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    Deep learning for gender estimation using hand radiographs: a comparative evaluation of CNN models
    (Bmc, 2025) Ulubaba, Hilal Er; Atik, Ipek; Ciftci, Rukiye; Eken, Ozgur; Aldhahi, Monira I.
    BackgroundAccurate 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.
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    Deep Learning for Sex Estimation from Whole-Foot X-Rays: Benchmarking CNNs for Rapid Forensic Identification
    (Mdpi, 2025) Ciftci, Rukiye; Atik, Ipek; Eken, Ozgur; Aldhahi, Monira I.
    Background: Accurate sex estimation is crucial in forensic identification when DNA analysis is impractical or remains are fragmented. Traditional anthropometric approaches often rely on single bone measurements and yield moderate levels of accuracy. Objective: This study aimed to evaluate deep convolutional neural networks (CNNs) for automated sex estimation using entire foot radiographs, an approach rarely explored. Methods: Digital foot radiographs from 471 adults (238 men, 233 women, aged 18-65 years) without deformities or prior surgery were retrospectively collected at a single tertiary center. Six CNN architectures (AlexNet, ResNet-18, ResNet-50, ShuffleNet, GoogleNet, and InceptionV3) were trained using transfer learning (70/15/15 train-validation-test split, data augmentation). The model performance was assessed using accuracy, sensitivity, specificity, precision, and F1-score. Results: InceptionV3 achieved the highest accuracy (97.1%), surpassing previously reported methods (typically 72-89%), with balanced sensitivity (97.5%) and specificity (96.8%). ResNet-50 followed closely (95.7%), whereas simpler networks, such as AlexNet, underperformed (90%). Conclusions: Deep learning applied to whole-foot radiographs delivers state-of-the-art accuracy for sex estimation, enabling rapid, reproducible, and cost-effective forensic identification when DNA analysis is delayed or unavailable, such as in mass disasters or clinical emergency settings.
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    Detection of common bile duct dilatation on magnetic resonance cholangiopancreatography by deep learning
    (Turkish Soc Radiology, 2025) Ulubaba, Hilal Er; Ciftci, Rukiye; Atik, Ipek; Karakul, Osman Furkan
    PURPOSE This study aims to detect common bile duct (CBD) dilatation using deep learning methods from artificial intelligence algorithms. METHODS To create a convolutional neural network (CNN) model, 77 magnetic resonance cholangiopancreatography (MRCP) images without CBD dilatation and 70 MRCP images with CBD dilatation were used. The system was developed using coronal maximum intensity projection reformatted 3D-MRCP images. The ResNet50, DenseNet121, and visual geometry group models were selected for training, and detailed training was performed on each model. RESULTS In the study, the DenseNet121 model showed the best performance, with a 97% accuracy rate. The ResNet50 model ranked second, with a 96% accuracy rate. CONCLUSION CBD dilatation was detected with high performance using the DenseNet CNN model. Once validated in multicenter studies with larger datasets, this method may help in diagnosis and treatment decision-making. CLINICAL SIGNIFICANCE Deep learning algorithms can aid clinicians and radiologists in the diagnostic process once technical, ethical, and financial limitations are addressed. Fast and accurate diagnosis is crucial for accelerating treatment, reducing complications, and shortening hospital stays.