Enhancing Bone Mineral Density Estimation from X-ray Images with Cross-Modal Knowledge Distillation

Dual-energy X-ray absorptiometry (DXA) enables accurate bone mineral density but requires specialized equipment and protocols. X-ray-based BMD screening offers opportunistic early detection, though prior methods struggle with X-ray intensity variations and demand large datasets. We introduce a cross-modal knowledge distillation BMD prediction framework (CMKD-BMD) fusing X-ray/CT data to enhance X-ray-only BMD prediction. Each single-modal student network employs a multi-scale visual extractor for hierarchical features, an unsupervised graph-based structural learner for anatomical relationships, and an adaptive fusion module to generate a unified representation. The teacher network integrates single-modal representations from students and transfers multimodal knowledge from the teacher to students. Our model outperforms existing methods on both the collected dataset (comprising 1620 X-rays and 280 CT cases) and the publicly available VerSe2019 dataset, demonstrating superior BMD estimation performance. Furthermore, we developed OrthoSim, an orthopedic surgical simulation platform with CMKD-BMD, which has shown promising clinical effectiveness in trial evaluations. Our code is available at https://github.com/KeyueShi/CMKD-BMD.