Related Experiment Video
Updated: Jan 16, 2026
Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
Published on: February 23, 2024
Deep learning-based method for estimating age from periapical radiographs of upper incisors in a Thai population
Praewpannarai Khruasarn1, Kasemsit Teeyapan2, Sangsom Prapayasatok1
1Division of Oral Radiology, Department of Oral Biology and Diagnostic Sciences, Faculty of Dentistry, Chiang Mai University, Chiang Mai, Thailand.
Purpose:
This study aimed to develop a deep learning model incorporating the pulp-to-tooth area ratio (PTR) for estimating age from periapical radiographs of upper central incisors in a Thai population.
Materials And Methods:
A total of 2,041 periapical radiographs, representing 3,108 upper central incisors from individuals aged 10.00 to 84.25 years, were analyzed. Root and root canal segmentation masks were generated using Labelbox. The dataset was randomly divided into training (2,175 teeth), validation (466 teeth), and test (467 teeth) sets. Model development proceeded in 3 steps: (1) DeepLabv3+ was trained to segment root and root canal regions. (2) ResNet-50 was trained to estimate age using both segmented images and PTR values derived from the masks. (3) The validation set guided model selection, and the final model was evaluated on the test set using mean absolute error (MAE).
Results:
The segmentation model (DeepLabv3+) achieved a mean intersection over union (mIoU) of 81.85%. The age estimation model (ResNet-50) yielded an overall MAE of 6.14 years. For age-specific evaluation, test data were grouped into 4 categories: 10-19 years, 20-39 years, 40-59 years, and ≥60 years. The MAE was lowest in the 10-19 years group (3.74 years) and progressively increased with age: 6.07, 6.58, and 8.40 years, respectively.
Conclusion:
This study demonstrated that integrating deep learning models with PTR measurement offers a promising method for dental radiographic age estimation using a single tooth.
