Opportunistic Screening of Osteoporosis Using Plain Film Chest X-ray

TL;DR

This paper introduces a novel method to predict bone mineral density from routine chest X-rays, enabling accessible osteoporosis screening with high accuracy and potential for early diagnosis.

Contribution

It is the first to use chest X-ray scans for spine BMD prediction, combining ROI detection and multi-ROI modeling for improved accuracy.

Findings

Strong correlation (0.840) between predicted and DXA BMD.

High classification performance with AUC 0.936 for osteoporosis screening.

Potential to enable routine, early osteoporosis detection.

Abstract

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.