Ulubaba, Hilal ErAtik, IpekCiftci, RukiyeEken, OzgurAldhahi, Monira I.2026-04-042026-04-0420251471-2342https://doi.org/10.1186/s12880-025-01809-8https://hdl.handle.net/11616/108975BackgroundAccurate gender estimation plays a crucial role in forensic identification, especially in mass disasters or cases involving fragmented or decomposed remains where traditional skeletal landmarks are unavailable. This study aimed to develop a deep learning-based model for gender classification using hand radiographs, offering a rapid and objective alternative to conventional methods.MethodsWe analyzed 470 left-hand X-ray images from adults aged 18 to 65 years using four convolutional neural network (CNN) architectures: ResNet-18, ResNet-50, InceptionV3, and EfficientNet-B0. Following image preprocessing and data augmentation, models were trained and validated using standard classification metrics: accuracy, precision, recall, and F1 score. Data augmentation included random rotation, horizontal flipping, and brightness adjustments to enhance model generalization.ResultsAmong the tested models, ResNet-50 achieved the highest classification accuracy (93.2%) with precision of 92.4%, recall of 93.3%, and F1 score of 92.5%. While other models demonstrated acceptable performance, ResNet-50 consistently outperformed them across all metrics. These findings suggest CNNs can reliably extract sexually dimorphic features from hand radiographs.ConclusionsDeep learning approaches, particularly ResNet-50, provide a robust, scalable, and efficient solution for gender prediction from hand X-ray images. This method may serve as a valuable tool in forensic scenarios where speed and reliability are critical. Future research should validate these findings across diverse populations and incorporate explainable AI techniques to enhance interpretability.eninfo:eu-repo/semantics/openAccessGender predictionHand radiographDeep learningConvolutional neural network (CNN)Forensic identificationResNet-50Artificial intelligenceMass disaster responseArticle2514059774810.1186/s12880-025-01809-82-s2.0-105009738955N/AWOS:001522890200004Q10000-0003-2124-45250000-0002-5488-3158