Evidence map›Paper›PMID 42857211›Full record

ArticleOsteoporosis and sarcopenia2026

Can artificial intelligence-derived bone mineral density outperform traditional morphometric measurements in opportunistic osteoporosis screening using pelvic radiographs? A multicenter comparative diagnostic study.

Chia-Che Lee, Xi-Qian Heng, Hung-Kuan Yen, I-Hsin Chen, Tzu-Hao Tseng, Hong-Da Lu, Hung-Chun Lee, Ruey-Feng Chang, Chen-Yu Wang, Shau-Huai Fu

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Article in Osteoporosis and sarcopenia, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

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10 authors.

Chia-Che LeeGraduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan.
Xi-Qian HengDepartment of Medical Education, National Taiwan University Hospital, Taipei, Taiwan.
Hung-Kuan YenDepartment of Orthopaedic Surgery, National Taiwan University Hospital, Taipei, Taiwan.
I-Hsin ChenDepartment of Orthopaedic Surgery, Shin Kong WHS Memorial Hospital, Taipei, Taiwan.
Tzu-Hao TsengDepartment of Orthopaedic Surgery, National Taiwan University Hospital, Taipei, Taiwan.
Hong-Da LuSchool of Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan.
Hung-Chun LeeSchool of Medicine, College of Medicine, Fu Jen Catholic University, Taipei, Taiwan.
Ruey-Feng ChangGraduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan.
Chen-Yu WangNational Center for Geriatrics and Welfare Research, National Health Research Institutes, Huwei Township, Yunlin County, Taiwan.
Shau-Huai FuDepartment of Orthopaedic Surgery, National Taiwan University Hospital, Taipei, Taiwan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: To investigate whether an artificial intelligence (AI) model could demonstrate high diagnostic accuracy in identifying dual-energy X-ray absorptiometry (DXA) confirmed osteoporosis, outperform traditional human-read metrics, and deliver substantial clinical net benefit. Methods: This retrospective multicenter cohort study included 382 patients with hip fractures who had both preoperative pelvic radiographs and postoperative DXA scans. An AI model analyzed the intact contralateral proximal femur to predict both bone mineral density (BMD) values and T-scores. Diagnostic performance was evaluated against DXA-confirmed osteoporosis (T-score ≤-2.5) and compared with manual cortical thickness index (CTI) and femoral neck width (FNW) measurements using Spearman rank-order correlation analysis, receiver operating characteristic (ROC) curves, the DeLong test, and decision curve analysis (DCA). Results: The AI model showed a strong correlation with DXA-derived BMD values for both the femoral neck ( Conclusions: Deep learning AI offers a superior, automated alternative to traditional manual morphometric measurements for identifying osteoporosis on routine pelvic radiographs in patients with hip fractures, potentially facilitating timely secondary fracture prevention.

Indexed as

Artificial intelligenceBone mineral densityCortical thickness indexFemoral neck widthOpportunistic screeningOsteoporosis

Identifiers

PMID42857211
PMCPMC13650165

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.