Automated and Precise Bone Mineral Density Prediction and Fracture Risk Assessment using Hip/Lumbar Spine Plain Radiographs via Learning Deep Image Signatures and Correlations

Abstract Dual-energy X-ray absorptiometry (DXA) and the Fracture Risk Assessment Tool are recommended tools for osteoporotic fracture risk evaluation, but are underutilized. We present a novel and fully-automated tool to identify fractures, predict bone mineral density (BMD), and evaluate fracture risk using plain pelvis and lumbar spine radiographs. The performance of this tool were evaluated in 1639 and 11908 patients with pelvis or lumbar spine radiographs and DXA, respectively. The model was well calibrated for hip and spine BMD assessments with minimal or no bias. The area under the curve and accuracy were 0.89 and 92.4% for hip osteoporosis, 0.87 and 86.8% for spine osteoporosis, 0.92 and 94.6% for high 10-year major fracture risk, and 0.92 and 92.2% for high hip fracture risk, respectively. The success rates of our automated algorithm a real-world test were 85.3% and 90.4% for hip and spine, respectively. The clinical use of this automated tool may increase the likelihood of identifying high-risk patients in previously unscreened populations.