enhanced soft tissue contrast and is frequently used to assess soft tissue pathology in the middle ear and surrounding anatomical regions. Using ionizing radiation in CT scanning raises potential concerns, particularly for pediatric patients or persons requiring recurrent imaging. MRI, as a non-ionizing modality, presents a more secure alternative. MRI can offer valuable functional information, such as dynamic imaging of the eustachian tube or evaluation of cochlear implants, which may not be attainable only by CT scanning. Temporal bone X-ray provides a rapid and economical initial assessment. Still, it may not provide the information required for a thorough review compared to CT or MRI.

The study’s central tenet is to improve diagnostic accuracy and efficiency by combining High-Resolution Spiral Computed Tomography (HRSCT) [ 16 ] scanning with Deep Learning Techniques (DLT) [ 17 ]. Incorporating the CNN-UNet deep learning method, which has been fine-tuned to perform exceptionally well in catching the finest distinctions inside medical images, is central to this groundbreaking method. This integration of cutting-edge science and medical practice gives doctors and researchers access to diagnostic technologies that promise previously unattainable levels of understanding [ 16 ]. It is clear that the union of medical imaging and deep learning has transformative potential, and this combined strategy has the potential to take patient care to new heights [ 18 ]. The primary goals of this study cover a wide range of topics. This research further advances diagnostic capabilities by investigating the complementary nature of the CNN-UNet model with high-resolution CT images [ 19 ]. Several essential tasks, such as ossicle segmentation, fracture identification, and disruption cause categorization, characterize this investigation [ 20 ]. This study aims to improve the speed and accuracy of clinical decision-making by automating these processes.

This study’s main contribution is.

To develop a state-of-the-art diagnostic framework for automated, precise evaluation of auriculotemporal and ossicular disorders based on the HRSCT-DLT model, improving diagnostic accuracy and clinical insight in otolaryngology.

To automate crucial diagnostic activities such as ossicle segmentation, fracture detection, and disruption cause categorization using the CNN-UNet deep learning model within the HRSCT-DLT framework for improved efficiency and accuracy in diagnosis.

To assess the HRSCT-DLT model’s clinical effects, validate the framework’s efficacy, and pave the way for future research and advancements, this will serve as a standard for successfully incorporating cutting-edge technology into medical diagnosis.

The remainder of the article is structured as follows: Sect.  2 examines the results and limitations of several research studies in the field. In Sect.  3 , the suggested methodology and its underlying architecture are described in detail. Section  4 presents the experimental results and discusses our study’s outcomes. Section  5 concludes the paper.

Literature survey

Segmentation of ct scans of the temporal bone.

Three groups of researchers have made contributions to the process of segmenting CT images of the temporal bone: Neves et al. [ 21 ], Li et al. [ 22 ], and Ke et al. [ 23 ]. To segment otologic components such as the cochlea, ossicles, facial nerve, and sigmoid sinus, Neves et al. created a CNN-based automated approach that produced very accurate results. Using promising efficacy measures, Li et al. presented a 3D-DSD Net to segment important anatomical organs. A convolutional neural network (CNN) model was developed by Ke et al. for automatic segmentation in adults and children. The model demonstrated remarkable performance for various spatial features of the temporal bone. Error analysis, misclassification, and the creation of user-friendly interfaces are all areas that still have space for development despite the progress made.

Deep learning in ear disease diagnosis

Many researchers, including Fujima et al. [ 24 ], Wang et al. [ 25 ], Khan et al. [ 26 ], and Erolu et al. [ 27 ], have focused their attention on the utilization of deep learning in the diagnosis of a variety of ear problems. One group, Fujima et al., researched using deep-learning analysis to diagnose otosclerosis. In contrast, another group, Wang et al., developed a deep-learning technique for diagnosing middle ear problems that are persistent. The researchers Khan et al. and Erolu et al. examined the ability of artificial intelligence modelling to differentiate between individuals with chronic otitis media who had cholesteatoma and those who did not. Khan et al. revealed a novel usage of CNNs for diagnosing tympanic membrane and middle ear infections. The findings of these studies emphasize the promise of artificial intelligence in diagnosing ear diseases but also indicate the necessity of conducting additional studies in areas such as generalizability, clinical impact, and data variety.

Diagnostic tools and techniques

Different diagnostic tools and methods are presented by Duan et al. [ 28 ], Jeevakala et al. [ 29 ], and Diwakar et al. [ 30 ] to distinguish and locate particular ear disorders. Duan et al. researched whether deep learning might be used as a diagnostic tool to differentiate between otitis media caused by OME and OM caused by PCD. Jeevakala and colleagues developed an automatic method to find and isolate the internal auditory canal (IAC) from the nerves that supply it. Diwakar et al. presented a method combining wavelet packet-based thresholding with a non-local means (NLM) filter for better edge preservation. The findings of this research demonstrate the significance of artificial intelligence in assisting radiologists in generating accurate diagnostic decisions. However, they also highlight the need for more clinical validation, generalizability testing, and optimizing interpretability.

Perspectives from research on otosclerosis and dentistry

Asavanamuang et al. [ 31 ] suggested utilizing CBCT, or cone-beam computed tomography, to examine radiographic features associated with pre-eruptive interstitial resorption (PEIR) in teeth that have not yet erupted. The objectives of this study are to ascertain the prevalence of PEIR and its relationship to the angulation, location, and pericoronal space of teeth. Results point to the prevalence of PEIR in particular tooth orientations, highlighting the significance of CBCT monitoring, especially for molars. Silva et al. [ 32 ] described a systematic review that aims to offer evidence-based guidelines for the diagnosis and management of otosclerosis. Members of the task force receive training in knowledge synthesis techniques, and they evaluate literature to provide recommendations on treatment (such as surgery, medication, hearing aids, and implantable devices) and diagnosis (including audiologic and radiologic) based on predetermined parameters.

The study developed a state-of-the-art diagnostic framework for automated, exact evaluation of auriculotemporal and ossicular abnormalities using the HRSCT-DLT model, enhancing otolaryngology diagnostic accuracy and clinical insight. Optimize diagnosis efficiency and accuracy by automating ossicle segmentation, fracture identification, and disruption cause categorization using the CNN-UNet deep learning model in the HRSCT-DLT framework. This will set a precedent for effectively integrating cutting-edge technology into medical diagnostics by assessing the HRSCT-DLT model’s clinical impacts, validating the framework, and enabling future research and developments.

Medical imaging aims to detect and track healthy and diseased bodily structures and functions by creating three-dimensional models of individual organs and tissues. Various medical imaging modalities are utilized for this aim, including X-ray, CT, PET, MRI, digital mammography, diagnostic sonography, and many more. Cardiovascular diseases, cancer of various tissues, neurological problems, congenital heart conditions, complications in the abdomen, complicated broken bones, and many other significant illnesses can be better diagnosed with the use of these cutting-edge medical imaging tools. Any kind of imaging has its advantages and disadvantages. Two main approaches exist for temporal skeleton computed tomography (CT) accumulation: a dual intake with independent bilateral axial and panoramic scans or a single axially recorded volume with coronal and if desired, sagittal reorganizes applied to the longitudinal source data. While contrast medication can be useful in certain cases, including when looking for otomastoiditis issues, vascular tumors, or vascular anomalies, it is usually not needed for routine evaluations of coalescence, mastectomy air cell death, or hearing loss. Because of CT’s superior contrast compared to traditional hypocycloidal tomography, traumatic ossicular disturbances may now be seen. Additionally, congenital anomalies of the stapes’s framework can be better seen.

