Generating accurate sex estimation from hand X-ray images using AI deep-learning techniques: A study of limited bone regions

Abstract

Hand bone structure provides valuable features for sex estimation. This research introduces a novel approach using Artificial Intelligence (AI), specifically Convolutional Neural Networks (CNNs), to classify sex from hand X-ray images, focusing on the diagnostic potential of specific bone regions. We assess CNN performance on different hand skeleton areas, utilize Score-CAM to understand sex-discriminating features, and evaluate advanced CNN architectures. While the Xception model achieved the highest overall accuracy of 83.5% using complete hand X-rays, the InceptionResNetV2 model demonstrated remarkable efficiency by achieving 81.68% accuracy using only the proximal phalanx and metacarpal bones, maintaining a comparable AUC-ROC score of 0.92. Metacarpals of the first and second fingers were identified as key for differentiation. This approach demonstrates the power of AI in skeletal analysis and represents a significant step towards deployable AI tools for forensic and medical sex identification.