{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T06:16:55Z","timestamp":1788157015292,"version":"build-2803163510"},"reference-count":36,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2026,8,30]],"date-time":"2026-08-30T00:00:00Z","timestamp":1788048000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,8,30]],"date-time":"2026-08-30T00:00:00Z","timestamp":1788048000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["RS\u20102025\u201020082969"],"award-info":[{"award-number":["RS\u20102025\u201020082969"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["RS\u20102023\u201000217689"],"award-info":[{"award-number":["RS\u20102023\u201000217689"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Clinical &amp;amp; Exp Dental Res"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>To develop interpretable machine learning (ML) models for risk stratification of clinically meaningful anterior overbite decrease after stabilization splint therapy in temporomandibular disorder (TMD) patients, and to compare ML performance with conventional logistic regression.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>\n                      This retrospective cohort included 87 TMD patients treated with stabilization splints, physical therapy, and pharmacologic therapy. Thirty\u2010five pre\u2010treatment cephalometric and demographic features were used. The primary outcome was overbite difference (OD = post\u2010treatment minus pre\u2010treatment overbite). For classification, patients were labeled as Decrease (OD &lt; \u22120.5\u2009mm;\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200937) versus Non\u2010Decrease (OD \u2265 \u22120.5\u2009mm;\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200950), combining No Change and Increase due to severe imbalance in the Increase subgroup. Six classifiers (logistic regression, random forest, extreme gradient boosting (XGBoost), Light Gradient Boosting Machine, support vector machine, k\u2010nearest neighbors) were evaluated using stratified fivefold cross\u2010validation and leave\u2010one\u2010out cross\u2010validation. Class imbalance was handled using class\u2010weighting and the Synthetic Minority Over\u2010sampling Technique (SMOTE) applied within the cross\u2010validation loop. For regression, nine algorithms were tested to predict continuous OD. Model explainability was assessed using SHapley Additive exPlanations (SHAP).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      In fivefold cross\u2010validation, XGBoost achieved the best discrimination (AUC\u2009=\u20090.734), outperforming logistic regression (AUC\u2009=\u20090.611). With SMOTE, the random forest achieved a comparable AUC (0.735) with improved sensitivity. Across regression models, cross\u2010validated R\n                      <jats:sup>2<\/jats:sup>\n                      values were near\u2010zero or negative, indicating limited ability to predict the exact magnitude of overbite change from baseline variables alone. SHAP ranked pre\u2010treatment overbite, ramus height, upper occlusal plane to SN angle, sella\u2010to\u2010condylion distance, and condylar positional indices among the most influential predictors for classification.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Interpretable ML enables improved risk stratification for clinically meaningful anterior overbite decrease after stabilization splint therapy in TMD patients, whereas predicting the exact magnitude of overbite change remains limited using pre\u2010treatment cephalometrics alone. These models may support individualized counseling and closer monitoring for patients at higher risk of open\u2010bite development during splint therapy.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/cre2.70447","type":"journal-article","created":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T05:55:36Z","timestamp":1788155736000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning Prediction of Anterior Open\u2010Bite Development After Stabilization Splint Treatment in Temporomandibular Disorder"],"prefix":"10.1002","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0379-8460","authenticated-orcid":false,"given":"Jae Joon","family":"Hwang","sequence":"first","affiliation":[{"name":"Department of Oral and Maxillofacial Radiology, Dental and Life Science Institute, School of Dentistry Pusan National University Yangsan Republic of Korea"},{"name":"Dental Research Institute Pusan National University Dental Hospital Yangsan Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0007-5662","authenticated-orcid":false,"given":"Hye\u2010Mi","family":"Jeon","sequence":"additional","affiliation":[{"name":"Department of Oral Medicine Pusan National University Hospital Busan Republic of Korea"},{"name":"Biomedical Research Institute Pusan National University Hospital Busan Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9252-6717","authenticated-orcid":false,"given":"Hye\u2010Min","family":"Ju","sequence":"additional","affiliation":[{"name":"Dental Research Institute Pusan National University Dental Hospital Yangsan Republic of Korea"},{"name":"Department of Oral Medicine, Dental and Life Science Institute, School of Dentistry Pusan National University Yangsan Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6296-4775","authenticated-orcid":false,"given":"Sung\u2010Hee","family":"Jeong","sequence":"additional","affiliation":[{"name":"Dental Research Institute Pusan National University Dental Hospital Yangsan Republic of Korea"},{"name":"Department of Oral Medicine, Dental and Life Science Institute, School of Dentistry Pusan National University Yangsan Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2197-0394","authenticated-orcid":false,"given":"Yong\u2010Woo","family":"Ahn","sequence":"additional","affiliation":[{"name":"Dental Research Institute Pusan National University Dental Hospital Yangsan Republic of Korea"},{"name":"Department of Oral Medicine, Dental and Life Science Institute, School of Dentistry Pusan National University Yangsan Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1776-371X","authenticated-orcid":false,"given":"Soo\u2010Min","family":"Ok","sequence":"additional","affiliation":[{"name":"Dental Research Institute Pusan National University Dental Hospital Yangsan Republic of Korea"},{"name":"Department of Oral Medicine, Dental and Life Science Institute, School of Dentistry Pusan National University Yangsan Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,8,30]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"crossref","DOI":"10.3390\/app122211864","article-title":"Trends and Application of Artificial Intelligence Technology in Orthodontic Diagnosis and Treatment Planning\u2014A Review","volume":"12","author":"Albalawi F.","year":"2022","journal-title":"Applied Sciences"},{"key":"e_1_2_12_3_1","first-page":"2175","article-title":"Integrating Machine Learning for Treatment Decisions in Anterior Open Bite Orthodontic Cases: A Retrospective Study","volume":"19","author":"Alqerban A.","year":"2024","journal-title":"Journal of Dental Sciences"},{"key":"e_1_2_12_4_1","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1145\/1007730.1007735","article-title":"A Study of the Behavior of Several Methods for Balancing Machine Learning Training Data","volume":"6","author":"Batista G. 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