The proposed HRSCT-DLT model symbolizes a harmonious merger of high-resolution spiral CT scanning and the CNN-UNet model. This union is designed to address the nuances of auriculotemporal and ossicular disorders. It is not only a shortening of the diagnostic procedure; it marks an ascension in our grasp of these delicate situations, defining the boundary of medical imaging and diagnostics. This research intends to help doctors make more accurate diagnoses by highlighting the possibilities of combining high-resolution CT scans [ 33 ] with the CNN [ 34 ] and UNet [ 19 ] models in otolaryngology. In addition, this method facilitates the development of patient-specific treatment plans for auriculotemporal and ossicular disorders. The ultimate goal is for this game-changing strategy to transfer to better patient outcomes and a higher general level of care. The research has the potential to herald a new era of precision and quality in otolaryngology through its careful path of discovery, customization, and application. The study developed a state-of-the-art diagnostic framework for automated, exact evaluation of auriculotemporal and ossicular abnormalities using the HRSCT-DLT model, enhancing otolaryngology diagnostic accuracy and clinical insight. Optimize diagnosis efficiency and accuracy by automating ossicle segmentation, fracture identification, and disruption cause categorization using the CNN-UNet deep learning model in the HRSCT-DLT framework. This will set a precedent for effectively integrating cutting-edge technology into medical diagnostics by assessing the HRSCT-DLT model’s clinical impacts, validating the framework, and enabling future research and developments (Table  1 ).

This literature review investigates otolaryngology and otologic imaging analysis, focusing on applying deep learning approaches, particularly CNNs. CT scans of the temporal bones have been segmented automatically using CNNs, with impressive results in accuracy and overlap with the human ground truth. The studies stress the significance of user-centred design, mistake detection and correction, clinical validation, data variance, and interpretability. Otosclerosis, chronic middle ear illnesses, tympanic membrane and middle ear infections, and differentiating between comorbidities caused by OME and PCD are all successfully diagnosed using deep learning algorithms. The study suggests combining high-resolution spiral CT scanning with deep learning techniques (HRSCT-DLT) for effective and trustworthy diagnosis of auriculotemporal and ossicle-related disorders.

Propoced system model

The proposed research intends to change otolaryngology by increasing the accuracy and efficiency of diagnostic procedures by combining High-Resolution Spiral Computed Tomography scanning with Deep Learning Techniques (HRSCT-DLT). The CNN-UNet deep learning model is at the heart of this groundbreaking method, and it has been fine-tuned to excel in capturing minute details in medical photos. This integration of cutting-edge science and medical practice gives doctors and researchers access to diagnostic technologies that promise previously unattainable levels of understanding.

With High-Resolution Spiral Computed Tomography scanning and the CNN-UNet deep learning model, the HRSCT-DLT model helps doctors and scientists capture sensitive data in medical images. This method can improve the treatment of patients and diagnostic time by automating ossicle categorization, fracture diagnosis, and disruption cause categorization. Automation of clinical decision-making improves diagnosis accuracy and reduces medical staff workload. Cutting-edge medical imaging and diagnostics tools like the HRSCT-DLT model help clinicians make accurate diagnoses and customize patient care.

Figure  1 portrays the system architecture of the suggested HRSCT-DLT approach.

HRSCT-DLT system model

Figure  1 shows how the HRSCT-DLT plans to revolutionize otolaryngology by making diagnostics more precise and faster. With a major focus on auriculotemporal and ossicular illnesses, this study will develop a database of CT scans of the temporal bones. The collection contains detailed information about the middle ear’s anatomy, acquired using HRSCT imaging. The accuracy of the deep learning model relies heavily on the annotations provided by medical professionals. These experts separate relevant data into its component parts, such as ossicles, fracture sites, and disruption triggers. When working with medical picture data, data preparation is absolutely necessary. Prior to training your Convolutional Neural Network-UNet (CNN-UNet) model, you must conduct data preprocessing on your High-Resolution Spiral Computed Tomography (HRSCT) scans using deep learning techniques. The fundamental objective of these rigorous preparation steps is to meticulously get your data ready for the next CNN-UNet model training. The CNN-UNet model excels at precise segmentation in medical images, which are particularly useful for depicting the intricate anatomy and subtle disorders affecting the middle ear. For the best results in picture segmentation, try using the CNN-UNet technique, which combines CNN with U-Net. When using high-resolution CT data to segment anatomical components in the middle ear, the HRSCT-DLT model relies heavily on CNN-UNet. Because of its well-calibrated convolutional layers, the CNN-UNet model is able to pick up on the tiniest of anatomical details.

Data collection and preparation

A database of CT scans of temporal bones will be created for this study, with a primary focus on auriculotemporal and ossicular disorders. Detailed anatomical information regarding the middle ear may be found in the dataset, which was gathered via HRSCT imaging. Congenital abnormalities, concussions, and recurrent ear infections are just some of the many medical conditions addressed. The purpose of this comprehensive dataset is to simplify the field of otolaryngology by illuminating all aspects of auriculotemporal and ossicle-related illnesses. Since it encompasses such a large and comprehensive dataset, this study is ideal for tackling the complexity and nuances that drive the discipline of otolaryngology since it offers a bird’s-eye view of auriculotemporal and ossicle-related problems.

Data annotation

Medical experts’ annotations are crucial to the performance of the deep learning model. These specialists isolate and define data subsets of interest, such as ossicles, fracture locations, and disruption triggers. The ground truth labels provided by these annotations are crucial to the success of the deep learning procedure. The CNN-UNet model requires these labels for thorough training and validation. Using these comparisons, the deep learning model can be trained to become a reliable diagnostic tool in the context of the study.

Data preprocessing

Data preprocessing is essential in preparing your data, especially in medical image analysis. Auriculotemporal and ossicular illnesses will be the focus of this study’s temporal bone CT scan database. The collection contains HRSCT-imaged middle ear anatomy. Congenital defects, concussions, and recurring ear infections are among the medical issues treated. This comprehensive dataset simplifies otolaryngology by revealing all auriculotemporal and ossicle-related diseases. This study provides a s-eye view of auriculotemporal and ossicle-related issues. It is perfect for confronting the complexity and nuances that drive otolaryngology due to its big and thorough dataset. Using deep learning techniques with High-Resolution Spiral Computed Tomography (HRSCT) scans, you must first perform some preprocessing to get the data into shape for your Convolutional Neural Network-UNet (CNN-UNet) model. Consider the following pre-analysis steps for your data:

Data Cleaning : Noise in high-resolution medical images like CT scans can have many causes, including human error, faulty equipment, and the surrounding environment. Diagnostic accuracy and image analysis precision are both susceptible to noise. Gaussian and other noise reduction filters can reduce background noise without losing valuable diagnostic information. Images obtained from CT scanners can benefit from these filters’ improved clarity and resolution.

Image Resizing : Reduce the images’ size until they fit your CNN-UNet’s criteria. Computing-intensive high-resolution scans can benefit from scaling, which also helps to standardize the data. The new pixel values in the scaled image are determined using a weighted average of surrounding pixels from the original image due to the resampling technique of bilinear interpolation. This method lowers the image’s size without degrading its overall quality.

