Dental Age-Group Classification from Panoramic Radiographs Using Convolutional Neural Networks

Abstract

Background/Objectives: Determining chronological age is important in several domains, including forensic identification, clinical decision-making, legal matters, and immigration procedures. Dental tissues are widely recognized as reliable indicators of age because they undergo gradual and measurable structural changes throughout life. Nevertheless, most conventional dental methods show limited reliability when applied to adults and elderly individuals. The objective of this study was to investigate an automated deep learning-based approach for age-group classification in adults and seniors using panoramic dental radiographs. Methods: Panoramic dental radiographs were analyzed using a custom-designed Convolutional Neural Network (CNN) along with several established pre-trained deep learning architectures. The dataset consisted of 1469 radiographic images obtained from Egyptian individuals aged between 25 and 70 years. Images were classified into five predefined age categories using a classification-based framework, and the models were trained to learn age-related dental patterns from radiographic images. Results: The proposed Custom CNN achieved the highest accuracy of 85.2%, outperforming YOLOv8 (79.1%) and all other evaluated models, with the lowest prediction error (MAE = 1.92 years; RMSE = 5.46 years). Overall, the deep learning models demonstrated strong performance in classifying dental age groups, particularly within adult and senior populations, where conventional methods often show reduced reliability. Conclusions: The findings suggest that deep learning analysis of panoramic dental radiographs may serve as a supportive tool for age-group classification in adult populations, complementing rather than replacing traditional assessment methods. These results, while promising, are limited to the dataset and experimental conditions of this study, and broader applicability requires further validation across diverse populations and settings.