Use of deep learning in forensic sex estimation of virtual pelvic models from the Han population

Abstract

Accurate sex estimation is crucial to determine the identity of human skeletal remains effectively. Here, we developed convolutional neural network (CNN) models for sex estimation on virtual hemi-pelvic regions, including the ventral pubis (VP), dorsal pubis (DP), greater sciatic notch (GSN), pelvic inlet (PI), ischium, and acetabulum from the Han population and compared these models with two experienced forensic anthropologists using morphological methods. A Computed Tomography (CT ) dataset of 862 individuals was divided into the subgroups of training, validation, and testing, respectively. The CT -based virtual hemi-pelvises from the training and validation groups were used to calibrate sex estimation models; and then a testing dataset was used to evaluate the performance of the trained models and two human experts on the sex estimation of specific pelvic regions in terms of overall accuracy, sensitivity, specificity, F1 score, and receiver operating characteristic (ROC) curve. Except for the ischium and acetabulum, the CNN models trained with the VP, DP, GSN, and PI images achieved excellent results with all the prediction metrics over 0.9. All accuracies were superior to those of the two forensic anthropologists in the independent testing. Notably, the heatmap results confirmed that the trained CNN models were focused on traditional sexual anatomic traits for sex classification. This study demonstrates the potential of AI techniques based on the radiological dataset in sex estimation of virtual pelvic models. The excellent sex estimation performance obtained by the CNN models indicates that this method is valuable to proceed with in prospective forensic trials.