  Osteoporosis is a common chronic metabolic bone disease often under-diagnosed
and under-treated due to the limited access to bone mineral density (BMD)
examinations, e.g. via Dual-energy X-ray Absorptiometry (DXA). This paper
proposes a method to predict BMD from Chest X-ray (CXR), one of the most
commonly accessible and low-cost medical imaging examinations. Our method first
automatically detects Regions of Interest (ROIs) of local CXR bone structures.
Then a multi-ROI deep model with transformer encoder is developed to exploit
both local and global information in the chest X-ray image for accurate BMD
estimation. Our method is evaluated on 13719 CXR patient cases with ground
truth BMD measured by the gold standard DXA. The model predicted BMD has a
strong correlation with the ground truth (Pearson correlation coefficient 0.894
on lumbar 1). When applied in osteoporosis screening, it achieves a high
classification performance (average AUC of 0.968). As the first effort of using
CXR scans to predict the BMD, the proposed algorithm holds strong potential for
early osteoporosis screening and public health promotion.
