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Lin C, Tsai DJ, Wang CC, Chao YP, Huang JW, Lin CS, Fang WH. Osteoporotic Precise Screening Using Chest Radiography and Artificial Neural Network: The OPSCAN Randomized Controlled Trial. Radiology 2024; 311:e231937. [PMID: 38916510 DOI: 10.1148/radiol.231937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
Background Diagnosing osteoporosis is challenging due to its often asymptomatic presentation, which highlights the importance of providing screening for high-risk populations. Purpose To evaluate the effectiveness of dual-energy x-ray absorptiometry (DXA) screening in high-risk patients with osteoporosis identified by an artificial intelligence (AI) model using chest radiographs. Materials and Methods This randomized controlled trial conducted at an academic medical center included participants 40 years of age or older who had undergone chest radiography between January and December 2022 without a history of DXA examination. High-risk participants identified with the AI-enabled chest radiographs were randomly allocated to either a screening group, which was offered fully reimbursed DXA examinations between January and June 2023, or a control group, which received usual care, defined as DXA examination by a physician or patient on their own initiative without AI intervention. A logistic regression was used to test the difference in the primary outcome, new-onset osteoporosis, between the screening and control groups. Results Of the 40 658 enrolled participants, 4912 (12.1%) were identified by the AI model as high risk, with 2456 assigned to the screening group (mean age, 71.8 years ± 11.5 [SD]; 1909 female) and 2456 assigned to the control group (mean age, 72.1 years ± 11.8; 1872 female). A total of 315 of 2456 (12.8%) participants in the screening group underwent fully reimbursed DXA, and 237 of 315 (75.2%) were identified with new-onset osteoporosis. After including DXA results by means of usual care in both screening and control groups, the screening group exhibited higher rates of osteoporosis detection (272 of 2456 [11.1%] vs 27 of 2456 [1.1%]; odds ratio [OR], 11.2 [95% CI: 7.5, 16.7]; P < .001) compared with the control group. The ORs of osteoporosis diagnosis were increased in screening group participants who did not meet formalized criteria for DXA compared with those who did (OR, 23.2 [95% CI: 10.2, 53.1] vs OR, 8.0 [95% CI: 5.0, 12.6]; interactive P = .03). Conclusion Providing DXA screening to a high-risk group identified with AI-enabled chest radiographs can effectively diagnose more patients with osteoporosis. Clinical trial registration no. NCT05721157 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Smith and Rothenberg in this issue.
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Affiliation(s)
- Chin Lin
- From the Medical Technology Education Center, School of Medicine, National Defense Medical Center, Taipei, Taiwan, ROC (C.L.); Department of Artificial Intelligence (C.L., D.J.T., W.H.F.), Department of Family and Community Medicine (C.C.W., Y.P.C., J.W.H., W.H.F.), and Division of Cardiology, Department of Internal Medicine (C.S.L.), Tri-Service General Hospital, National Defense Medical Center, No. 325, Sec. 2, Chenggong Rd, Neihu District, Taipei TW 114, ROC; School of Public Health, National Defense Medical Center, Taipei, Taiwan, ROC (C.L., D.J.T.); and Department of Statistics and Information Science, Fu Jen Catholic University, Taipei, Taiwan, ROC (D.J.T.)
| | - Dung-Jang Tsai
- From the Medical Technology Education Center, School of Medicine, National Defense Medical Center, Taipei, Taiwan, ROC (C.L.); Department of Artificial Intelligence (C.L., D.J.T., W.H.F.), Department of Family and Community Medicine (C.C.W., Y.P.C., J.W.H., W.H.F.), and Division of Cardiology, Department of Internal Medicine (C.S.L.), Tri-Service General Hospital, National Defense Medical Center, No. 325, Sec. 2, Chenggong Rd, Neihu District, Taipei TW 114, ROC; School of Public Health, National Defense Medical Center, Taipei, Taiwan, ROC (C.L., D.J.T.); and Department of Statistics and Information Science, Fu Jen Catholic University, Taipei, Taiwan, ROC (D.J.T.)