Histogram Equalization : This method can improve the contrast of medical images by shifting the relative brightness of individual pixels. Enhancing the clarity of finer details is one area where it can be beneficial. High-resolution CT scan images can improve their contrast and overall visual quality with the help of histogram equalization. Contrast Limited Adaptive Histogram Equalization (CLAHE) is a widespread method for histogram equalization. CLAHE improves classic histogram equalization since it accounts for regional differences within an image, making it ideal for diagnostic tools like CT scans.

Noise Reduction : Noise in medical images might degrade the quality of any subsequent analysis. Reduce noise with methods like median filtering and Gaussian smoothing. Non-local means (NLM) Denoising is an efficient method for reducing noise in high-resolution CT scan pictures. The NLM approach is frequently utilized in the medical imaging processing industry to reduce noise while maintaining image features.

Cropping : Cropping images to isolate the area of interest can simplify processing by removing extraneous data. Manual or semi-automated region-of-interest (ROI) selection is typical for cropping high-resolution CT scan pictures. A radiologist or other medical professional analyzes the image to pinpoint the location of any pathology or anatomical structures of interest.

The primary goal of such stringent preprocessing processes is to methodically prepare your data, setting a firm groundwork for the upcoming training of the CNN-UNet model. Ossicle segmentation, fracture identification, and disruption cause categorization are complex and diverse procedures requiring meticulous data preparation in auriculotemporal and ossicle-related disorders. These steps in preparation guarantee that the data is polished to perfection, ready to provide the best training and validation results possible for the model.

CNN-UNet model development

The proposed methodology centres on the creation and refinement of the CNN-UNet deep learning model, which is essential to the diagnostic framework of the research. Medical images, such as those showing the complex anatomy and subtle diseases of the middle ear, are ideal candidates for the CNN-UNet model’s exact segmentation. The CNN-UNet model’s greatest asset is its well-calibrated convolutional layers, which allow it to catch even the minutest anatomical information. Due to its complexity and relative fragility, medical imaging analysis of the middle ear is of the utmost relevance. The CT scans can reveal even the tiniest of abnormalities, but the convolutional layers were built with sensitivity to ensure nothing was overlooked. CNN-UNet’s training phase is rigorous to ensure the model is up to the task of recognizing and segmenting important structures inside CT scans, and this step is crucial to the model’s eventual success. The use of the meticulously documented dataset facilitates this procedure. During training, the model absorbs information from the dataset to improve its knowledge and ability to identify target areas inside images. The model improves at detecting and outlining critical structures through this iterative learning process, making it more useful for precise diagnosis.

The CNN-UNet model is a sophisticated deep-learning technique capable of precisely delineating ossicles, which are small and fragile bones located in the middle ear. This process dramatically aids in the detection and examination of anomalies or disorders. Additionally, it can accurately identify fractures in the temporal bone, which is vital for auditory function and overall well-being. The proposed model employs deep learning techniques to evaluate CT images and effectively identify regions that suggest fractures. It enables doctors to focus on these specific locations for subsequent assessment. Additionally, it aids in categorizing the reasons for disruption in the temporal bone, which can arise from factors such as trauma, infection, or congenital anomalies. This information assists healthcare professionals in making precise diagnoses and developing personalized treatment strategies. The incorporation of the CNN-UNet model into high-resolution CT images improves the effectiveness and precision of diagnostic procedures in the field of otolaryngology. This integration automates several activities: segmentation, fracture identification, and categorizing disruption causes. This novel methodology enables healthcare professionals to make well-informed choices that maximize patient results.

Convolutional neural network model

The visual data processing and analysis tasks that CNNs, a subset of deep neural networks, excel at include image classification, segmentation, and object detection. CNNs are excellent at jobs involving patterns, such as those observed in medical imaging, since they are made up of layers that automatically acquire features from the data. CNNs play the role of feature extractors in the HRSCT-DLT framework. They perform an in-depth analysis of the provided CT scans, deciphering essential patterns and structures that are fundamental to grasping the complex anatomy of the middle ear. Edges, textures, forms, and spatial interactions between image components are all potential candidates for such patterns. Consider the CT picture \(X\) to be the input. Convolutional neural networks (CNNs) examine \(X\) as an input image and extract features \(F\) that describe salient aspects of the image. This operation can be mathematically expressed by Eq. ( 1 ).

The extracted features are denoted by \(F\) , while CNN indicates the Convolutional Neural Network. Convolutional layers are the building blocks of CNNs, and they use a set of learnable filters or kernels to process the input image. To make things easier, this study refers to the input image ( \(X\) ) and the convolution procedure ( \(*\) ). The filters are portrayed as \(K\) (kernels), while the output feature maps are denoted as \(Y\) . The mathematical expression for this convolution is shown in Eq. ( 2 ).

Here, \(X\) is the input picture, \(Y\) are the feature maps, and \(K\) are the convolutional kernels. Sliding the kernels about the input image systematically is what the convolutional process does to pick up on local patterns like edges and corners. It is common practice to downsample the data using pooling layers following the convolutional layers. For example, max-pooling takes a small area within each feature map and picks its maximum value. Equation ( 3 ) is a symbolic illustration of the pooling process.

Here, \(Y\) is the feature map after downsampling, and Max-Pool is the maximum pooling operation. Each convolutional layer generates feature maps, which represent various picture features. These feature maps stand in for data abstractions. Equation ( 4 ) provides an algebraic model for a layer with \(N\) feature mappings.

An \({i}^{th}\) the variable denotes the feature map \({F}_{i}\) . The HRSCT-DLT model uses convolutional neural networks (CNNs) to segment middle ear anatomy. The CNN’s learned characteristics form the basis for the segmentation procedure. As shown in Eq. ( 5 ), the input CT image \(X\) generates the segmented output \(S\) .

Where \(S\) is the image after segmentation, and the convolutional neural network (CNN) employed for segmentation is denoted here as \(Segmentation-CNN\) . Training a CNN takes a lot of time and labelled data. Training the HRSCT-DLT model requires the use of labelled data. To make things easier to understand, we’ll refer to the annotated dataset as \(D=({X}_{i},{Y}_{i})\) , where \({X}_{i} ,\) is the input CT image, and \({Y}_{i}\) , are the ground truth labels identifying the location of structures. Using a loss function (typically represented by the letter L) during training is common practice to decrease the gap between the model’s predictions and the truth. This method fine-tunes the model to generate the correct segmentations, as indicated in Eq. ( 6 ).

Here, \(\theta\) stands for the original model parameters, L for the loss function, and * for the optimal set of values. The CNN can make inferences about novel, unseen CT images following training. It accepts an image as input and produces a segmented output focusing on specific features (such as ossicles or fractures) inside that picture. The segmented output produced by CNN supports healthcare practitioners in making diagnostic decisions. It expedites clinical care by improving accuracy and efficiency through automated examination of critical anatomical structures and diseases.

