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      Artificial Intelligence in Temporomandibular Joint Disorders: An Umbrella Review

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          ABSTRACT

          Objectives

          Given the complexity of temporomandibular joint disorders (TMDs) and their overlapping symptoms with other conditions, an accurate diagnosis necessitates a thorough examination, which can be time‐consuming and resource‐intensive. Consequently, innovative diagnostic tools are required to increase TMD diagnosis efficiency and precision. Therefore, the purpose of this umbrella review was to examine the existing evidence about the usefulness of artificial intelligence (AI) in TMD diagnosis.

          Material and Methods

          A comprehensive search of the literature was performed from inception to November 30, 2024, in PubMed‐MEDLINE, Embase, and Scopus databases. This review evaluated systematic reviews (SRs) and meta‐analyses (MAs) that reported TMD patients/datasets, any AI model as intervention, no treatment, placebo as comparator and accuracy, sensitivity, specificity, or predictive value of AI models as outcome. The extracted data were complemented with narrative synthesis.

          Results

          Out of 1497 search results, this umbrella review included five studies. One of the five articles was an SR while the other four were SRMAs. Three studies focused on patients with temporomandibular joint (TMJ) problems as a group, whereas two were specific to temporomandibular joint osteoarthritis (TMJOA). The included studies reported the use of imaging datasets as samples, including cone‐beam computed tomography (CBCT), magnetic resonance imaging (MRI), and panoramic radiography. The studies reported an accuracy level ranging from 0.59 to 1. Four studies reported sensitivity levels ranging from 0.76 to 0.80. Four studies reported specificity values ranging from 0.63 to 0.95 for TMJ conditions. However, only one study provided the area under the curve (AUC) in the diagnosis of TMDs.

          Conclusions

          AI has the ability to provide faster, more accurate, sensitive, and objective diagnosis of TMJ condition. However, the performance is determined on the AI models and datasets used. Therefore, before implementing AI models in clinical practice, it is essential for researchers to extensively refine and evaluate the AI application.

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          Most cited references33

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          AMSTAR 2: a critical appraisal tool for systematic reviews that include randomised or non-randomised studies of healthcare interventions, or both

          The number of published systematic reviews of studies of healthcare interventions has increased rapidly and these are used extensively for clinical and policy decisions. Systematic reviews are subject to a range of biases and increasingly include non-randomised studies of interventions. It is important that users can distinguish high quality reviews. Many instruments have been designed to evaluate different aspects of reviews, but there are few comprehensive critical appraisal instruments. AMSTAR was developed to evaluate systematic reviews of randomised trials. In this paper, we report on the updating of AMSTAR and its adaptation to enable more detailed assessment of systematic reviews that include randomised or non-randomised studies of healthcare interventions, or both. With moves to base more decisions on real world observational evidence we believe that AMSTAR 2 will assist decision makers in the identification of high quality systematic reviews, including those based on non-randomised studies of healthcare interventions.
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            Preferred Reporting Items for Overviews of Reviews (PRIOR): a protocol for development of a reporting guideline for overviews of reviews of healthcare interventions

            Background Overviews of reviews (i.e., overviews) compile information from multiple systematic reviews to provide a single synthesis of relevant evidence for healthcare decision-making. Despite their increasing popularity, there are currently no systematically developed reporting guidelines for overviews. This is problematic because the reporting of published overviews varies considerably and is often substandard. Our objective is to use explicit, systematic, and transparent methods to develop an evidence-based and agreement-based reporting guideline for overviews of reviews of healthcare interventions (PRIOR, Preferred Reporting Items for Overviews of Reviews). Methods We will develop the PRIOR reporting guideline in four stages, using established methods for developing reporting guidelines in health research. First, we will establish an international and multidisciplinary expert advisory board that will oversee the conduct of the project and provide methodological support. Second, we will use the results of comprehensive literature reviews to develop a list of prospective checklist items for the reporting guideline. Third, we will use a modified Delphi exercise to achieve a high level of expert agreement on the list of items to be included in the PRIOR reporting guideline. We will identify and recruit a group of up to 100 international experts who will provide input into the guideline in three Delphi rounds: the first two rounds will occur via online survey, and the third round will occur during a smaller (8 to 10 participants) in-person meeting that will use a nominal group technique. Fourth, we will produce and publish the PRIOR reporting guideline. Discussion A systematically developed reporting guideline for overviews could help to improve the accuracy, completeness, and transparency of overviews. This, in turn, could help maximize the value and impact of overviews by allowing more efficient interpretation and use of their research findings.
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              Diagnosis of Diabetes Mellitus Using Gradient Boosting Machine (LightGBM).

              Diabetes mellitus (DM) is a severe chronic disease that affects human health and has a high prevalence worldwide. Research has shown that half of the diabetic people throughout the world are unaware that they have DM and its complications are increasing, which presents new research challenges and opportunities. In this paper, we propose a preemptive diagnosis method for diabetes mellitus (DM) to assist or complement the early recognition of the disease in countries with low medical expert densities. Diabetes data are collected from the Zewditu Memorial Hospital (ZMHDD) in Addis Ababa, Ethiopia. Light Gradient Boosting Machine (LightGBM) is one of the most recent successful research findings for the gradient boosting framework that uses tree-based learning algorithms. It has low computational complexity and, therefore, is suited for applications in limited capacity regions such as Ethiopia. Thus, in this study, we apply the principle of LightGBM to develop an accurate model for the diagnosis of diabetes. The experimental results show that the prepared diabetes dataset is informative to predict the condition of diabetes mellitus. With accuracy, AUC, sensitivity, and specificity of 98.1%, 98.1%, 99.9%, and 96.3%, respectively, the LightGBM model outperformed KNN, SVM, NB, Bagging, RF, and XGBoost in the case of the ZMHDD dataset.
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                Author and article information

                Contributors
                vini.mehta@statsense.in
                Journal
                Clin Exp Dent Res
                Clin Exp Dent Res
                10.1002/(ISSN)2057-4347
                CRE2
                Clinical and Experimental Dental Research
                John Wiley and Sons Inc. (Hoboken )
                2057-4347
                11 March 2025
                February 2025
                : 11
                : 1 ( doiID: 10.1002/cre2.v11.1 )
                : e70115
                Affiliations
                [ 1 ] Faculty of Dentistry University of Ibn al‐Nafis for Medical Sciences San'a Yemen
                [ 2 ] Department of Dental Research Cell Dr. D. Y. Patil Dental College and Hospital, Dr. D. Y. Patil Vidyapeeth Pune India
                Author notes
                [*] [* ] Correspondence: Vini Mehta ( vini.mehta@ 123456statsense.in )

                Author information
                http://orcid.org/0000-0003-4174-907X
                Article
                CRE270115
                10.1002/cre2.70115
                11894261
                40066511
                63495ee9-dcdd-4ad7-9d94-93551a8eda5f
                © 2025 The Author(s). Clinical and Experimental Dental Research published by John Wiley & Sons Ltd.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 18 February 2025
                : 10 December 2024
                : 01 March 2025
                Page count
                Figures: 1, Tables: 4, Pages: 10, Words: 5938
                Funding
                Funded by: The authors received no specific funding for this work.
                Categories
                Review Article
                Review Article
                Custom metadata
                2.0
                February 2025
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.5.4 mode:remove_FC converted:11.03.2025

                artificial intelligence,machine learning,temporomandibular joint disorders

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