Evidence map›Paper›PMID 42908721›Full record

ArticleFrontiers in medicine2026

AI-driven CT assessment of erector spinae myosteatosis: muscle quality and its association with strength and frailty in older surgical patients.

Moritz F Mayr, Thierno D Diallo, Christoph Maurer, Moritz M M Albrecht, Tobias Scheef, Alexander Rau, Marco Reisert, Fabian Bamberg, Hagen Schmal

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Article in Frontiers in medicine, 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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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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3 · Its place in the literature

Who cites it

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No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Moritz F Mayr *Department of Orthopedics and Trauma Surgery, University Medical Center Freiburg, Freiburg, Germany.
Thierno D Diallo *Department of Diagnostic and Interventional Radiology, Faculty of Medicine, Medical Center - University of Freiburg, University of Freiburg, Freiburg, Germany.
Christoph MaurerDepartment of Neurology and Neuroscience, Faculty of Medicine, Medical Center, University of Freiburg, Freiburg, Germany.
Moritz M M AlbrechtDepartment of Orthopedics and Trauma Surgery, University Medical Center Freiburg, Freiburg, Germany.
Tobias ScheefDepartment of Diagnostic and Interventional Radiology, Faculty of Medicine, Medical Center - University of Freiburg, University of Freiburg, Freiburg, Germany.
Alexander RauDepartment of Diagnostic and Interventional Radiology, Faculty of Medicine, Medical Center - University of Freiburg, University of Freiburg, Freiburg, Germany.
Marco ReisertDivision of Medical Physics, Department of Diagnostic and Interventional Radiology, Faculty of Medicine, University Medical Center Freiburg, University of Freiburg, Freiburg, Germany.
Fabian BambergDepartment of Diagnostic and Interventional Radiology, Faculty of Medicine, Medical Center - University of Freiburg, University of Freiburg, Freiburg, Germany.
Hagen SchmalDepartment of Orthopedics and Trauma Surgery, University Medical Center Freiburg, Freiburg, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Frailty and sarcopenia are overlapping syndromes that increase adverse outcomes in older adults. Computed tomography (CT) is a reference standard for body composition analysis, but manual segmentation is time-consuming. This exploratory study used a fully automated deep learning model (DLM) to investigate associations between CT-derived muscle quality and quantity and clinical frailty. Methods: We performed a retrospective, cross-sectional secondary analysis of 53 geriatric surgical patients (mean age 77.4 ± 4.4 years). A DLM segmented skeletal muscle (SM), subcutaneous and visceral adipose tissue at the third lumbar vertebral level. Intermuscular adipose tissue (IMAT) was quantified exclusively within the erector spinae muscle and is therefore reported as a surrogate marker of trunk muscle quality rather than whole-muscle myosteatosis. Frailty was assessed with the Clinical Frailty Scale (CFS), handgrip strength and the Timed Up and Go (TUG) test. Because functional testing was incomplete, effective sample sizes varied ( Results: In the total cohort, a higher IMAT Index was associated with lower handgrip strength ( Conclusions: In this small, retrospective, cross-sectional cohort, an erector-spinae-based IMAT Index was associated with handgrip strength but not independently with global clinical frailty. Automated opportunistic CT analysis may therefore identify specific muscle weakness rather than global frailty. The observed sex differences are hypothesis-generating and require confirmation in larger, adequately powered longitudinal studies.

Indexed as

body compositiondeep learningfrailtymuscle qualitymyosteatosissarcopenia

Identifiers

PMID42908721
PMCPMC13683118

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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.