CNN-UNet algorithm in HRSCT-DLT framework

The CNN-UNet strategy is a CNN and U-Net hybrid optimized for image segmentation. CNN-UNet is critical in the HRSCT-DLT model for segmenting middle ear anatomical structures from high-resolution CT data. The U-shaped design of the U-Net design is a defining feature of the encoder and decoder. The encoder downsamples the input image to capture relevant components; the decoder then upsamples these features to produce the segmentation map. The CNN-UNet starts by operating as a feature extractor. As input, it accepts high-resolution CT images, such as those of the temporal bone. The U-Net uses convolutional layers to process the input image in the encoder, which is a convolutional neural network. These layers identify specific details, structures, and patterns in the image. Let’s call this first step in the process “feature extraction,” and let’s refer to the input image as \({I}_{in}\) , in Eq. ( 7 ).

Here, \({F}_{cnn}\) , stands for CNN’s gleaned feature maps. These feature maps represent small-scale variations in the input image’s overall structure, colour, and texture. The image’s spatial dimensions are decreased while the encoder’s feature channel count rises. The encoder’s successive layers can record increasingly abstract characteristics. Convolutional layers using max-pooling or strided convolutions accomplish this. Let’s use Eq. ( 8 ) to represent the encoding procedure.

High-level feature maps are encoded and stored in the variable \({E}_{cnn}\) . The model takes the most essential features from the U-Net’s bottleneck and keeps their high-level representation. Equation ( 9 ) is a graphical representation of the bottleneck property.

The U-Net’s decoder starts upsampling the bottleneck’s high-level characteristics. Upsampling raises the number of spatial dimensions, enabling the identification of features inside an image that may be described using the formula (10).

Where, \({D}_{cnn},\) is a variable that stores the decoded feature maps. The presence of skip connections is an essential part of the U-Net design. These bridges open the encoder’s data to the decoder on various levels. Equation ( 11 ) depicts the importance of skip connections in preventing the loss of fine-grained information during the encoding and decoding processes.

The \({S}_{cnn}\) , variable represents enriched feature maps achieved by skip connections. The decoder creates the final segmentation map as the features are upsampled with skip connections. This map emphasizes the regions that are intriguing within the supplied image. Equation ( 12 ) is a valuable representation of the segmentation procedure.

Here, \({S}_{output}\) ​, represents the segmented output, a map highlighting regions of interest, such as ossicles or fractures. The CNN-UNet model is trained using annotated datasets that contain input CT images ( \({I}_{in}\) ​) and ground truth labels for segmentation ( \(GT)\) . At the heart of the training process is a loss function (usually a pixel-wise cross-entropy loss or a dice loss), whose goal is to reduce the discrepancy between the model’s forecasts and the ground truth labels (Eq. ( 13 ).

As a map emphasizing regions of interest like ossicles or fractures, \({S}_{output}\) , depicts the segmented output. Input CT images ( \({I}_{in}\) ) and ground truth labels for segmentation (GT) are used to train the CNN-UNet model from annotated datasets. Preparing the model entails optimizing its parameters with a loss function (usually a pixel-wise cross-entropy loss or a dice loss) to reduce the discrepancy between the model’s predictions and the ground truth labels, as shown in Eq. ( 13 ).

The model is fine-tuned through this optimization process to produce reliable segmentations. After training the CNN-UNet, it can infer information from fresh CT scans. It accepts an image as input and produces a segmented result with relevant parts. By applying Eq. ( 14 ) to an image input ( \({I}_{in}\) ), it has a segmented image output ( \({S}_{output}\) ).

The CNN-UNet model provides a segmented output (Soutput) sound for medical diagnosis. It expedites clinical care by improving accuracy and efficiency through automated examination of critical anatomical structures and diseases. Algorithm 1 (Table  2 ) exemplifies how this comprehensive pipeline uses convolutional neural networks and the U-Net architecture to improve the HRSCT-DLT model’s picture segmentation and diagnostic capabilities.

A Convolutional Neural Network - U-Net (CNN-UNet) model and the steps required to construct, train, and employ it for semantic image segmentation. Medical image analysis frequently involves segmenting images into various classes, such as segmenting anatomical components in high-resolution CT scans. Image size, segmentation class count, learning rate, batch size, and training iterations are all important hyperparameters to tweak. The CNN-UNet model consists of an input layer, a hidden layer for decoding, and an output layer. The algorithm reads the training data, cleans it up, creates an Adam optimizer, loss function, and evaluation measure (in this case, accuracy), trains the model for a given number of iterations, and stores the result. For applying the trained model to the segmentation of brand-new, unseen images, the method additionally defines the function segment_new_images. They are combining the capabilities of CNNs for feature extraction with those of the U-Net architecture for image segmentation results in the CNN-UNet architecture. It is an essential part of the HRSCT-DLT model for accurate and automated segmentation and detection of auriculotemporal and ossicle-related disorders in the middle ear because of its ability to capture delicate anatomical details inside high-resolution CT scans.

The proposed methodology relies heavily on a deep learning model called CNN-UNet that was built from the ground up to meet the specific challenges of this research. Owing to its design, fine-tuned convolutional layers, and rigorous training on the annotated dataset, it can adequately identify and segment essential structures inside temporal bone CT scans, improving precision and insight into otolaryngology. This concept has the potential to change otolaryngology (ENT) diagnostics and bring about significant improvements in patient treatment.

In this section, we outline the methodology and framework that will enable High-Resolution Spiral Computed Tomography scanning and Deep Learning Techniques (HRSCT-DLT), and especially the CNN-UNet deep learning technique, to revolutionize otolaryngology. This tool was developed to aid otolaryngologists in their work by giving them a synopsis of all the disorders that might affect the auricle, temporal bone, and ossicles. This novel approach has the potential to revolutionize the ENT industry because of the architecture and training technique of the CNN-UNet model.

Compared to more traditional forms of chest imaging, high-resolution computed tomography (HRCT) allows for a clearer view of the lungs’ complex structures and the detection of subtle disease changes. By excluding variations caused by gravity or dependent atelectasis, upright HRCT imaging is helpful for individuals with basal illness.

Methodology chosen for the purpose of identifying and selecting studies that will further improve diagnostic skills by exploring how high-resolution CT images complement the CNN-UNet model. Obssicle segmentation, fracture recognition, and disruption cause categorization are some of the important tasks that this inquiry focuses on.

Experimental results and analysis

Due to its high diagnostic accuracy, the HRSCT-DLT Dataset relies heavily on HRSCT imaging of the temporal bone. Traumatic injuries, chronic otitis media, congenital disabilities, and auriculotemporal and ossicle-related illnesses constitute only a few of the many middle ear conditions included in the dataset [ 35 , 36 ]. This study uses a randomized stratified split depending on the prevalence of various illnesses to separate the dataset into training, test and validation sets of 80%, 10% and 10%, respectively. The study uses standard image segmentation measures like Dice Coefficient, Recall, Precision, F1 Score, Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Hausdorff Distance, and Intersection over Union (IoU) to assess CNN-UNet’s performance. We evaluate the proposed model’s efficacy and utility inside the HRSCT-DLT framework by contrasting it against several other deep learning models, such as CNN-GoogLeNet, CNN-DenseNet, CNN-ResNet, and Mask-R-CNN-UNet.

