Real-World Validation of an AI-Based Opportunistic Osteoporosis Risk Stratification Tool Using Chest Radiographs in a Male-Predominant Cohort.
Authors
Affiliations (7)
Affiliations (7)
- Department of Orthopaedics, Taichung Veterans General Hospital, Taichung 40705, Taiwan.
- Doctoral Program in Tissue Engineering and Regenerative Medicine, National Chung Hsing University, Taichung 402202, Taiwan.
- Department of Industrial Engineering and Enterprise Information, Tunghai University, Taichung 407224, Taiwan.
- Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung 402202, Taiwan.
- Department of Computer Science and Information Engineering, Providence University, Taichung 43301, Taiwan.
- Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, Zhunan, Miaoli County 35053, Taiwan.
- Institute of NanoEngineering and MicroSystems, National Tsing Hua University, Hsinchu 30013, Taiwan.
Abstract
<b>Background:</b> Osteoporosis is a major public health concern and a leading cause of fragility fractures, yet screening with dual-energy X-ray absorptiometry (DXA) remains limited by accessibility and utilization. Opportunistic artificial intelligence (AI)-based analysis of routinely acquired chest radiographs (CXRs) may facilitate osteoporosis risk stratification. This study aimed to independently validate a regulatory-approved deep learning model (VeriOsteo™ OP) in a real-world, male-predominant cohort. <b>Methods:</b> We retrospectively analyzed 2336 patients who underwent both CXR and DXA at a medical center in 2023, including 1688 males (72.3%) and 648 females (27.7%). Osteoporosis was defined as a DXA T-score ≤ -2.5. AI performance was evaluated using sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and area under the receiver operating characteristic curve (AUC), with sex-specific subgroup analyses. <b>Results:</b> The AI model achieved an accuracy of 76.97%, sensitivity of 69.67%, specificity of 84.64%, PPV of 82.66%, and NPV of 72.65%. The overall AUC was 0.854 (95% CI, 0.839-0.868). The AUC was 0.825 (95% CI, 0.794-0.854) in males and 0.849 (95% CI, 0.831-0.866) in females. PPV was lower in males than in females (60.00% vs. 83.39%), while other performance measures were broadly comparable. The optimal AI cutoff also differed between males (≤-1.9) and females (≤-2.5). <b>Conclusions:</b> The AI model demonstrated good discrimination for opportunistic osteoporosis risk stratification using CXRs in a real-world cohort. Its moderate sensitivity and lower PPV among males highlight potential sex-related differences in AI-assisted osteoporosis assessment. The model may be most appropriately used as an adjunctive risk-stratification and DXA-prioritization tool to identify individuals who may benefit from subsequent DXA evaluation rather than a standalone diagnostic test.