Evidence map›Paper›PMID 40248069›Full record

ArticleFrontiers in rehabilitation sciences2025

Comparison of manual and semi-automated algorithm for measuring architectural features during different isometric knee extension intensities: a reliability and comparative study in novice raters.

Micheal J Luera, JoCarol E Shields, Emma Bozarth, Rob J MacLennan, Natalie P Walker, Jesus A Hernandez-Sarabia, Carlos A Estrada, Jason M DeFreitas, Scott K Crawford

Abstract read
In one paragraph

Article in Frontiers in rehabilitation sciences, 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

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

1 citing paper in PubMed.

  1. Article
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

9 authors.

Micheal J LueraDepartment of Neuroscience, Tarleton State University, Stephenville, TX, United States.
JoCarol E ShieldsNeural Health Research Laboratory, Syracuse University, Syracuse, NY, United States.
Emma BozarthDepartment of Health and Human Performance, Tarleton State University, Stephenville, TX, United States.
Rob J MacLennanMalcom Randall VA Medical Center, Gainesville, FL, United States.
Natalie P WalkerDepartment of Health and Human Performance, Tarleton State University, Stephenville, TX, United States.
Jesus A Hernandez-SarabiaDepartment of Neuroscience, Tarleton State University, Stephenville, TX, United States.
Carlos A EstradaDepartment of Exercise Science, Aurora University, Aurora, IL, United States.
Jason M DeFreitasNeural Health Research Laboratory, Syracuse University, Syracuse, NY, United States.
Scott K CrawfordDepartment of Kinesiology, University of Wisconsin-Madison, Madison, WI, United States.

Funding

University of Wisconsin Institute for Clinical and Translational ResearchUL1TR002373 · NCATS · UNIVERSITY OF WISCONSIN-MADISON · PI ELIZABETH S BURNSIDE, Allan R. Brasier · 2017 to 2026
$75.9M
Institutional Career Development CoreKL2TR002374 · NCATS · UNIVERSITY OF WISCONSIN-MADISON · PI Bo Liu, Marin Leigh Schweizer · 2017 to 2026
$9.7M
NCATS NIH HHS KL2 TR002374NCATS NIH HHS UL1 TR002373
6 · The paper itself

Abstract

Introduction: Ultrasound is a cost-effective and reliable method to determine skeletal muscle architecture. However, manual analysis of fascicle length (FL) and pennation angle (PA) can be arduous and subjective among raters, particularly among novice raters. Alternatives to manual processing have been proposed that expedite the evaluation of muscle architecture and afford more consistency. While using algorithms has provided dependable results of muscle architecture, it has often focused on variables of passive range of motion and submaximal contractions. To fully understand the impact of muscle architecture using semi-automated analysis, an investigation of a broad range of contraction intensities is needed. The purpose of this study was to develop and determine the intra-rater and inter-rater reliability of a custom, semi-automated algorithm to extract measures of muscle thickness, pennation angle, and fascicle length, and second to compare the semi-automated measures to measures extracted manually from the same novice raters while accounting for differences between contraction intensities. Methods: Fifteen resistance-trained individuals (male: Results: Intra-rater reliability for manual measures was poor for FL (ICCs: 0-0.30), poor to good for PA (ICCs: 0.46-0.77), and moderate to good for muscle thickness (MT) (ICCs: 0.55-0.84). For the semi-automated algorithm, the intra-rater reliability was good to excellent for FL (range: 0.90-0.99), PA (range: 0.88-0.99), and MT (range: 0.996-0.999) across all contraction intensities. Discussion: The findings of this study suggest that the reliability of manual measurement is lower when novice raters perform image analyses compared to the semi-automated method. Therefore, careful consideration and training should be provided when considering manual assessment of muscle architecture values, and standardized identification methods and features in algorithm development may be a better method for reproducibility.

Indexed as

fascicle lengthmusclepennation anglereliabilityultrasound

Identifiers

PMID40248069
PMCPMC12003410

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.