A detailed description of the tests, together with the results and data obtained, is provided in this section. The paper describes the experimental framework that was created, the dataset that was utilized, and the method used to partition the dataset into training and testing sets. Also included are comparisons to other models of its kind and an explanation of the criteria used to assess the HRSCT-DT model. Several metrics pertinent to medical image segmentation, including accuracy, recall, F1 score, dice coefficient, IoU and error measures like RMSE and MAE, demonstrate outstanding performance by the HRSCT-DT model. It delves deep into the ramifications of the HRSCT-DT model’s effectiveness for medical image analysis, specifically looking at how significant it is. By demonstrating how the model outperforms competing deep-learning algorithms, this section emphasizes the model’s promise in otolaryngology and other medical fields.

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The petrous temporal bone contains the air-filled middle ear cavity, often known as the tympanic cavity or tympanum (plural: tympanums/tympana). The tympanic membrane and medial wall separate it from the exterior and inner ears. The three auditory ossicles transport and enhance sound vibrations from the tympanic membrane to the oval window of the inner ear’s lateral wall.

The Dice Coefficient, also known as the Srensen-Dice Coefficient, is a critical measure for gauging the segmentation efficacy of the HRSCT-DT model. Values 0 and 1 indicate how much the ground truth mask matches the model’s expected segmentation mask. If the value is 0, there is no spatial overlap; if it’s 1, there is perfect alignment. The higher spatial agreement, as seen by a more significant Dice Coefficient in Fig.  2 , indicates the model’s efficacy in segmenting problematic regions across several medical pictures. Otolaryngologists rely heavily on this statistic since it is essential for establishing informed diagnoses and treatment plans. A higher Dice Coefficient suggests better spatial agreement in the context of many processed images.

Dice Coefficient Value of the HRSCT-DT Model

Precision and Recall emerge as central metrics for evaluating the model’s proficiency in detecting correctly classifying pathological regions during image segmentation tasks (see Figs.  3 and 4 for an illustration of the high-performance HRSCT-DT model’s use of extensive training epochs). Precision measures how accurate the model is at making positive predictions, or “true positives.” A higher Precision score indicates that the model is more likely to predict diseased locations accurately. Recall (sensitivity or true positive rate) measures how well it can spot and include all truly problematic regions when assessing a model’s predictive power. When the Recall score is high, the model is very good at spotting and includes difficult areas of its predictions. Precision and Recall are essential metrics for validating the HRSCT-DT model’s efficacy in identifying and classifying challenging regions using many training epochs. This skill is critical in medical image segmentation, especially in otolaryngology, where a thorough and precise diagnosis is paramount.

Precision Rate (%) of the HRSCT-DT Model

Recall Rate (%) of the HRSCT-DT Model

It is essential to recognize a common difficulty in image segmentation, the intrinsic trade-off between precision and Recall, within the effective HRSCT-DT model, which flourishes with many training epochs. Improving one of these indicators could lead to a decline in the other. Therefore, the F1 Score, a helpful indicator, becomes an attractive option. The F1 Score is the harmonic mean of accuracy and Recall, successfully integrating each aspect of model performance. In the context of the HRSCT-DT model, where optimal segmentation is crucial, the F1 Score is an indispensable single metric, harmoniously harmonizing precision and Recall, enabling a full assessment of the model’s performance, as seen in Fig.  5 .

F1 Score (%) of the HRSCT-DT Model

The Intersection over Union (IoU), also known as the Jaccard Index, is a crucial metric in the extraordinary performance of the HRSCT-DT model. As the number of training iterations grows, so does the quality of the results. IoU expertly determines the degree of overlap between the ground-truth regions and the model’s predictions using exact measurements of the intersection and union of the two sets. Figure  6 shows that as the number of training epochs for the HRSCT-DT model increases, the IoU value rises progressively, highlighting the impressive degree to which the predicted and ground truth regions overlap. The IoU value for the HRSCT-DT model steadily increases as the number of training epochs increases, attesting to its superior performance. An IoU of 0 indicates poor segmentation, while an IoU of 1 indicates an exact match. This metric becomes extremely useful when evaluating overlap in intricate segmentations or working with regions of varying shapes.

RMSE and MAE Rate of the HRSCT-DT Model

Figure  7 displays the decreased MAE and RMSE values that can be achieved using the HRSCT-DT model as more epochs pass. The mean absolute error (MAE) shows how off the model is, on average, from the actual pixel values. As the number of epochs used in the HRSCT-DT model grows, the MAE score constantly decreases, suggesting an impressively high level of agreement between the predicted and observed values. The RMSE is a more comprehensive measure of the model’s performance. It gives a rough estimate of the forecast error standard deviation. In particular, RMSE’s ability to retain the same units as the pixel values makes it easy to relate to the images’ features directly. In addition, the HRSCT-DT model improves performance with more training iterations.

Intersection Over Union Metric of the HRSCT-DT Model

Hausdorff Distance Metric of the HRSCT-DT Model

Figure  8 shows how the Hausdorff distance emerges as a critical metric in the proposed HRSCT-DT model, which offers impressive performance with increased training epochs. This precision distance measure accurately calculates the most significant possible gap between the model’s anticipated and the real-world segmentation borders. The Hausdorff distance within the HRSCT-DT model continually decreases as the number of epochs grows, demonstrating the model’s accuracy. If the projected and ground-truth bounds are similarly near in size, then the model has done an excellent job of delineating the borders.

The proposed HRSCT-DT model, created over several training epochs, outperforms competing deep learning models across various metrics (including accuracy, Recall, F1 score, Dice Coefficient, and Intersection over Union; see Table  3 ). Its recall score is impressive and shows good accuracy in predicting problematic regions. The model achieves a remarkable F1 score by striking a delicate balance between precision and Recall. IoU intensely beats other models in evaluating the degree of intersection between predicted and ground truth regions, and its superior Dice Coefficient illustrates its ability to align these regions precisely. This model excels at analyzing medical images. Examining cost-effectiveness with diagnostic accuracy metrics demonstrates a distinct pattern of enhanced efficacy as more sophisticated models are implemented. The baseline accuracy of CNN-GoogLeNet is 0.7496, followed by CNN-DenseNet at 0.7951 and CNN-ResNet at 0.8463. The Mask R-CNN-UNet model demonstrates a significantly improved accuracy rate of 0.8749. Nevertheless, the HRSCT-DT model reveals the most notable improvement in accuracy, with an outstanding accuracy rate of 0.9624. It implies that although all models exhibit usefulness, the HRSCT-DT model significantly improves diagnosis accuracy, which could lead to improved patient outcomes and cost reductions in healthcare provision.

Figure  9 displays the superior performance of the HRSCT-DT model in medical image analysis using the RMSE, MAE, and Hausdorff Distance Calculation metrics after extensive training. The root-mean-squared error (RMSE) measures how near predicted values are to the real ones. The MAE estimates how far off predictions are from the actual values, with smaller values representing more accurate predictions. Calculating the Hausdorff Distance is a method for determining how near a forecast is to the ground-truth segmentation borders. Accurate border delineation is critical in the processing of medical images.

