Dental Age Estimation From Mandibular Teeth in the Jordanian Population

Accurate estimation of dental age is crucial in forensic and clinical applications; however, traditional methods often suffer from subjectivity and observer bias. This study investigates the performance of advanced machine learning (ML) algorithms for estimating dental age based on features extracted from ConeBeam Computed Tomography (CBCT) images of mandibular canines and premolars in the Jordanian population. Eleven features encompassing volumetric measurements, morphological dimensions, and categorical dental aging stages were analyzed. Models were rigorously assessed using 1 0 -fold cross-validation across male, female, and combined datasets. CatBoost and Gradient Boosting regressors consistently outperformed conventional regression methods, demonstrating superior predictive accuracy as measured by Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Standard Error of Estimate (SEE). Notably, the CatBoost regressor exhibited exceptional robustness, achieving the best overall performance with a MAE of 7.14 ± 2.11 (Male), 6.19 ± 1.95 (Female), and 6.64 ± 1.15 (combined). Additionally, the Support Vector Regressor achieved the lowest MAE for females: 6.04 ± 2.45. Decision Tree and Gradient Boosting models also demonstrated commendable accuracy, further emphasizing the effectiveness of ensemble-based ML approaches. These results highlight the strong potential of ML techniques to enhance objectivity, accuracy, and applicability in dental age estimation by leveraging comprehensive dental feature sets.