Evidence map›Paper›PMID 40947375›Full record

ArticleUltrasound in medicine & biology2025

Open-Source AI for Vastus Lateralis and Adipose Tissue Segmentation to Assess Muscle Size and Quality.

McKenzie S White, Arimitsu Horikawa-Strakovsky, Kirby P Mayer, Brian W Noehren, Yuan Wen

Abstract read
In one paragraph

Article in Ultrasound in medicine & biology, 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

5 authors.

McKenzie S WhiteDepartment of Physical Therapy, College of Health Sciences, University of Kentucky, Lexington, KY, USA.
Arimitsu Horikawa-StrakovskyMath, Science and Technology Center, Paul Laurence Dunbar High School, Lexington, KY, USA; Institute for Biomedical Informatics, University of Kentucky, Lexington, KY, USA; Center for Muscle Biology, University of Kentucky, Lexington, KY, USA.
Kirby P MayerDepartment of Physical Therapy, College of Health Sciences, University of Kentucky, Lexington, KY, USA; Center for Muscle Biology, University of Kentucky, Lexington, KY, USA.
Brian W NoehrenDepartment of Physical Therapy, College of Health Sciences, University of Kentucky, Lexington, KY, USA; Center for Muscle Biology, University of Kentucky, Lexington, KY, USA.
Yuan WenInstitute for Biomedical Informatics, University of Kentucky, Lexington, KY, USA; Center for Muscle Biology, University of Kentucky, Lexington, KY, USA; Department of Physiology, University of Kentucky, Lexington, KY, USA; Division of Biomedical Informatics, Department of Internal Medicine, University of Kentucky, Lexington, KY, USA. Electronic address: ywen2@uky.edu.

Funding

Kentucky Center for Clinical and Translational ScienceUL1TR001998 · NCATS · UNIVERSITY OF KENTUCKY · PI HARTMANN, KATHERINE E, KERN, PHILIP A · 2016 to 2025
$34.2M
Sex-based Muscular Adaptations, Capillary dysfunction and functional decline impact Knee-related psychosocial outcomes after acute knee injury (SMACK)R01AR078316 · NIAMS · UNIVERSITY OF KENTUCKY · PI Christopher Fry, Brian Noehren · 2021 to 2026
$3.2M
Myostatin Alters Muscle Composition as The Result of an ACL InjuryR01AR072061 · NIAMS · UNIVERSITY OF TEXAS MED BR GALVESTON · PI FRY, CHRISTOPHER · 2018 to 2023
$2.2M
Cellular and Physical Function Outcomes Leading to Failed Muscle Recovery After Critical IllnessK23AR079583 · NIAMS · UNIVERSITY OF KENTUCKY · PI Kirby P Mayer · 2022 to 2026
$744k
Contribution of ribosome specialization to the pathophysiology of muscular dystrophyR00AR081367 · NIAMS · UNIVERSITY OF KENTUCKY · PI YUAN WEN · 2024 to 2026
$719k
NCATS NIH HHS UL1 TR001998NIAMS NIH HHS K23 AR079583NIAMS NIH HHS R00 AR081367NIAMS NIH HHS R01 AR072061NIAMS NIH HHS R01 AR078316
6 · The paper itself

Abstract

objectiveUltrasound imaging is a clinically feasible method for assessing muscle size and quality, but manual processing is time-consuming and difficult to scale. Existing artificial intelligence (AI) models measure muscle cross-sectional area, but they do not include assessments of muscle quality or account for the influence of subcutaneous adipose tissue thickness on echo intensity measurements. We developed an open-source AI model to accurately segment the vastus lateralis and subcutaneous adipose tissue in B-mode images for automating measurements of muscle size and quality.

methodsThe model was trained on 612 ultrasound images from 44 participants who had anterior cruciate ligament reconstruction. Model generalizability was evaluated on a test set of 50 images from 14 unique participants. A U-Net architecture with ResNet50 backbone was used for segmentation. Performance was assessed using the Dice coefficient and Intersection over Union (IoU). Agreement between model predictions and manual measurements was evaluated using intraclass correlation coefficients (ICCs), R² values and standard errors of measurement (SEM).

resultsDice coefficients were 0.9095 and 0.9654 for subcutaneous adipose tissue and vastus lateralis segmentation, respectively. Excellent agreement was observed between model predictions and manual measurements for cross-sectional area (ICC = 0.986), echo intensity (ICC = 0.991) and subcutaneous adipose tissue thickness (ICC = 0.996). The model demonstrated high reliability with low SEM values for clinical measurements (cross-sectional area: 1.15 cm², echo intensity: 1.28-1.78 a.u.).

conclusionWe developed an open-source AI model that accurately segments the vastus lateralis and subcutaneous adipose tissue in B-mode ultrasound images, enabling automated measurements of muscle size and quality.

Indexed as

Adipose TissueArtificial IntelligenceQuadriceps MuscleAdultAnterior Cruciate Ligament ReconstructionFemaleHumansMaleMiddle AgedOrgan SizeReproducibility of ResultsSubcutaneous FatUltrasonographyYoung AdultDeep learningEcho intensityMuscle qualityOpen-sourceSemantic segmentationUltrasound

Identifiers

PMID40947375
PMCPMC12453877

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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.