Comparative Analysis of the HRSCT-DT and Other Models with Error Metrics

The suggested HRSCT-DT model is a deep learning model that has undergone rigorous testing and evaluation. The spatial agreement between the predicted and ground truth masks is what the Dice Coefficient uses to determine how well it performs. Accurate diagnosis and clinical decision-making in otolaryngology rely on the model’s steadily rising Dice Coefficient as training epochs accumulate. During effectively detecting and classifying problematic regions, the HRSCT-DT model scores highly on two crucial metrics: Precision and Recall. Its excellent Precision and Recall rates guarantee precise predictions of difficult areas, and its high Recall rate indicates its success in locating and including actual pathological regions of its forecasts. The F1 Score is a comprehensive measure of the model’s efficacy that takes into account the trade-off between accuracy and Recall, a typical challenge in image segmentation. Intersection over Union (IoU) scores highly for the HRSCT-DT model, too, showing an impressive overlap between the model’s predictions and the truth. The model maintains higher IoU values as training epochs grow, demonstrating its superior performance. Predicting pixel values close to the ground truth is essential in medical image analysis, and error metrics like RMSE, MAE, and Hausdorff Distance demonstrate the model’s outstanding accuracy. The model also reflects its precision in border delineation using the Hausdorff Distance measure, which indicates its excellent boundary delineation capabilities. This paper presents a comparative study between the proposed HRSCT-DT model and several existing deep learning models, demonstrating the superiority of the HRSCT-DT model. Compared to competing models, it has superior accuracy, Recall, F1 score, Dice Coefficient, and IoU. The model demonstrates its prowess by accurately highlighting sick spots and properly syncing them with ground truth predictions.

d. It is highly suited for complex segmentations and asymmetrical regions since it can detect sick areas effectively while balancing precision and Recall. The model’s efficacy in predicting outcomes down to the pixel level, as measured by RMSE, also contributes to its usefulness in medical image analysis. The model’s proficiency in delineating boundaries is also evident, with distances increasing smaller and smaller as the number of training epochs increases. Its impressive results suggest it has the potential to greatly improve patient care, especially in areas like otolaryngology, where precise picture segmentation, assessment, and boundary delineation are essential for clinical decision-making and treatment planning.

In this section, we present a comprehensive account of the experiments conducted, the data collected, and the conclusions drawn. It details the experimental framework we developed, the dataset we used, and the way we divided the dataset into training and testing sets. It also describes the evaluation criteria used to evaluate the HRSCT-DT model and provides comparisons to similar models. In this section, we will discuss and assess the findings. In particular, it examines the significance of the efficacy of the HRSCT-DT model and its implications for medical picture analysis. This section highlights the model’s potential in otolaryngology and related medical domains by highlighting how it excels above other deep-learning models.

This research presents a diagnostic paradigm for otolaryngology incorporating High-Resolution Spiral Computed Tomography scanning and Deep Learning Techniques (HRSCT-DLT). Auriculotemporal and ossicular disorders can be challenging to diagnose, so our project aims to simplify the process for patients and medical professionals. Traditional diagnostic approaches are inadequate for elucidating such diseases. Clinicians and researchers may better capture subtle information within medical pictures thanks to the HRSCT-DLT model’s combination of High-Resolution Spiral Computed Tomography scanning and the CNN-UNet deep learning model. Using automation for essential functions, including ossicle segmentation, fracture diagnosis, and disruption cause categorization, this method can take patient care to new heights and speed up the diagnostic process. Improved diagnosis accuracy and decreased workload for medical professionals are two direct benefits of this automation of clinical decision-making. The HRSCT-DLT model is cutting-edge in medical imaging and diagnostics, giving doctors more tools to make accurate diagnoses and tailor care to each patient. This strategy aims to improve patient outcomes and raise the bar for otolaryngology care overall. High-resolution spiral CT scanning’s radiation exposure, contrast sensitivity, artefact generation, limited functional information, expense, and accessibility are drawbacks. Radiation exposure is hazardous for youngsters and pregnant women, who are more vulnerable. CT doses depend on scan parameters, patient size, and method. CT scans may lack soft tissue contrast, making diseases and soft tissues hard to distinguish. Beam hardening, metal, and motion artefacts can impair image quality and hide key anatomical features or pathology. CT imaging may lack functional or dynamic data, making it less useful for some disorders. High-resolution spiral CT scanners are expensive to buy and maintain, which may limit their use in particular healthcare settings and patient access to diagnostic services. Future research should integrate multimodal imaging methods like MRI and ultrasound with the HRSCT-DLT architecture for a comprehensive diagnostic approach.

This section details the experiments, data, and findings. It describes the experimental methodology, dataset, and training and testing sets. It also outlines the HRSCT-DT model’s evaluation criteria and compares it to others. The HRSCT-DT model excels in medical image segmentation metrics like precision, recall, F1 score, Dice Coefficient, IoU(98.01, 98.97, 99.12, 0.9897, 0.9924), and error measures like RMSE and MAE. It focuses on HRSCT-DT model efficacy and medical picture analysis. The section shows how the model outperforms existing deep-learning models in otolaryngology and related medical fields.

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Cai, Q., Zhang, P., Xie, F. et al. Clinical application of high-resolution spiral CT scanning in the diagnosis of auriculotemporal and ossicle. BMC Med Imaging 24 , 102 (2024). https://doi.org/10.1186/s12880-024-01277-6

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Clinical relevance of positive intraoperative bacterial culture in tibial plateau leveling osteotomy in dogs: a retrospective study

• Natália Korytárová 1 ,
• Sabine Kramer 1 ,
• Oliver Harms 1 &
• Holger A. Volk 1  

BMC Veterinary Research volume  20 , Article number:  175 ( 2024 ) Cite this article

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Tibial plateau leveling osteotomy (TPLO) belongs to the most frequently used surgical method for the treatment of cranial cruciate ligament rupture in dogs. Surgical site infection (SSI) is one of the possible postoperative complications. The aim of this study was to evaluate the diagnostic value of intraoperative bacterial culture as a tool for the detection of intraoperative bacterial contamination progressing to infection development in canine TPLO. Electronic patient records from dogs who underwent TPLO between January 2018 to December 2020 were retrospectively reviewed. Intraoperative bacterial culture results, used antimicrobial drugs and presence of SSI were recorded.

Ninety-eight dogs were included in the study. SSI rate was 10.2%. All dogs who developed SSI ( n  = 10) had negative intraoperative bacterial cultures. None of the dogs with positive intraoperative bacterial culture ( n  = 6) developed SSI. The most cultured bacteria causing SSI was Staphylococcus pseudintermedius ( n  = 4).

Conclusions

Intraoperative bacterial culture in dogs undergoing TPLO is not suitable as a predictor of surgical site infection.

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Cranial cruciate ligament disease is one of the main reasons for pelvic limb lameness in dogs [ 1 ]. Currently, one of the most common methods of surgical treatment of canine cranial cruciate ligament rupture is tibial plateau leveling osteotomy (TPLO) [ 2 , 3 , 4 , 5 , 6 ].

The complication rate of TPLO varies between 10 and 34%, with 2 to 4% requiring surgical revision.[ 7 ] Surgical site infection (SSI) rate after TPLO is reported to be 2.9–25.9% [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ], which is higher compared to other clean orthopaedic surgeries (2.0-6.7%) [ 28 , 30 , 31 ]. A complicating factor in the treatment of surgical site infections after an implant surgery like TPLO is the formation of a biofilm, which makes eradication of the infection problematic [ 32 , 33 , 34 ]. In some cases, it is possible to defeat the infection with long-term administration of antimicrobial drugs, but usually surgical removal of the implant is necessary [ 11 , 24 , 32 , 33 , 35 ]. Therefore treatment associated with SSI after TPLO often requires considerable financial costs [ 36 , 37 ]. Because of the high morbidity due to further surgery and the additional cost in case of a SSI post-TPLO, strategies are being sought to prevent infection development [ 27 ].

