Journal of thoracic disease · 2026
Diagnostic accuracy of an artificial intelligence-based osteoporosis screening system on portable chest radiographs.
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Kim Jee Hyun, Ahn So Hyun, Lee Rena, Cho Sungho, Choi Soeun, Kim Kwan-Chang
Аннотация
BACKGROUND: Artificial intelligence (AI) models applied to conventional chest radiographs (CXRs) have shown potential for osteoporosis screening in hospital settings. However, their performance when applied to portable CXR obtained in community programs remains uncertain. This study evaluated a commercially available AI model (PROS® CXR: OSTEO) for osteoporosis screening using portable CXR.
METHODS: Older adults participating in a Korean National Tuberculosis Association mobile screening program who consented to osteoporosis evaluation were prospectively enrolled. Portable CXR images were analyzed by the AI model to generate a continuous osteoporosis risk score (0-1). Participants subsequently underwent dual-energy X-ray absorptiometry (DXA) within approximately one week. Osteoporosis was defined as a T-score ≤-2.5 at the lumbar spine, femoral neck, or total hip. Diagnostic performance was assessed across thresholds from 0.0 to 1.0 to determine an optimal threshold.
RESULTS: Fifty-two participants with paired portable CXR and DXA data were analyzed. Osteoporosis was present in 20 participants (38.5%). A threshold of 0.2 provided the most favorable screening performance in this cohort, yielding an accuracy of 0.69, sensitivity of 0.90, specificity of 0.57, positive predictive value of 0.56, negative predictive value of 0.90, and an F1-score of 0.69. The area under the curve was 0.86 (95% confidence interval: 0.73-0.97). Most false-positive cases occurred in osteopenic individuals, whereas false negatives were limited to borderline osteoporosis cases.
CONCLUSIONS: In this feasibility-based pilot study, the AI model demonstrated promising discrimination when applied to portable CXR in community-based osteoporosis screening. Its high sensitivity at the selected threshold suggests potential utility as a referral-oriented screening tool, but the findings require confirmation in larger, more balanced, externally validated cohorts.
METHODS: Older adults participating in a Korean National Tuberculosis Association mobile screening program who consented to osteoporosis evaluation were prospectively enrolled. Portable CXR images were analyzed by the AI model to generate a continuous osteoporosis risk score (0-1). Participants subsequently underwent dual-energy X-ray absorptiometry (DXA) within approximately one week. Osteoporosis was defined as a T-score ≤-2.5 at the lumbar spine, femoral neck, or total hip. Diagnostic performance was assessed across thresholds from 0.0 to 1.0 to determine an optimal threshold.
RESULTS: Fifty-two participants with paired portable CXR and DXA data were analyzed. Osteoporosis was present in 20 participants (38.5%). A threshold of 0.2 provided the most favorable screening performance in this cohort, yielding an accuracy of 0.69, sensitivity of 0.90, specificity of 0.57, positive predictive value of 0.56, negative predictive value of 0.90, and an F1-score of 0.69. The area under the curve was 0.86 (95% confidence interval: 0.73-0.97). Most false-positive cases occurred in osteopenic individuals, whereas false negatives were limited to borderline osteoporosis cases.
CONCLUSIONS: In this feasibility-based pilot study, the AI model demonstrated promising discrimination when applied to portable CXR in community-based osteoporosis screening. Its high sensitivity at the selected threshold suggests potential utility as a referral-oriented screening tool, but the findings require confirmation in larger, more balanced, externally validated cohorts.