Diagnostic accuracy of an artificial intelligence-based osteoporosis screening system on portable chest radiographs

Jee Hyun Kim1,So Hyun Ahn2,3,4*,Rena Lee4,5,Sungho Cho5,Soeun Choi6,Kwan-Chang Kim1*

1Department of Thoracic and Cardiovascular Surgery, College of Medicine, Ewha Womans University, Seoul, South Korea; 2Ewha Medical Research Institute, College of Medicine, Ewha Womans University, Seoul, South Korea; 3Ewha Medical Artificial Intelligence Research Institute, Ewha Womans University, College of Medicine, Seoul, South Korea; 4Department of Biomedical Engineering, Ewha Womans University, College of Medicine, Seoul, South Korea; 5REMEDI Research and Development Center, Seoul, South Korea; 6Department of Computational Medicine, Ewha Womans University, College of Medicine, Seoul, South Korea

Contributions: (I) Conception and design: S Cho, JH Kim, R Lee; (II) Administrative support: SH Ahn, KC Kim, R Lee; (III) Provision of study materials or patients: Korean National Tuberculosis Association; (IV) Collection and assembly of data: All authors; (V) Data analysis and interpretation: JH Kim; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors.

*These authors contributed equally to this work.

Correspondence to: So Hyun Ahn, PhD. Ewha Medical Research Institute, College of Medicine, Ewha Womans University, 25 Magokdong-ro 2-gil, Gangseo-gu, Seoul 07804, South Korea; Ewha Medical Artificial Intelligence Research Institute, Ewha Womans University, College of Medicine, Seoul, South Korea; Department of Biomedical Engineering, Ewha Womans University, College of Medicine, Seoul, South Korea. Email: mpsohyun@ewha.ac.kr; Kwan-Chang Kim, MD, PhD. Department of Thoracic and Cardiovascular Surgery, College of Medicine, Ewha Womans University, 25 Magokdong-ro 2-gil, Gangseo-gu, Seoul 07804, South Korea. Email: mdkkchang@ewha.ac.kr.

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.

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