Development and evaluation of a deep learning-based system for dental age estimation using the demirjian method on panoramic radiographs.

Abstract

Background: To develop and evaluate a deep learning-based model for automatic dental age estimation using the Demirjian method on panoramic radiographs, and to compare its performance with the traditional manual approach.

Materials and methods: A total of 4,800 panoramic radiographs (mean age: 10.64 years) were used to train, validate, and test a YOLOv11-based deep learning model for tooth development staging. Model performance was evaluated using precision, recall, F1 score, and mAP metrics. In addition, a separate dataset of 650 individuals (325 females, 325 males) was used to compare chronological age, manual Demirjian assessments, and AI-assisted estimations through repeated-measures ANOVA and linear regression analysis.

Results: The model achieved its highest performance in the 2nd Molar-H group (Precision: 0.99, Recall: 1.0, F1: 0.995), and its lowest in the 1st Molar-B group (Precision: 0.471, F1: 0.601). Both manual and AI-assisted Demirjian methods significantly overestimated chronological age (p < 0.001), but no significant difference was observed between them (p = 0.433). Regression analysis indicated a weak but statistically significant relationship between age and prediction error, more pronounced in the AI-assisted model (R² = 0.042).

Conclusion: The AI-assisted system demonstrated comparable accuracy to the manual Demirjian method and showed higher performance in later stages of tooth development. The developed Python script and graphical interface allow for rapid, scalable, and user-friendly application of the method. While the system shows promise for use in clinical and forensic settings, broader validation with diverse populations and alternative model architectures is recommended before clinical deployment.