Clinical and experimental dental research · 2026
Machine Learning Prediction of Anterior Open-Bite Development After Stabilization Splint Treatment in Temporomandibular Disorder.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Hwang Jae Joon, Jeon Hye-Mi, Ju Hye-Min, Jeong Sung-Hee, Ahn Yong-Woo, Ok Soo-Min
Аннотация
OBJECTIVES: 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.
MATERIALS AND METHODS: This retrospective cohort included 87 TMD patients treated with stabilization splints, physical therapy, and pharmacologic therapy. Thirty-five pre-treatment cephalometric and demographic features were used. The primary outcome was overbite difference (OD = post-treatment minus pre-treatment overbite). For classification, patients were labeled as Decrease (OD < -0.5 mm; n = 37) versus Non-Decrease (OD ≥ -0.5 mm; n = 50), 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-nearest neighbors) were evaluated using stratified fivefold cross-validation and leave-one-out cross-validation. Class imbalance was handled using class-weighting and the Synthetic Minority Over-sampling Technique (SMOTE) applied within the cross-validation loop. For regression, nine algorithms were tested to predict continuous OD. Model explainability was assessed using SHapley Additive exPlanations (SHAP).
RESULTS: In fivefold cross-validation, XGBoost achieved the best discrimination (AUC = 0.734), outperforming logistic regression (AUC = 0.611). With SMOTE, the random forest achieved a comparable AUC (0.735) with improved sensitivity. Across regression models, cross-validated R2 values were near-zero or negative, indicating limited ability to predict the exact magnitude of overbite change from baseline variables alone. SHAP ranked pre-treatment overbite, ramus height, upper occlusal plane to SN angle, sella-to-condylion distance, and condylar positional indices among the most influential predictors for classification.
CONCLUSION: 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-treatment cephalometrics alone. These models may support individualized counseling and closer monitoring for patients at higher risk of open-bite development during splint therapy.
MATERIALS AND METHODS: This retrospective cohort included 87 TMD patients treated with stabilization splints, physical therapy, and pharmacologic therapy. Thirty-five pre-treatment cephalometric and demographic features were used. The primary outcome was overbite difference (OD = post-treatment minus pre-treatment overbite). For classification, patients were labeled as Decrease (OD < -0.5 mm; n = 37) versus Non-Decrease (OD ≥ -0.5 mm; n = 50), 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-nearest neighbors) were evaluated using stratified fivefold cross-validation and leave-one-out cross-validation. Class imbalance was handled using class-weighting and the Synthetic Minority Over-sampling Technique (SMOTE) applied within the cross-validation loop. For regression, nine algorithms were tested to predict continuous OD. Model explainability was assessed using SHapley Additive exPlanations (SHAP).
RESULTS: In fivefold cross-validation, XGBoost achieved the best discrimination (AUC = 0.734), outperforming logistic regression (AUC = 0.611). With SMOTE, the random forest achieved a comparable AUC (0.735) with improved sensitivity. Across regression models, cross-validated R2 values were near-zero or negative, indicating limited ability to predict the exact magnitude of overbite change from baseline variables alone. SHAP ranked pre-treatment overbite, ramus height, upper occlusal plane to SN angle, sella-to-condylion distance, and condylar positional indices among the most influential predictors for classification.
CONCLUSION: 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-treatment cephalometrics alone. These models may support individualized counseling and closer monitoring for patients at higher risk of open-bite development during splint therapy.