AI-assisted age estimation from occlusal tooth wear using biofluorescence imaging.

This proof-of-concept study evaluated the feasibility of an AI-based age estimation model using an occlusal tooth wear parameter (ΔFwear) quantified from biofluorescence. Quantitative light-induced fluorescence (QLF) images from 104 adults (20–70 years; 2,733 teeth) were analyzed. To prevent data leakage, the dataset was split at the participant level. A random forest (RF) regressor was optimized, and recursive feature elimination with cross-validation (RFECV) identified efficient tooth subsets. Final models were validated using an independent test set, and correlations between mean ΔFwear and chronological age were assessed. Cross-validation (CV) performance peaked with three teeth; however, independent testing showed that a model incorporating seven key teeth achieved the best generalization performance. This 7-tooth model achieved a mean absolute error (MAE) of 7.49 years (95% CI: 5.90–9.17), comparable to the full 28-tooth model (MAE: 7.27 years; p = 0.79), with a stronger Pearson correlation with age (r = 0.78 vs. 0.71) and an equivalent R2 of 0.61. These findings support the feasibility of integrating ΔFwear with an interpretable machine-learning framework for non-invasive age estimation. While the reduced 7-tooth model offers analytical efficiency, further validation in larger and more diverse cohorts is required to confirm its generalizability for broader forensic or epidemiological applications.