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Development of a fully automated dental age estimation framework from panoramic radiographs using tooth-level
Witsarut Upalananda1,2, Sangsom Prapayasatok3, Sakarat Na Lampang3
1Department of Biomedical Sciences and Biomedical Engineering, Faculty of Medicine, Prince of Songkla University, Hatyai, Songkhla 90110, Thailand.
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 1639 radiographs from individuals aged 8 to 23 years was used. The proposed 2-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.
Conclusions:
This study developed an explainable, high-performing deep learning framework that offers a promising solution for real-world age estimation in the forensic domain.
