Automatic sex estimation using deep convolutional neural network based on orthopantomogram images

Sex estimation is very important in forensic applications as part of individual identification. Morphological sex estimation methods predominantly focus on anatomical measurements. Based on the close relationship between sex chromosome genes and facial characterization, craniofacial hard tissues morphology shows sex dimorphism. In order to establish a more labor-saving, rapid, and accurate reference for sex estimation, the study investigated a deep learning network-based artificial intelligence (AI) model using orthopanto-mograms (OPG) to estimate sex in northern Chinese subjects. In total, 10703 OPG images were divided into training (80%), validation (10%), and test sets (10%). At the same time, different age thresholds were selected to compare the accuracy differences between adults and minors. The accuracy of sex estimation using CNN (convolutional neural network) model was higher for adults (90.97%) compared with minors (82.64%). This work demonstrated that the proposed model trained with a large dataset could be used in automatic morphological sex-related identification with favorable performance and practical significance in forensic science for adults in northern China, while also providing a reference for minors to some extent.(c) 2023 Elsevier B.V. All rights reserved.

Automated summary

Sex estimation is crucial for individual identification in forensic applications. This study developed an artificial intelligence model based on a deep learning network to estimate sex using orthopantomograms (OPG) in northern Chinese subjects. The accuracy of sex estimation using the CNN model was higher for adults (90.97%) compared to minors (82.64%). This model shows promise for automated morphological sex-related identification in forensic science for adults in northern China and provides a reference for minors.