| | - Chih-Chia Wang
- From the Medical Technology Education Center, School of Medicine, National Defense Medical Center, Taipei, Taiwan, ROC (C.L.); Department of Artificial Intelligence (C.L., D.J.T., W.H.F.), Department of Family and Community Medicine (C.C.W., Y.P.C., J.W.H., W.H.F.), and Division of Cardiology, Department of Internal Medicine (C.S.L.), Tri-Service General Hospital, National Defense Medical Center, No. 325, Sec. 2, Chenggong Rd, Neihu District, Taipei TW 114, ROC; School of Public Health, National Defense Medical Center, Taipei, Taiwan, ROC (C.L., D.J.T.); and Department of Statistics and Information Science, Fu Jen Catholic University, Taipei, Taiwan, ROC (D.J.T.)
| | - Yuan Ping Chao
- From the Medical Technology Education Center, School of Medicine, National Defense Medical Center, Taipei, Taiwan, ROC (C.L.); Department of Artificial Intelligence (C.L., D.J.T., W.H.F.), Department of Family and Community Medicine (C.C.W., Y.P.C., J.W.H., W.H.F.), and Division of Cardiology, Department of Internal Medicine (C.S.L.), Tri-Service General Hospital, National Defense Medical Center, No. 325, Sec. 2, Chenggong Rd, Neihu District, Taipei TW 114, ROC; School of Public Health, National Defense Medical Center, Taipei, Taiwan, ROC (C.L., D.J.T.); and Department of Statistics and Information Science, Fu Jen Catholic University, Taipei, Taiwan, ROC (D.J.T.)
| | - Jun-Wei Huang
- From the Medical Technology Education Center, School of Medicine, National Defense Medical Center, Taipei, Taiwan, ROC (C.L.); Department of Artificial Intelligence (C.L., D.J.T., W.H.F.), Department of Family and Community Medicine (C.C.W., Y.P.C., J.W.H., W.H.F.), and Division of Cardiology, Department of Internal Medicine (C.S.L.), Tri-Service General Hospital, National Defense Medical Center, No. 325, Sec. 2, Chenggong Rd, Neihu District, Taipei TW 114, ROC; School of Public Health, National Defense Medical Center, Taipei, Taiwan, ROC (C.L., D.J.T.); and Department of Statistics and Information Science, Fu Jen Catholic University, Taipei, Taiwan, ROC (D.J.T.)
| | - Chin-Sheng Lin
- From the Medical Technology Education Center, School of Medicine, National Defense Medical Center, Taipei, Taiwan, ROC (C.L.); Department of Artificial Intelligence (C.L., D.J.T., W.H.F.), Department of Family and Community Medicine (C.C.W., Y.P.C., J.W.H., W.H.F.), and Division of Cardiology, Department of Internal Medicine (C.S.L.), Tri-Service General Hospital, National Defense Medical Center, No. 325, Sec. 2, Chenggong Rd, Neihu District, Taipei TW 114, ROC; School of Public Health, National Defense Medical Center, Taipei, Taiwan, ROC (C.L., D.J.T.); and Department of Statistics and Information Science, Fu Jen Catholic University, Taipei, Taiwan, ROC (D.J.T.)
| | - Wen-Hui Fang
- From the Medical Technology Education Center, School of Medicine, National Defense Medical Center, Taipei, Taiwan, ROC (C.L.); Department of Artificial Intelligence (C.L., D.J.T., W.H.F.), Department of Family and Community Medicine (C.C.W., Y.P.C., J.W.H., W.H.F.), and Division of Cardiology, Department of Internal Medicine (C.S.L.), Tri-Service General Hospital, National Defense Medical Center, No. 325, Sec. 2, Chenggong Rd, Neihu District, Taipei TW 114, ROC; School of Public Health, National Defense Medical Center, Taipei, Taiwan, ROC (C.L., D.J.T.); and Department of Statistics and Information Science, Fu Jen Catholic University, Taipei, Taiwan, ROC (D.J.T.)
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