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

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.

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.

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.

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.