Early detection and treatment of bacterial contamination of the surgical site can reduce the incidence of SSI [ 38 ]. Intraoperative bacterial cultures have been collected for the identification of bacterial contamination in people and dogs undergoing total hip replacement [ 38 , 39 , 40 , 41 ]. In one study, isolation of Staphylococcus aureus from intraoperative bacterial culture in people undergoing total hip replacement was associated with a 7-fold increased risk of infection [ 40 ]. On the contrary, another study in people did not find any association between positive intraoperative bacterial culture and SSI development [ 39 ]. In canine patients undergoing total hip replacement, positive intraoperative bacterial culture was found not to be a predictor of SSI [ 38 , 41 ].

To the authors’ knowledge, no study has been conducted on the usefulness of intraoperative bacterial culture taken during canine TPLO. The aim of the current study was to evaluate the clinical relevance of positive intraoperative bacterial culture in dogs undergoing TPLO. We hypothesized that the development of surgical site infection would not be associated with positive intraoperative bacterial culture.

This study was performed at the Department of Small Animal Medicine and Surgery, University of Veterinary Medicine Hannover, Germany. All electronic patient records from dogs undergoing TPLO in a three-year period, from January 2018 to December 2020, were evaluated retrospectively. In these patients, breed, age, gender, weight and administered antimicrobial drugs were documented. Results of bacterial culture swabs taken intraoperatively and the occurrence of surgical site infection were recorded. In cases of SSI, results of bacterial culture taken from the infected surgical site were also recorded.

Diagnostics and surgery

For a non-invasive evaluation of the intraarticular structures of the stifle joint, magnetic resonance imaging (MRI) was performed. The diagnostic part and the surgery were performed either in two independent anaesthetic sessions or in one appointment according to the owner’s wish. All patients were prepared for surgery following the standard aseptic preparation protocol used at the hospital [ 42 ]. In patients with a meniscal lesion, a medial arthrotomy followed by partial meniscectomy was performed. TPLO was then performed as described by Slocum and Slocum [ 43 ]. After the surgery, the surgical site was covered by a sterile wound dressing applied in the surgical theatre using sterile technique.

Bacterial culture sampling

Sampling was performed before wound closure. A swab was taken from the surgical site, from the area around the placed TPLO plate and adjacent soft tissues. All samples were stored cooled in Amies transport medium and sent to the laboratory on the day of sample collection. All samples were examined in an accredited microbiological laboratory, in the Institute of Microbiology, University of Veterinary Medicine Hannover. Samples were processed immediately upon arrival at the laboratory. Swabs were streaked onto Columbia Sheep Blood Agar, Boiled Blood Agar and Schaedler Agar. Then the swabs were immersed in nutrient broth. In all cases, both aerobic and anaerobic culture was performed.

Postoperative management

All dogs remained hospitalized in the clinic until the next day when the wound dressing was changed. Contact with the surgical site was carried out only with single-use examination gloves. The dogs had to wear an Elizabethan collar to prevent licking of the wound. All dog owners were informed by the time of discharge about postoperative wound management. They also received this information on the discharge documents. After receiving the bacterial culture results, which was usually 7 days after the surgery, the owners were contacted by phone and asked about the general condition of the patient and wound healing. This was recorded and considered in our study. Referring veterinarians were asked to contact the clinic in case of any complications. Control radiographs were performed at our clinic six weeks after surgery. Dogs who developed any complications were seen back earlier. Medical records of the dogs in the study were followed up for one year after the surgery. Dogs with incomplete data were excluded from the study.

Definition of SSI

The definition of SSI was adapted from the standard criteria developed by the US Centers for Disease Control and Prevention (CDC) [ 44 , 45 ]. A wound was considered infected when purulent discharge, an abscess, or a fistula and/or one or more of the clinical signs of pain and localized swelling, redness, heat, fever, or deep incision spontaneous dehiscence was identified on clinical examination and/or when an organism was isolated from an aseptically collected sample by culture and/or positive cytology study. SSIs were classified according to superficial, deep, or organ/space infections (Table 1 ). Cases with positive intraoperative bacterial culture but without any clinical signs of infection, were not considered infected.

Data analysis

All data were transferred from the clinic’s electronic practice management software Easyvet (Veterinärmedizinisches Dienstleistungszentrum (VetZ) GmbH, Isernhagen, Germany), where they were originally documented, into a spreadsheet in Excel (version 2021, Microsoft, Redmond, Washington, USA) and then imported in a statistical software for further analysis. All analyses were performed with SPSS Statistics 20 (IBM, Armonk, NY, USA). The very low number of positive bacterial cultures observed restricted statistical test procedures and therefore the data were analysed with descriptive statistics only.

Ninety-eight dogs met the inclusion criteria. Among the 98 dogs, there were 26.5% (26) spayed females, 24.5% (24) intact females, 24.5% (24) intact males and 24.5% (24) neutered males. The median age in years was 5 (range 1–13). A total of 36 different dog breeds were included. Mixed breed dogs were most common (22), followed by Labrador Retriever (10), Golden Retriever (6), American Staffordshire Terrier (4), Boxer (4), Rottweiler (4) and Siberian Husky (4). The median body weight in kg was 32.7 (range 11.5–63.0).

Surgical procedure

51% (50/98) of the surgical procedures were performed on the left pelvic limb and 49% (48/98) on the right pelvic limb. In 58.2% (57/98) of cases, an MRI was performed prior to surgery. In 41.8% (41/98) dogs, the diagnostic imaging and surgery were split into two separate anaesthetic sessions. Medial arthrotomy and partial meniscectomy were performed in 38.8% (38/98) of dogs.

Antimicrobial drug use

Prophylactic perioperative antimicrobial therapy was administered in all 98 dogs. Either cefazolin (22 mg/kg IV) or amoxicillin/clavulanic acid (12,5 mg/kg IV) were administered. Cefazolin was administered in 65.3% (64/98) dogs and 34.7% (34/98) patients received amoxicillin/clavulanic acid. All 98 dogs received amoxicillin/clavulanic acid (12,5 mg/kg PO q 12 h) postoperatively. The median duration of the postoperative antimicrobial therapy was 7 days (range 5–28 days).

• Intraoperative bacterial culture

Intraoperative bacterial culture was collected in all 98 dogs. 93.9% (92/98) of dogs had negative intraoperative bacterial culture. Bacteria were isolated in only six dogs (6.1%). In all cases with positive intraoperative bacterial culture, there was only low bacterial contamination detected. None of these six dogs developed a surgical site infection. Summary of the positive intraoperative bacterial culture results is shown in Table  2 .

