Evidence map›Paper›PMID 41558598›Full record

ArticleJournal of advanced research2026

Construction of musculoskeletal quantitative model based on deep learning and study of musculoskeletal relationship in patients with osteoporosis.

Zhenzhen Guan, Yijin Wang, Shiyu Zhang, Yanyang Zhang, Liang Wang, Yixin Chen, Xuhua Lu

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PubMed Full text at PMC Publisher
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Article in Journal of advanced research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in
1citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Zhenzhen GuanDepartment of Orthopedics, The Second Affiliated Hospital of Naval Medical University of Chinese People's Liberation Army, Shanghai 200003, China; Department of Radiology, 905 Hospital of People's Liberation Army Navy, Shanghai 200052, China.
Yijin WangDepartment of Orthopedics, The Second Affiliated Hospital of Naval Medical University of Chinese People's Liberation Army, Shanghai 200003, China.
Shiyu ZhangUCSI University, Kuala Lumpur 56000, Malaysia.
Yanyang ZhangDepartment of Orthopedics, The Second Affiliated Hospital of Naval Medical University of Chinese People's Liberation Army, Shanghai 200003, China.
Liang WangDepartment of Orthopedics, The Second Affiliated Hospital of Naval Medical University of Chinese People's Liberation Army, Shanghai 200003, China.
Yixin ChenFirst People's Hospital of Lanzhou City, Lanzhou 730050, China.
Xuhua LuDepartment of Orthopedics, The Second Affiliated Hospital of Naval Medical University of Chinese People's Liberation Army, Shanghai 200003, China. Electronic address: xuhualu_hotmail@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionLumbar CT and MRI scans are helpful for osteoporosis (OP) screening. Deep learning enhances the efficiency and accuracy of musculoskeletal quantification for OP screening.

objectivesTo develop a deep learning-based model (UNet3D) for musculoskeletal quantification from lumbar CT and MRI in OP patients, and to explore musculoskeletal relationship. MATERIALS: Retrospectively analyzing OP patients' lumbar CT, MRI and DXA scans between January 2018 and August 2025 from two center, and UNet3D was developed based on U-Net to simultaneously segment L1-S1 vertebrae on CT and paraspinal muscles/intermuscular spaces on MRI. Performance was compared against U-Net, U

resultsThe study analyzed 410 OP patients (65 ± 10 years; 240 women) with 76,700 axial CT and 6210 axial T2 slices, including training (266), validation (76), internal test (38) and external test (30) sets. On the internal test set, UNet3D's accuracy in CT (PA/mPA/mIoU, 0.984/0.849/0.760) and MRI (PA/mPA/mIoU, 0.973/0.849/0.724), outperformed U-Net/U

conclusionUNet3D outperformed U-Net/U

Indexed as

Deep LearningOsteoporosisAgedFemaleHumansLumbar VertebraeMagnetic Resonance ImagingMaleMiddle AgedRetrospective StudiesTomography, X-Ray ComputedComputed tomographyDeep learningMagnetic resonance imagingMusculoskeletal quantificationOsteoporosis

Identifiers

PMID41558598
PMCPMC13669575

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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.