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

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 Xray 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.