Automated sex and age estimation from orthopantomograms using deep learning: A comparison with human predictions

Abstract

Introduction/objectives: Estimating sex and chronological age is crucial in forensic dentistry and forensic identification. Traditional manual methods for sex and age estimation are labor-intensive, time-consuming, and prone to errors. This study aimed to develop an automatic and robust method for estimating sex and chronological age from orthopantomograms using a multi-task deep learning network. Methods: A deep learning model was developed using a multi-task learning approach with a backbone network and separate attention branches for sex and age estimation. The dataset comprised 2067 orthopantomograms, evenly distributed across sex and age groups ranging from 3 to 89 years. The model was trained using the VGG backbone, optimizing for both sex classification and age regression tasks. Performance was evaluated using mean absolute error (MAE), coefficient of determination (R2), and classification accuracy. Results: The developed model demonstrated outstanding performance in chronological age estimation, achieving a mean absolute error (MAE) of 3.43 years and a coefficient of determination (R2) of 0.941. For sex estimation, the model achieved an accuracy of 90.2 %, significantly outperforming human observers, whose accuracy ranged from 46.3 % to 63 % for sex prediction and from 16.4 % to 91.3 % for age estimation. Conclusions: The proposed multi-task deep learning model provides a highly accurate and automated method for estimating sex and chronological age from orthopantomograms. Compared to human predictions, the model exhibited superior accuracy and consistency, highlighting its potential for forensic applications.