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A deep learning model was developed to identify osteoporosis from chest X-ray (CXR) features with high accuracy in internal and external validation. It has significant prognostic implications, identifying individuals at higher risk of all-cause mortality. This Artificial Intelligence (AI)-enabled CXR strategy may function as an early detection screening tool for osteoporosis. The aim of this study was to develop a deep learning model (DLM) to identify osteoporosis via CXR features and investigate the performance and clinical implications. This study collected 48,353 CXRs with the corresponding T score according to Dual energy X-ray Absorptiometry (DXA) from the academic medical center. Among these, 35,633 CXRs were used to identify CXR- Osteoporosis (CXR-OP). Another 12,720 CXRs were used to validate the performance, which was evaluated by the area under the receiver operating characteristic curve (AUC). Furthermore, CXR-OP was tested to assess the long-term risks of mortality, which were evaluated by Kaplan‒Meier survival analysis and the Cox proportional hazards model. The DLM utilizing CXR achieved AUCs of 0.930 and 0.892 during internal and external validation, respectively. The group that underwent DXA with CXR-OP had a higher risk of all-cause mortality (hazard ratio [HR] 2.59, 95% CI: 1.83-3.67), and those classified as CXR-OP in the group without DXA also had higher all-cause mortality (HR: 1.67, 95% CI: 1.61-1.72) in the internal validation set. The external validation set produced similar results. Our DLM uses CXRs for early detection of osteoporosis, aiding physicians to identify those at risk. It has significant prognostic implications, improving life quality and reducing mortality. AI-enabled CXR strategy may serve as a screening tool.

Titel:	Artificial Intelligence-enabled Chest X-ray Classifies Osteoporosis and Identifies Mortality Risk.
Autor/in / Beteiligte Person:	Tsai, DJ ; Lin, C ; Lin, CS ; Lee, CC ; Wang, CH ; Fang, WH
Link:	Full Text Finder (Volltext)
Zeitschrift:	Journal of medical systems, Jg. 48 (2024-01-13), Heft 1, S. 12
Veröffentlichung:	1999- : New York, NY : Kluwer Academic/Plenum Publishers ; <i>Original Publication</i>: New York, Plenum Press., 2024
Medientyp:	academicJournal
ISSN:	1573-689X (electronic)
DOI:	10.1007/s10916-023-02030-2
Schlagwort:	Humans Artificial Intelligence X-Rays Absorptiometry, Photon methods Osteoporosis diagnostic imaging Deep Learning
Sonstiges:	Nachgewiesen in: MEDLINE Sprachen: English Publication Type: Journal Article Language: English [J Med Syst] 2024 Jan 13; Vol. 48 (1), pp. 12. <i>Date of Electronic Publication: </i>2024 Jan 13. MeSH Terms: Osteoporosis* / diagnostic imaging ; Deep Learning* ; Humans ; Artificial Intelligence ; X-Rays ; Absorptiometry, Photon / methods References: Gharib, H., et al., Associazione Medici Endocrinologi, and European Thyroid Association medical guidelines for clinical practice for the diagnosis and management of thyroid nodules. Endocr Pract, 2010. 16(Suppl 1): p. 1-43. (PMID: 2049793810.4158/10024.GL) ; Organization, W.H. WHO scientific group on the assessment of osteoporosis at primary health care level. in Summary meeting report. 2004. ; Curry, S.J., et al., Screening for osteoporosis to prevent fractures: US Preventive Services Task Force recommendation statement. Jama, 2018. 319(24): p. 2521-2531. 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Grant Information: NSTC 112-2222-E-030 -002 -MY2 National Science and Technology Council; NSTC 112-2321-B-016-003 National Science and Technology Council; MOST110-2314-B-016-010-MY3 Ministry of Science and Technology, Taiwan Contributed Indexing: Keywords: Artificial intelligence; Bone mass density; Chest X-ray; Deep learning; T score Entry Date(s): Date Created: 20240113 Date Completed: 20240115 Latest Revision: 20240115 Update Code: 20240115

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Artificial Intelligence-enabled Chest X-ray Classifies Osteoporosis and Identifies Mortality Risk.