Evidence map›Paper›PMID 40166944›Full record

ArticleEuropean journal of translational myology2025

Sonographic image texture features in muscle tissue-mimicking material reduce variability introduced by probe angle and gain settings compared to traditional echogenicity.

Dustin J Oranchuk, Katie L Boncella, Daniella Gonzalez-Rivera, Michael O Harris-Love

Abstract read
In one paragraph

Article in European journal of translational myology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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

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.

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

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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

4 authors.

Dustin J OranchukMuscle Morphology, Mechanics, and Performance Laboratory, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States; Department of Physical Medicine and Rehabilitation, University of Colorado, Anschutz Medical Campus, Aurora, Colorado. dustin.oranchuk@cuanschutz.edu.
Katie L BoncellaMuscle Morphology, Mechanics, and Performance Laboratory, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States; Department of Bioengineering, University of Colorado Denver, Anschutz Medical Campus, Aurora, Colorado. katie.boncella@cuanschutz.edu.
Daniella Gonzalez-RiveraMuscle Morphology, Mechanics, and Performance Laboratory, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States; University of Colorado Physical Therapy Program, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado. daniella.Gonzalez-Rivera@cuanschutz.edu.
Michael O Harris-LoveMuscle Morphology, Mechanics, and Performance Laboratory, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States; Department of Physical Medicine and Rehabilitation, University of Colorado, Anschutz Medical Campus, Aurora, Colorado, United States; University of Colorado Physical Therapy Program, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States; Eastern Colorado VA Geriatric Research, Education, and Clinical Center, Aurora, Colorado. michael.harris-love@cuanschutz.edu.

Funding

Maternal Morbidity and Mortality: Risk Factors, Early Detection and Personalized InterventionUL1TR001409 · NCATS · GEORGETOWN UNIVERSITY · PI GONDRE-LEWIS, MARJORIE C, MELLMAN, THOMAS A · 2015 to 2024
$37.8M
Georgetown-Howard Universities Center for Clinical and Translational Science (GHUUL1TR000101 · NCATS · GEORGETOWN UNIVERSITY · PI MELLMAN, THOMAS A, VERBALIS, JOSEPH G · 2012 to 2015
$19.3M
NCATS NIH HHS UL1 TR000101NCATS NIH HHS UL1 TR001409
6 · The paper itself

Abstract

Cost-effective and portable ultrasonography offers a promising approach for monitoring skeletal muscle damage and quality in many contexts. However, echogenicity analysis relies on precise transducer orientations and machine parameters, posing challenges for data pooling across different raters and settings. Muscle texture analysis offers a potential means of reducing inter-rater and machine-setting variability. Scans were assessed at nine angles, controlled using a custom transducer shell and software. Scans were performed three times, and different gains were applied. All scans were performed on a muscle tissue-mimicking phantom to eliminate biological variability. Intra-angle and intra-gain variability and internal consistency were assessed via coefficient of variation (CV%) and Cronbach's alpha (αc). Spearman's (ρ) correlations were employed to determine the relationship between echogenicity and each texture feature. Entropy (angle: CV=2.7-7.6%; gain: CV=10.5%; αc=0.86), and inverse difference moment (angle: CV=3.7-9.8%; gain: CV=16.5%; αc=0.87) were less variable than echogenicity (angle: CV=6.4-19.4%; gain: CV=39.0%; αc=0.82). Angular second moment (angle: CV=17.9-116.6%; gain: CV=71.6%; αc=0.68), contrast (angle: CV=7.8-14.7%; gain: CV=41.8%;αc=0.75), and correlation (angle: CV=9.0-13.5%; gain: CV=28.6%; αc=0.49) features were generally more variable. Entropy (ρ=0.82-0.98, p≤0.011) and inverse difference moment (ρ=-0.98--0.83, p≤0.008), were more strongly correlated with echogenicity than angular second moment (ρ=-0.98--0.77, p≤0.016), contrast (ρ=0.53-0.98, p≤0.15), and correlation (ρ=-0.25--0.19, p=0.520-0.631). Entropy and inverse difference moment features may allow data sharing between laboratory and clinical settings with ultrasound machine parameters and raters of varying skill levels. Clinical and mechanistic studies are required to determine if texture features can replace echogenicity assessments.

Identifiers

PMID40166944
PMCPMC12265416

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LicenceCC BY-NC
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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.