  Osteoporosis is a common chronic metabolic bone disease that is often
under-diagnosed and under-treated due to the limited access to bone mineral
density (BMD) examinations, Dual-energy X-ray Absorptiometry (DXA). In this
paper, we propose a method to predict BMD from Chest X-ray (CXR), one of the
most common, accessible, and low-cost medical image examinations. Our method
first automatically detects Regions of Interest (ROIs) of local and global bone
structures from the CXR. Then a multi-ROI model is developed to exploit both
local and global information in the chest X-ray image for accurate BMD
estimation. Our method is evaluated on 329 CXR cases with ground truth BMD
measured by DXA. The model predicted BMD has a strong correlation with the gold
standard DXA BMD (Pearson correlation coefficient 0.840). When applied for
osteoporosis screening, it achieves a high classification performance (AUC
0.936). As the first effort in the field to use CXR scans to predict the spine
BMD, the proposed algorithm holds strong potential in enabling early
osteoporosis screening through routine chest X-rays and contributing to the
enhancement of public health.
