Using Kvaal method and machine learning to improve adult dental age estimation with CBCT

Accurate age estimation in adults remains challenging in forensic practice. This study aimed to improve dental age estimation by using Kvaal method and machine learning with cone-beam computed tomography (CBCT). CBCT scans of 400 Northern Chinese individuals aged 21-70 years were analyzed, and Kvaal-derived indices were measured for selected teeth. Sex-specific multiple linear regression models were developed as baseline methods and compared with two machine-learning regressors: Random Forest (RF) and Extreme Gradient Boosting (XGBoost). Data measurement reliability was excellent (ICC values exceeding 0.95). Width-related indices showed stronger correlations with age than length-related indices, and Kvaal's indices exhibited significant sex differences. On the independent test set, traditional linear regression models yielded mean absolute error (MAE) of 8.16-11.22 years. Both RF and XGBoost clearly outperformed linear regression, reducing MAE by about 15-25% in most tooth position. The best-performing models for the RF model and the XGBoost model were male maxillary second premolars (tooth 15/25; MAE 6.72 years) and the male mandibular lateral incisors (tooth 32/42; MAE 7.18 years), respectively. These findings indicate that CBCT-based dental age estimation combined with machine learning modestly improves accuracy over the conventional Kvaal approach, better capturing complex patterns in age-related dental changes and providing more reliable age estimates for forensic applications in the studied population. Further validation across diverse populations is necessary. However, the proposed strategy would be a complementary option within forensic dental age estimation.