Development of A Fully Automated Dental Age Estimation Framework from Panoramic Radiographs Using Tooth-Level Information with an Attention-Weighting Module.

Objective: This study aimed to develop a fully automated and explainable framework for dental age estimation from panoramic radiographs in young individuals.

Methods: A dataset of 1,639 radiographs from individuals aged 8 to 23 years was used. The proposed two-stage pipeline involved: (1) oriented tooth detection using the YOLO11-OBB model, and (2) age estimation using deep learning-based regression models with an attention-weighting module to aggregate predictions from individual teeth. Auxiliary features, including the presence of deciduous teeth and sex, were also evaluated for their impact on model performance.

Results: For the first stage, the tooth detection model achieved an F1-score of 0.981, demonstrating accurate tooth localization and identification. In the later stage, the best-performing model, DenseNet-121 with the deciduous teeth feature, achieved a mean absolute error (MAE) of 1.05 ± 0.95 years. Compared to traditional methods, the proposed framework significantly reduced the MAE.

Conclusion: This study developed an explainable, high-performing deep learning framework offers a promising solution for real-world age estimation in the forensic domain.