Evidence map›Paper›PMID 40395884›Full record

ArticleArchives of medical science : AMS2025

Analysis of the current status of computed tomography diagnosis of sarcopenia.

Qingxin He, Wenfang Xia

Abstract read
In one paragraph

Article in Archives of medical science : AMS, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

10 citing papers in PubMed.

  1. Article
  2. CT-defined sarcopenia as a predictor of early mortality following hip fracture: a retrospective cohort study.European journal of orthopaedic surgery & traumatology : orthopedie traumatologie · 2026
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  4. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Qingxin HeDepartment of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Wenfang XiaDepartment of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Sarcopenia is a clinical syndrome characterized by the reduction of skeletal muscle mass and strength, leading to adverse events such as falls, fractures, frailty, disability, and increased mortality. Compared to previous diagnostic techniques such as dual-energy X-ray absorptiometry (DXA), bioelectrical impedance analysis (BIA), and body composition analysis, computed tomography (CT) offers significant advantages. Opportunistic CT imaging, enhanced by artificial intelligence (AI) software, provides a superior diagnostic tool for sarcopenia. AI software can automatically segment muscle groups on opportunistic CT images from different populations, enabling the efficient calculation of body composition parameters and more accurate and rapid diagnosis of sarcopenia. Early intervention may significantly reduce adverse clinical outcomes associated with sarcopenia. This study aims to evaluate the advantages of using CT images compared to traditional diagnostic techniques and to assess the value of skeletal muscle parameters at different spinal levels on opportunistic CT images for diagnosing sarcopenia.

Indexed as

artificial intelligencecomputed tomographysarcopeniaskeletal muscle index

Identifiers

PMID40395884
PMCPMC12087324

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Registered trials

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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.