The future of DXA: How AI is transforming bone health diagnostics

Dual-energy X-ray absorptiometry (DXA) remains the clinical gold standard for assessing bone mineral density (BMD), guiding diagnosis and therapeutic decisions. However, conventional DXA analysis suffers from several limitations, including insensitivity to early microarchitectural changes, operator dependence, limited availability in primary care settings, and an insufficient ability to predict fracture risk when used alone. Artificial intelligence (AI), incorporating machine learning (ML) and deep learning (DL), offers transformative potential in enhancing DXA-based bone health assessment. The purpose of this review is to describe the integration of AI algorithms into DXA image interpretation, highlighting improvements in diagnostic accuracy, and fracture risk stratification beyond traditional methods. Using AI-driven models, complex features of DXA images can be extracted, increasing sensitivity to microstructural deterioration that is typically not detected by standard BMD measurements. A combination of quantitative image features and comprehensive demographic and clinical data enhance the early detection of osteoporosis and fracture susceptibility, enabling personalised treatment strategies. In comparison to classical DXA or fracture risk assessment tool (FRAX) algorithms, convolutional neural networks (CNNs) and ensemble methods demonstrate superior predictive performance, with average area under the curve (AUC) values often about 0.90. In addition to minimising inter-operator variability and improving reproducibility, AI improves DXA technical challenges such as region-of-interest selection and image segmentation. In addition to providing indirect measurements of bone microarchitecture, AI-enabled indices, such as the trabecular bone score (TBS), contribute to the improvement of fracture risk prediction. It has been demonstrated in large-scale clinical validations that AI-assisted DXA can enhance bone health diagnostic capability.