{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T06:09:29Z","timestamp":1790834969194,"version":"4.1.0"},"reference-count":29,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,7,6]],"date-time":"2021-07-06T00:00:00Z","timestamp":1625529600000},"content-version":"vor","delay-in-days":186,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["BioMed Research International"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>\n                    The purpose of this study was to propose a machine learning model and assess its ability to classify TMJ pathologies on magnetic resonance (MR) images. This retrospective cohort study included 214 TMJs from 107 patients with TMJ signs and symptoms. A radiomics platform was used to extract (Huiying Medical Technology Co., Ltd., China) imaging features of TMJ pathologies, condylar bone changes, and disc displacements. Thereafter, different machine learning (ML) algorithms and logistic regression were implemented on radiomic features for feature selection, classification, and prediction. The following radiomic features included first\u2010order statistics, shape, texture, gray\u2010level cooccurrence matrix (GLCM), gray\u2010level run length matrix (GLRLM), and gray\u2010level size zone matrix (GLSZM). Six classifiers, including logistic regression (LR), random forest (RF), decision tree (DT),\n                    <jats:italic>k<\/jats:italic>\n                    \u2010nearest neighbors (KNN), XGBoost, and support vector machine (SVM) were used for model building which could predict the TMJ pathologies. The performance of models was evaluated by sensitivity, specificity, and ROC curve. KNN and RF classifiers were found to be the most optimal machine learning model for the prediction of TMJ pathologies. The AUC, sensitivity, and specificity for the training set were 0.89 and 1, while those for the testing set were 0.77 and 0.74, respectively, for condylar changes and disc displacement, respectively. For TMJ condylar bone changes Large\u2010Area High\u2010Gray\u2010Level Emphasis, Gray\u2010Level Nonuniformity, Long\u2010Run Emphasis Long\u2010Run High\u2010Gray\u2010Level Emphasis, Flatness, and Volume features, while for TMJ disc displacements Average Intensity, Sum Average, Spherical Disproportion, and Entropy features, were selected. This study has proposed a machine learning model by KNN and RF analysis on TMJ MR images, which can be used to classify condylar changes and TMJ disc displacements.\n                  <\/jats:p>","DOI":"10.1155\/2021\/6656773","type":"journal-article","created":{"date-parts":[[2021,7,6]],"date-time":"2021-07-06T19:35:07Z","timestamp":1625600107000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":50,"title":["Development and Validation of a Magnetic Resonance Imaging\u2010Based Machine Learning Model for TMJ Pathologies"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6768-0176","authenticated-orcid":false,"given":"Kaan","family":"Orhan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6003-8137","authenticated-orcid":false,"given":"Lukas","family":"Driesen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6250-2650","authenticated-orcid":false,"given":"Sohaib","family":"Shujaat","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3461-0363","authenticated-orcid":false,"given":"Reinhilde","family":"Jacobs","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2477-7247","authenticated-orcid":false,"given":"Xiangfei","family":"Chai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,7,6]]},"reference":[{"key":"e_1_2_8_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/0030-4220(89)90293-4"},{"key":"e_1_2_8_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00056-007-0720-3"},{"key":"e_1_2_8_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11282-009-0001-5"},{"key":"e_1_2_8_4_2","first-page":"2983","article-title":"Static and dynamic MR imaging in the evaluation of temporomandibular disorders","volume":"18","author":"Barchetti F.","year":"2014","journal-title":"European Review for Medical and Pharmacological Sciences"},{"key":"e_1_2_8_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.tripleo.2009.02.023"},{"key":"e_1_2_8_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.tripleo.2005.04.004"},{"key":"e_1_2_8_7_2","first-page":"123","article-title":"Relationship between clinical and magnetic resonance imaging diagnoses and findings in degenerative and inflammatory temporomandibular joint diseases: a systematic literature review","volume":"23","author":"Koh K. 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