Deep Learning Approaches for Osteoporosis Detection: Advances, Challenges, and Future Directions

R. Revathi, Dr. M. Vijayakumar

Abstract: Osteoporosis is a chronic and progressive skeletal disease that is characterised by a reduction in bone mineral density (BMD) and progressive deterioration of bone microarchitecture, which increases fracture risk. Its largely asymptomatic presentation frequently results in delayed detection, being already in advanced stages of the disease until severe fractures and consequent morbidity appear, so the early and correct diagnosis is fundamental for the implementation of effective interventions and the improvement of patient outcomes. Recent advances in artificial intelligence, particularly in deep learning (DL), have demonstrated significant potential in automating the detection of osteoporosis, estimating BMD, and predicting fracture risk. This review systematically examines fifty research studies involving models based on machine learning (ML) and deep learning (DL), as well as a variety of medical imaging modalities, including X-ray, computed tomography (CT), low-dose computed tomography (LDCT), and magnetic resonance imaging (MRI). Radiography-based convolutional neural network (CNN) models were found to have diagnostic accuracy in the range of 0.74 to 0.93, with ensemble and attention-driven architectures beating the conventional single model frameworks. Models using CT and LDCT images achieved an excellent level of precision (AUROC > 0.90) for BMD predictions and for fracture risk assessment, and the performance of MRI-revealing methods was better for the detection and characterization of vertebral fractures. Multimodal learning approaches incorporating imaging, clinical, and demographic features further improved both the predictive robustness and interpretability. Despite promising advances, there are still considerable research limitations, such as small sample sizes, class imbalance, high computational costs, and a lack of external or cross-population validation. Future investigations should focus on the collection of large and multi-institutional datasets, the development of explainable and privacy-preserving AI models, the improvement of advanced feature-fusion strategies, and the implementation of real-time and clinically integrable decision support systems. Overall, this review highlights the increasing role of deep learning in the diagnosis of osteoporosis and outlines the future direction of deep learning toward clinically reliable, scalable, and interpretable artificial intelligence (AI)-driven solutions.

Keywords: Osteoporosis detection; Deep learning algorithms; Bone mineral density, X-ray, Computed tomography (CT); Magnetic resonance imaging (MRI); Artificial Intelligence

How to Cite?: R. Revathi, Dr. M. Vijayakumar, "Deep Learning Approaches for Osteoporosis Detection: Advances, Challenges, and Future Directions", Volume 15 Issue 3, March 2026, International Journal of Science and Research (IJSR), Pages: 1838-1848, https://www.ijsr.net/getabstract.php?paperid=SR26328142657, DOI: https://dx.dx.doi.org/10.21275/SR26328142657

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