Evidence map›Paper›PMID 42801184›Full record

ArticlePeerJ2026

MuscleExpert: a semi-automated tool delivering ImageJ-comparable measurements of muscle thickness, area, and quality.

Zongpan Li, Li-Qun Zhang, Marcel Bahia Lanza

Abstract read
In one paragraph

Article in PeerJ, 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
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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

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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

5 · Who and what money

Authors and funding

3 authors.

Zongpan LiPhysical Therapy and Rehabilitation Sciences, University of Maryland Baltimore, Baltimore, MD, United States of America.
Li-Qun ZhangPhysical Therapy and Rehabilitation Sciences, University of Maryland Baltimore, Baltimore, MD, United States of America.
Marcel Bahia LanzaPhysical Therapy and Rehabilitation Sciences, University of Maryland Baltimore, Baltimore, MD, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Most image-analysis software programs were not designed specifically for ultrasound-based measurements, resulting in irrelevant general-purpose features that force users to navigate through multiple menus and making the analysis time-consuming and increasing the likelihood of error. The aim of this study was to present and validate the semi-automated MuscleExpert software against the widely used ImageJ by comparing muscle thickness (MT), cross-sectional area (CSA), and muscle quality (MQ-measured as echo intensity). Methods: Ten volunteers (five males; age: 44.9 ± 23.5 years) participated in this cross-sectional study. Ultrasound images of the tibialis anterior and medial gastrocnemius muscles were acquired using 2D B-mode ultrasonography and analyzed in both software programs. Equivalence of means was evaluated Results: The TOST for MT, CSA, and MQ were within their respective equivalence margins. The ICC was excellent for all measures (MT: ICC = 0.999; CSA: ICC = 0.999; MQ: ICC = 1.000). Precision was similarly high, with CVs of 0.49% (MT), 0.54% (CSA), and 0.33% (MQ), demonstrating minimal variability between the two methods. SEM values were low across all outcomes, corresponding to 0.49% (MT), 0.53% (CSA), and 0.23% (MQ). Finally, the Bland-Altman analysis demonstrated minimal systematic differences and no proportional bias between ImageJ and MuscleExpert for all metrics. The present findings indicate that MuscleExpert and ImageJ produce nearly identical results for MT, CSA, and MQ. These findings support the integration of MuscleExpert into clinical and research workflows, offering a more efficient solution for muscle assessment without compromising measurement integrity.

Indexed as

Image Processing, Computer-AssistedMuscle, SkeletalSoftwareAdultCross-Sectional StudiesFemaleHumansMaleMiddle AgedReproducibility of ResultsUltrasonographyCross-sectional areaMuscleExpert softwareMuscle qualityMuscle sizeMuscle thicknessSoftwareUltrasound imaging

Identifiers

PMID42801184
PMCPMC13615733

What OpenQuestion holds

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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.