{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T18:27:14Z","timestamp":1786991234994,"version":"build-2736575974"},"reference-count":33,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T00:00:00Z","timestamp":1779667200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T00:00:00Z","timestamp":1779667200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J of Oral Rehabilitation"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>This study aims to predict TMD using ML approaches based on clinical and sociodemographic variables to aid in the early detection and risk assessment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The study was on patients with and without TMD who met the inclusion criteria in a retrospective\u2013prospective manner. Clinical and demographic variables of 340 patients were collected during the study. Data were split into 80% training and 20% test sets. Five different machine learning models (DANets, KAN, KNN, MLP, SVM) were used. Model performance was evaluated using accuracy, weighted precision, recall, and F1\u2010score. A model interpretability analysis was performed using SHapley Additive ExPlanations (SHAP).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>For result classification, the DANets model achieved the highest performance (accuracy\u2009=\u20090.8529). The DANets model achieved the strongest performance in diagnostic classification by far (accuracy\u2009=\u20090.8382, F1\u2009=\u20090.8212). SHAP\u2010based analyses revealed that demographic factors (especially age and gender) and certain clinical symptoms (e.g., joint pain, clicking, and limited mouth opening) had the highest contribution to the models' decision\u2010making processes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>AI\u2010based models\u2014particularly deep learning algorithms\u2014may provide substantial support to clinicians in the diagnosis and risk assessment of TMD. These tools may reduce diagnostic uncertainty, especially for inexperienced practitioners, facilitate early diagnosis and referral processes, and decrease the risk of TMD progression.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/joor.70220","type":"journal-article","created":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T20:15:34Z","timestamp":1779740134000},"page":"1665-1674","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence\u2010Based Approach for Determining the Risk of Temporomandibular Disorders"],"prefix":"10.1111","volume":"53","author":[{"given":"Damla","family":"Torul","sequence":"first","affiliation":[{"name":"Department of Oral and Maxillofacial Surgery Ordu University  Ordu Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mustafa Hakan","family":"Bozkurt","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence and Data Engineering Karadeniz Teknik University  Trabzon Turkey"},{"name":"Maveria Technologies  Trabzon Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mehmet Melih","family":"\u00d6mezli","sequence":"additional","affiliation":[{"name":"Department of Oral and Maxillofacial Surgery Ordu University  Ordu Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammed Furkan","family":"Y\u0131lmaz","sequence":"additional","affiliation":[{"name":"Department of Oral and Maxillofacial Surgery Ordu University  Ordu Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"K\u00fcbra","family":"Y\u0131ld\u0131z","sequence":"additional","affiliation":[{"name":"Department of Oral and Maxillofacial Surgery Ordu University  Ordu Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,5,25]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"publisher","DOI":"10.1097\/MD.0000000000039912"},{"key":"e_1_2_12_3_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12903-024-04832-3"},{"key":"e_1_2_12_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3224055"},{"key":"e_1_2_12_5_1","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics13162700"},{"key":"e_1_2_12_6_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-98837-5"},{"key":"e_1_2_12_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00784-020-03710-w"},{"key":"e_1_2_12_8_1","doi-asserted-by":"publisher","DOI":"10.1111\/joor.13701"},{"key":"e_1_2_12_9_1","doi-asserted-by":"publisher","DOI":"10.3390\/bioengineering11020134"},{"key":"e_1_2_12_10_1","doi-asserted-by":"publisher","DOI":"10.3389\/fphys.2024.1445258"},{"key":"e_1_2_12_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-47425-5_18"},{"key":"e_1_2_12_12_1","doi-asserted-by":"publisher","DOI":"10.1038\/s43856-025-01081-5"},{"key":"e_1_2_12_13_1","doi-asserted-by":"publisher","DOI":"10.3390\/s24113646"},{"key":"e_1_2_12_14_1","doi-asserted-by":"publisher","DOI":"10.1111\/joor.13350"},{"key":"e_1_2_12_15_1","doi-asserted-by":"publisher","DOI":"10.1136\/annrheumdis-2018-214354"},{"key":"e_1_2_12_16_1","doi-asserted-by":"publisher","DOI":"10.1111\/joor.14030"},{"key":"e_1_2_12_17_1","doi-asserted-by":"publisher","DOI":"10.1080\/08869634.2003.11746260"},{"key":"e_1_2_12_18_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-83750-4"},{"key":"e_1_2_12_19_1","doi-asserted-by":"publisher","DOI":"10.1111\/joor.13383"},{"key":"e_1_2_12_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.apacoust.2021.108292"},{"key":"e_1_2_12_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.joms.2011.03.069"},{"key":"e_1_2_12_22_1","doi-asserted-by":"publisher","DOI":"10.1111\/joor.12972"},{"key":"e_1_2_12_23_1","volume-title":"Contemporary Orthodontics","author":"Proffit W. 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