{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T15:09:22Z","timestamp":1788188962586,"version":"build-2803163510"},"reference-count":54,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2025,3,3]],"date-time":"2025-03-03T00:00:00Z","timestamp":1740960000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Oral Diseases"],"published-print":{"date-parts":[[2025,7]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>Temporomandibular disorders (TMDs) refer to a group of disorders related to the temporomandibular joint (TMJ), the diagnosis of which is important in dental practice but remains challenging for nonspecialists. With the development of machine learning (ML) methods, ML\u2010based TMDs\u00a0diagnostic models have shown great potential. The purpose of this review is to summarize the application of ML in TMDs diagnosis, as well as future directions and possible challenges.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>PubMed, Google Scholar, and Web of Science databases were searched for electronic literature published up to October 2024, in order to describe the current application of ML in the classification and diagnosis of TMDs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We summarized the application of various ML methods in the diagnosis and classification of different subtypes of TMDs and described the role of different imaging modalities in constructing diagnostic models. Ultimately, we discussed future directions and challenges that ML methods may confront in the application of TMDs diagnosis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The screening and diagnosis models of TMDs\u00a0based on ML methods hold significant potential for clinical application, but still need to be further verified by a large number of multicenter data and longitudinal studies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/odi.15300","type":"journal-article","created":{"date-parts":[[2025,3,11]],"date-time":"2025-03-11T21:30:41Z","timestamp":1741728641000},"page":"1964-1972","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Application of Machine Learning in the Diagnosis of Temporomandibular Disorders: An Overview"],"prefix":"10.1111","volume":"31","author":[{"given":"Xiayanran","family":"Wu","sequence":"first","affiliation":[{"name":"State Key Laboratory of Oral Diseases &amp; 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