Automated Estimation of Chronological Age from Panoramic Dental X-Ray Images Using Deep Learning

Abstract

Accurate age estimation holds significant clinical and social value in medical diagnosis, forensic identification, and population health management. Teeth, due to the biological stability of their mineralized tissue, have been proven to be reliable biomarkers for age inference in forensic science. However, traditional manual evaluation methods are subjective and prone to significant errors. To achieve automated age assessment from panoramic dental X-ray images, this paper proposes a hybrid deep learning architecture that innovatively integrates InceptionResNetV2, Spatial Transformer Networks (STN), and Feature Pyramid Networks (FPN) to enable adaptive spatial normalization and multi-scale feature extraction from dental images. Additionally, we developed an intelligent data augmentation method based on reinforcement learning and an improved loss function design, significantly enhancing the model's generalization capability and training stability. The model was validated using a dataset of 2,157 patient dental panoramic X-rays, and the results showed significant improvements in age prediction performance: the Mean Absolute Error (MAE) was 1.50 years, Mean Squared Error (MSE) was 2.25, and the Coefficient of Determination (R2) reached 0.90, outperforming the current state-of-the-art methods by 54.3%, 88.2%, and 4.7%, Arespectively. These results confirm the potential clinical application value of this method in automated dental age assessment.