Utility of Osteoporosis Screening Based on Estimation of Bone Mineral Density Using Bidirectional Chest Radiographs with Deep Learning Models

Akifumi Yoshida ¹ Yoichi Sato ² Chiharu Kai ^1,3 Yuta Hirono ^3,4 Ikumi Sato ^3,5 Satoshi Kasai ^1*

• ¹ Department of Radiological Technology, Faculty of Medical Technology, Niigata University of Health and Welfare, Niigata, Niigata, Japan
• ² Graduate School of Medicine, Nagoya University, Nagoya, Aichi, Japan
• ³ Major in Health and Welfare, Graduate School of Niigata University of Health and Welfare, Niigata, Niigata, Japan
• ⁴ TOITU Co., Ltd., Tokyo, Japan
• ⁵ Department of Nursing, Faculty of Nursing, Niigata University of Health and Welfare, Niigata, Niigata, Japan

Osteoporosis increases the risk of fragility fractures, especially of the lumbar spine and femur. As fractures affect life expectancy, it is crucial to detect the early stages of osteoporosis. Dual X-ray absorptiometry (DXA) is the gold standard for bone mineral density (BMD) measurement and the diagnosis of osteoporosis; however, its low screening usage is problematic. The accurate estimation of BMD using chest radiographs (CXR) could expand screening opportunities. This study aimed to indicate the clinical utility of osteoporosis screening using deep-learning-based estimation of BMD using bidirectional CXRs. This study included 1,624 patients aged ≥ 20 years who underwent DXA and bidirectional (frontal and lateral) chest radiography at a medical facility. A dataset was created using BMD and bidirectional CXR images. Inception-ResNet-V2-based models were trained using three CXR input types (frontal, lateral, and bidirectional). We compared and evaluated the BMD estimation performances of the models with different input information. In the comparison of models, the model with bidirectional CXR showed the highest accuracy. The correlation coefficients between the model estimates and DXA measurements were 0.766 and 0.683 for the lumbar spine and femoral BMD, respectively. Osteoporosis detection based on bidirectional CXR showed higher sensitivity and specificity than the models with single-view CXR input, especially for osteoporosis based on T-score ≤ -2.5, with 92.8% sensitivity at 50.0% specificity. These results suggest that bidirectional CXR contributes to improved accuracy of BMD estimation and osteoporosis screening compared with single-view CXR. This study proposes a new approach for early detection of osteoporosis using a deep learning model with frontal and lateral CXR inputs. BMD estimation using bidirectional CXR showed improved detection performance for low bone mass and osteoporosis, and has the potential to be used as a clinical decision criterion. The proposed method shows potential for more appropriate screening decisions, suggesting its usefulness in clinical practice.