A fully automated sex estimation for proximal femur X-ray images through deep learning detection and classification

Abstract

Purpose: To develop a fully automated deep learning pipeline using digital radiographs to detect the proximal femur region for accurate automated sex estimation.

Method: Radiograph predictive features from 2122 Chinese Han clinical pelvic with ages ranging from 18 to 26 years were collected retrospectively to train and test the sex prediction model using deep machine learning's convolutional neural networks (CNN). Model performance was assessed using a Chinese Han population with 361 samples and a white population with 50 samples. The average accuracy of the sex estimation of the two test datasets was determined.

Results: For the Chinese Han population test dataset, the sex estimation accuracy was 94.6% (males: 93.9% and females: 94.7%). For the white population samples, the accuracy of sex estimation was 82.9% (males: 80.9% and females: 88.6%). The accuracy of CNN tested in the Chinese population was significantly higher than that tested in the White population (p < 0.001) CONCLUSIONS: The model based on convolutional neural networks has an accuracy similar to that of current state-of-the-art mathematical functions using manually extracted features for the Chinese Han population samples, proving to be a reliable choice for the human sex estimation.

Keywords: Deep learning; Femur; Forensic anthropology; Sex determination analysis.