Opportunistic Screening of Osteoporosis Using Plain Film Chest X-ray
This addresses the limited access to bone density exams for osteoporosis screening, offering a low-cost alternative using routine chest X-rays, though it is an incremental application of existing methods to a new medical imaging domain.
The paper tackled the problem of under-diagnosis of osteoporosis by proposing a method to predict bone mineral density (BMD) from chest X-rays, achieving a Pearson correlation of 0.840 with gold-standard DXA and an AUC of 0.936 for osteoporosis screening.
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.