• Surgical site infection

Surgical site infection was diagnosed in a 10.2% (10/98) of dogs. All dogs, who developed a SSI, had a negative result in the intraoperative bacterial culture. Seven dogs developed a superficial SSI and three dogs a deep SSI. All patients with superficial SSI were treated only medically. Medical therapy consisted of systemic antimicrobial treatment combined with local antiseptic therapy using wound irrigation solution (Prontovet, B Braun, Melsungen, Germany). Implant removal was performed in all three dogs with deep SSI. Bacterial culture sampling from the infected surgical site was performed in all three dogs with deep SSI and in two dogs with superficial SSI. In the remaining five dogs with superficial SSI, no bacterial culture sampling was performed. The most cultured bacteria causing SSI in our study was Staphylococcus pseudintermedius (4), followed by Staphylococcus aureus (1) and Pseudomonas spp . (1). In one patient a polymicrobial culture was identified. The summary of the cultured bacterial strains in dogs with SSI after TPLO is shown in Table 3 .

In our study, intraoperative bacterial culture was positive in six cases. None of these six dogs developed a SSI. Conversely, some of the dogs with a negative intraoperative bacterial culture later developed SSI. This finding suggests that positive intraoperative bacterial culture in TPLO patients is not an accurate predictor of surgical site infection. Therefore, our initial hypothesis can be accepted. The same finding was documented in both veterinary studies evaluating the clinical relevance of intraoperative bacterial cultures in canine total hip replacement [ 38 , 41 ]. In people, the combination of a positive opening and a positive closing culture was a significant predictor of subsequent infection [ 39 , 40 ]. In our study, only one intraoperative bacterial culture was taken at the end of the surgery before wound closure. Further studies are needed to determine whether the collection of two samples, at the beginning and at the end of surgery, with a positive finding can predict the development of SSI in dogs after TPLO.

There are several possible reasons why the contamination of the surgical site did not result in SSI. It is possible that the bacterial contamination was suppressed by the administration of antimicrobial drugs in the postoperative period. The cultured isolates were sensitive to the administered antimicrobial drug, except in one case. Although resistant bacteria were found in one patient, no SSI developed. Therefore, the question whether postoperative antimicrobials played a role in suppressing bacterial contamination or whether patients would have coped with low bacterial contamination even without the use of antimicrobials in the postoperative period remains unclear. Another reason could be low pathogenicity of the cultured bacteria or the presence of a subclinical infection.

Development of a surgical site infection is an inherent risk in orthopaedic surgery [ 45 ]. The SSI rate in this study was 10.2%, which is within the range of previously reported results [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ]. In the current study, SSI developed in 10 cases. All dogs with SSI had a negative intraoperative bacterial culture. It is highly probable that these infections occurred after the surgery. Medical personnel, environment and commensal organisms from the patient’s own microbiome are potential sources for surgical site contamination [ 46 ]. Hands of the medical workers represent an important role in the infection development in the early postoperative period [ 45 , 46 ]. In the study from Anderson et al. (2014) the use of hand hygiene products at veterinary clinics was often lower than recommended and the overall hand hygiene compliance was poor [ 47 ]. It is important that hands are washed or disinfected before and after contact with every patient and wound dressings are used after surgery to reduce the exposure to possible exogenous contamination sources [ 45 ]. To prevent contamination of the surgical site during hospital stay, a sterile plaster was applied on the wound after surgery and any handling with the wound was performed using single-use examination gloves. The owners of the dogs were instructed how to properly handle the surgical wound and the dogs had to wear Elizabethan collar to prevent wound licking. Nevertheless, based on our results, it seems that surgical site contamination in our patients occurred in the postoperative period. It remains unclear whether the clinic environment became the source of contamination or whether the contamination only occurred in the home environment after discharge from the hospital. Although we did not find any mention in the reviewed medical records of dogs licking their wounds, we cannot rule this out with certainty.

The most frequently cultured organisms causing SSI in this study were bacteria Staphylococcus pseudintermedius . In many other recent TPLO studies, Staphylococcus spp . predominated as the cause of surgical site infection as well [ 11 , 19 , 20 , 22 , 23 , 24 , 25 , 27 , 29 , 48 ]. Staphylococci are skin commensals and opportunistic pathogens, which probably explains their frequent occurrence in surgical site infections [ 11 , 46 ]. Their significant feature is the ability to form resistance to antimicrobial drugs [ 49 ]. The most relevant species include coagulase-positive species Staphylococcus aureus and Staphylococcus pseudintermedius  [ 49 ]. Other bacterial strains commonly causing SSI in veterinary orthopaedic surgery are Streptococcus spp ., Enterococcus spp . and Pseudomonas spp.  [ 10 , 11 , 20 , 24 , 27 , 35 ].

Prophylactic perioperative administration of antimicrobial drugs is a well-established tool to prevent surgical site infection [ 46 ]. Opinions on the role of postoperative antimicrobial drugs in dogs after TPLO differ [ 36 ]. There are several studies supporting the use of postoperative antimicrobial drugs in TPLO patients [ 12 , 13 , 14 , 15 , 20 , 23 , 26 , 50 ]. However, there are also some reports in which postoperative antimicrobial therapy in TPLO patients is not considered beneficial [ 10 , 16 , 19 , 22 , 27 ]. The first review article on postoperative antimicrobial drug use after TPLO from Budsberg et al. (2021) concluded that there is little evidence to support protective effect of postoperative antimicrobials against the development of surgical site infection in dogs after TPLO. Nevertheless, the answer to this question from a clinical point of view remains unclear due to only a small number of prospective studies and inconsistent treatment protocols in the reviewed studies [ 36 ]. All dogs in our study received antimicrobial drugs postoperatively. Most of the cultured organisms were sensitive to the antimicrobials used. Resistant bacteria were isolated only in one dog with intraoperative contamination and in two dogs with SSI. It is possible that if antimicrobials were not administered in the postoperative period, the incidence of surgical site infection would be higher.

The main limitation of this study was its retrospective nature. It is possible that mild infections, particularly more superficial ones, which resolved without any medical intervention, were not identified or reported. Therefore, the number of cases with SSI could have been underestimated. Conducting our study in a prospective fashion with a uniform antibiotic protocol and bacteriological sampling in all cases of SSI would increase the power of our results. Due to the fact that taking bacterial culture swabs is associated with a certain rate of false negative results, taking a tissue sample for culture would be preferable. Another limitation of the study was the low number of positive intraoperative cultures. A higher number of positive findings would enable us to perform statistical analysis which would increase the power of this study.

Based on our results, intraoperative bacterial culture does not seem to be a suitable method to predict infection development in dogs undergoing TPLO and it is uncertain whether cultured organisms can cause infection at all. We assume that the contamination of the surgical site and subsequent infection occurred in our patients in the postoperative period and therefore adherence to the hygiene principles in the postoperative period remains an important part in the fight against surgical site infection.

Availability of data and materials

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

Abbreviations

• Tibial plateau leveling osteotomy

Magnetic resonance imaging

US Centers for Disease Control and Prevention

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Natália Korytárová, Sabine Kramer, Oliver Harms & Holger A. Volk

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SK, OH and HAV initiated the study and participated in its design and coordination. NK collected the data. NK wrote the first draft of the manuscript. All authors contributed to the article and approved the final manuscript.

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Korytárová, N., Kramer, S., Harms, O. et al. Clinical relevance of positive intraoperative bacterial culture in tibial plateau leveling osteotomy in dogs: a retrospective study. BMC Vet Res 20 , 175 (2024). https://doi.org/10.1186/s12917-024-04007-w

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Received : 11 February 2024

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DOI : https://doi.org/10.1186/s12917-024-04007-w

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