Evidence map›Paper›PMID 39343707›Full record

ArticleJournal of cachexia, sarcopenia and muscle2024

Muscle Fat and Volume Differences in People With Hip-Related Pain Compared With Controls: A Machine Learning Approach.

Chris Stewart, Evert O Wesselink, Zuzana Perraton, Kenneth A Weber, Matthew G King, Joanne L Kemp, Benjamin F Mentiplay, Kay M Crossley, James M Elliott, Joshua J Heerey and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of cachexia, sarcopenia and muscle, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. 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

14 authors.

Chris StewartSchool of Allied Health, Human Services and Sport, Discipline of Physiotherapy, La Trobe University, Melbourne, Australia.
Evert O WesselinkFaculty of Behavioural and Movement Sciences, Amsterdam Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
Zuzana PerratonSchool of Allied Health, Human Services and Sport, Discipline of Physiotherapy, La Trobe University, Melbourne, Australia.
Kenneth A WeberDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Palo Alto, California, USA.
Matthew G KingSchool of Allied Health, Human Services and Sport, Discipline of Physiotherapy, La Trobe University, Melbourne, Australia.
Joanne L KempLa Trobe Sport and Exercise Medicine Research Centre, School of Allied Health, Human Services and Sport, La Trobe University, Bundoora, Australia.
Benjamin F MentiplayLa Trobe Sport and Exercise Medicine Research Centre, School of Allied Health, Human Services and Sport, La Trobe University, Bundoora, Australia.
Kay M CrossleyLa Trobe Sport and Exercise Medicine Research Centre, School of Allied Health, Human Services and Sport, La Trobe University, Bundoora, Australia.
James M ElliottFaculty of Medicine and Health, Northern Sydney Local Health District and the University of Sydney, The Kolling Institute St Leonards, Sydney, Australia.
Joshua J HeereyLa Trobe Sport and Exercise Medicine Research Centre, School of Allied Health, Human Services and Sport, La Trobe University, Bundoora, Australia.
Mark J ScholesLa Trobe Sport and Exercise Medicine Research Centre, School of Allied Health, Human Services and Sport, La Trobe University, Bundoora, Australia.
Peter R LawrensonLa Trobe Sport and Exercise Medicine Research Centre, School of Allied Health, Human Services and Sport, La Trobe University, Bundoora, Australia.
Chris CalabreseLa Trobe Sport and Exercise Medicine Research Centre, School of Allied Health, Human Services and Sport, La Trobe University, Bundoora, Australia.
Adam I SemciwSchool of Allied Health, Human Services and Sport, Discipline of Physiotherapy, La Trobe University, Melbourne, Australia.

Funding

MRI-Derived Neuromuscular Signatures to Predict Surgical Response in Degenerative Cervical MyelopathyR01NS128478 · NINDS · STANFORD UNIVERSITY · PI Kenneth Arnold Weber · 2023 to 2026
$2.2M
Neuroimaging-Based Brain and Spinal Cord Biomarkers for Cervical RadiculopathyK23NS104211 · NINDS · STANFORD UNIVERSITY · PI WEBER, KENNETH ARNOLD · 2018 to 2022
$875k
National Health and Medical Research Council of Australia (NHMRC) GNT:1088683NIH HHS K23NS104211NIH HHS L30NS108301NIH HHS R01NS128478NINDS NIH HHS K23 NS104211NINDS NIH HHS L30 NS108301NINDS NIH HHS R01 NS128478
6 · The paper itself

Abstract

backgroundHip-related pain (HRP) affects young to middle-aged active adults and impacts physical activity, finances and quality of life. HRP includes conditions like femoroacetabular impingement syndrome and labral tears. Lateral hip muscle dysfunction and atrophy in HRP are more pronounced in advanced hip pathology, with limited evidence in younger populations. While MRI use for assessing hip muscle morphology is increasing, with automated deep-learning techniques showing promise, studies assessing their accuracy are limited. Therefore, we aimed to compare hip intramuscular fat infiltrate (MFI) and muscle volume, in individuals with and without HRP as well as assess the reliability and accuracy of automated machine-learning segmentations compared with human-generated segmentation.

methodsThis cross-sectional study included sub-elite/amateur football players (Australian football and soccer) with a greater than 6-month history of HRP [n = 180, average age 28.32, (standard deviation 5.88) years, 19% female] and a control group of sub-elite/amateur football players without pain [n = 48, 28.89 (6.22) years, 29% female]. Muscle volume and MFI of gluteus maximus, medius, minimis and tensor fascia latae were assessed using MRI. Associations between muscle volume and group were explored using linear regression models, controlling for body mass index, age, sport and sex. A convolutional neural network (CNN) machine-learning approach was compared with human-performed muscle segmentations in a subset of participants (n = 52) using intraclass correlation coefficients and Sorensen-Dice index.

resultsWhen considering adjusted estimates of muscle volume, there were significant differences observed between groups for gluteus medius (adjusted mean difference 23 858 mm

conclusionsThe increased muscle volumes in the symptomatic group compared with controls could be associated with increased myofibrillar size, sarcoplasmic hypertrophy or both. These changes may facilitate greater muscular efficiency for a given load, enabling the athlete to maintain their normal level of function. In addition, the CNNs for muscle segmentation was more efficient and demonstrated excellent reliability in comparison to manual segmentations.

Indexed as

Adipose TissueMachine LearningAdultCross-Sectional StudiesFemaleHumansMagnetic Resonance ImagingMaleMuscle, SkeletalPainYoung AdultButtocksGlutealMusclePain

Identifiers

PMID39343707
PMCPMC11634488

What OpenQuestion holds

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Registered trials

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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.