Performance evaluation of deep learning-based osteoporosis diagnostic models with conventional chest X-ray in a clinical cohort

Background: Dual-energy X-ray absorptiometry (DXA) is the gold standard for diagnosing osteoporosis; however, its limited accessibility often hinders routine screening in primary care settings. To address this gap, we developed and evaluated a deep learning-based model, PROS^® CXR: OSTEO (ProMedius, Inc., Seoul, South Korea), which predicts osteoporosis from conventional chest radiographs. Methods: This retrospective study included 80 adult patients who underwent both DXA and chest radiography within a 3-month interval. The deep learning model, based on convolutional neural networks and trained via transfer learning, generated osteoporosis predictions from chest X-rays. Model performance was assessed against DXA-derived T-scores of the femur and lumbar spine, using either the minimum or average T-score per site as the reference standard. Results: The proposed model achieved an area under the curve (AUC) of 0.94 for femur and 0.93 for lumbar spine predictions. For osteoporosis screening, the sensitivity and specificity were 90% and 81%, respectively. Subgroup analysis demonstrated higher predictive performance in female patients, whereas false-positives (FPs) occurred more frequently in males. Conclusions: The PROS^® CXR: OSTEO model enables opportunistic and low-cost osteoporosis screening using routine chest radiographs. This approach holds promise for early detection in aging populations and resource-limited settings. Further optimization is required to improve specificity and minimize FPs before clinical implementation.