Estimating femoral neck bone mineral density from distal radius x-rays using deep learning
Abstract
Osteoporosis is a systemic disease characterized by low Bone Mineral Density (BMD) caused by an imbalance between bone formation and resorption. Gold standard BMD assessments rely on dual energy X-ray absorptiometry (DXA) scans of the hip, however uptake among eligible patients remains low, highlighting the need for alternative assessment methods. In this work, we propose a simple fusion strategy that combines information from distal radius -rays with basic patient information (i.e. sex), and imaging acquisition parameters (i.e. machine settings) to predict femoral neck BMD, the primary metric for osteoporosis diagnosis. Rather than relying on complex or hard-toimplement methods, our approach is intentionally straightforward. A Vision Transformer (ViT) model processes the Xray image, and its features are directly fused with metadata using a lightweight design. This minimalist fusion strategy makes our solution easy to integrate into clinical workflows and robust against overfitting or unnecessary complexity. Among five tested models (DenseNet121, ResNet50, VGG16, ViT, and Swin), our simple ViT-based fusion model achieved the best results, with an MAE of 0.813, accuracy of 0.733, precision of 0.829, recall of 0.600, F1 score of 0.689, specificity of 0.865, NPV of 0.687, and AUC of 0.820. Our findings also show that including even basic metadata can boost performance. This study demonstrates that a simple fusion design, pairing image features with essential patient and acquisition data, can achieve promising performance and is well-suited for routine osteoporosis screening.
Keywords
wrist, hands, bone density x-ray imaging
Document Type
Conference Proceeding
Date of Publication
2025
Publication Title
2025 International Conference on Digital Image Computing: Techniques and Applications (DICTA)
Publisher
IEEE Xplore
School
School of Science
RAS ID
100246
Copyright
subscription content
Recommended Citation
Al-Fawa'reh, M., Abela, B., Kelly, L., Abu-Khalaf, J., Masek, M., Suter, D., & Gupta, A. (2025, December). Estimating femoral neck bone mineral density from distal radius X-rays using deep learning. In 2025 International Conference on Digital Image Computing: Techniques and Applications (DICTA) (pp. 1-10). IEEE. https://doi.org/10.1109/DICTA68720.2025.11302425