Evidence map›Paper›PMID 42252263›Full record

ArticleAsian spine journal2026

Differential association of psoas and paraspinal muscle volumes with appendicular lean mass index: AI-derived lumbar computed tomography three-dimensional volumetric indices in older Korean Women: an observational study.

Eun Chae Lee, Jang Hyuk Cho, Dong Gyu Lee

Abstract read
In one paragraph

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

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Eun Chae LeeDepartment of Physical Medicine and Rehabilitation, Yeungnam University College of Medicine, Daegu, Korea.
Jang Hyuk ChoDepartment of Rehabilitation Medicine, Keimyung University Dongsan Hospital, Keimyung University School of Medicine, Daegu, Korea.
Dong Gyu LeeDepartment of Physical Medicine and Rehabilitation, Yeungnam University College of Medicine, Daegu, Korea. painfree@yu.ac.kr.

Funding

Yeungnam University
6 · The paper itself

Abstract

STUDY

designRetrospective cross-sectional study. PURPOSE: To investigate the relationship between sarcopenia-related markers and artificial intelligence (AI)-derived three-dimensional (3D) volumetric indices of lumbar muscles. OVERVIEW OF LITERATURE: Sarcopenia is characterized by loss of skeletal muscle mass and physical function. Although appendicular skeletal muscle mass (ASM) is widely used for sarcopenia assessment, reliable evaluation of lumbar paraspinal muscles remains limited. AIbased 3D volumetric analysis may provide a reproducible approach for muscle assessment.

methodsThis single-center retrospective cross-sectional study included 58 women aged ≥65 years who underwent lumbar computed tomography and sarcopenia assessment. AI-derived 3D volumetric analysis was performed using TotalSegmentator implemented in 3D Slicer. Volumetric indices were calculated as muscle volume (cm3) divided by height3 (m3). Associations between volumetric indices and ASM measured by dual-energy X-ray absorptiometry were evaluated using Pearson correlation and multivariable linear regression analyses.

resultsAmong the 58 participants, 25 were classified as having sarcopenia. Appendicular skeletal muscle mass index (ASMI) was not significantly associated with paraspinal muscle volume indices. In contrast, the iliopsoas muscle volume index showed a significant positive correlation with ASMI (r=0.424, p<0.001). No significant differences in AI-derived 3D lumbar muscle volumetric indices were observed between sarcopenic and non-sarcopenic groups.

conclusionsParaspinal muscle volume indices were not significantly associated with ASMI, whereas iliopsoas muscle volume indices showed a significant association. These findings suggest that the iliopsoas muscle may better reflect systemic skeletal muscle mass than paraspinal muscles in older women.

Indexed as

Artificial intelligenceMuscle atrophyParaspinal musclesPsoas indexSarcopenia

Identifiers

PMID42252263
PMCPMC13547662

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.