New algorithm for the forensic estimation of sex from chest radiographs using artificial intelligence

Abstract

Sex estimation by forensic investigators can be enhanced through the use of artificial intelligence, which has shown considerable promise in the interpretation of medical images. This study introduces a novel multimodal deep learning framework that combines chest radiographic features with manually annotated anatomical landmarks, enabling more accurate sex prediction. This retrospective study included chest radiographs from 563 Egyptian patients obtained from the Department of Diagnostic and Interventional Radiology, Faculty of Medicine, Cairo University. A custom deep convolutional neural network was developed comprising two parallel branches: (1) an image-processing branch utilizing residual convolutional blocks with batch normalization, skip connections, max pooling, global average pooling, and fully connected layers; and (2) an anatomy branch processing 45 structured anatomical features derived from one JSON annotation file. Features from both branches were fused through concatenation and classified using fully connected layers with dropout regularization and a sigmoid output layer. The model achieved an overall accuracy of 83% and Area under the curve = 0.8776. Class-wise performance showed a precision, recall, and F1-score of 0.79, 0.89, and 0.84, respectively, for female samples, and 0.82, 0.68, and 0.75, respectively, for male samples. Upon further study of the results, sex classification accuracy reached 90% when anatomical landmark annotations were incorporated into the prediction process. Although the model learned anatomical relationships during training, some predictions were generated without utilizing this anatomical information and resulted in a lower accuracy of 71%. The proposed multimodal framework provides a reliable approach for sex estimation by combining chest radiographs with structured anatomical annotations. The findings highlight the importance of annotation-guided models in forensic identification and support the development of large-scale national radiographic datasets to facilitate the implementation of artificial intelligence-assisted medico-legal assessments, particularly in scenarios requiring positive identification such as disaster victim identification.