Evidence map›Paper›PMID 39921123›Full record

ArticleJournal of shoulder and elbow surgery2025

Developing a three-dimensional convolutional neural network for automated full-volume multi-tissue segmentation of the shoulder with comparisons to Goutallier classification and partial volume muscle quality analysis.

Brian Kim, Ziba Gandomkar, Marnee J McKay, Amee L Seitz, Evert O Wesselink, Benjamin Cass, Allan A Young, James M Linklater, Jeremy Szajer, Kushalappa Subbiah and 2 more

Abstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of shoulder and elbow surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Review
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

12 authors.

Brian KimThe Kolling Institute, The University of Sydney, Faculty of Medicine and Health & The Northern Sydney Local Health District, St Leonards, NSW, Australia; School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia. Electronic address: brian.kim@sydney.edu.au.
Ziba GandomkarDiscipline of Medical Imaging Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia.
Marnee J McKaySchool of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia.
Amee L SeitzDepartment of Physical Therapy and Human Movement Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
Evert O WesselinkFaculty of Behavioural and Movement Sciences, Amsterdam Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, USA.
Benjamin CassSydney Shoulder Research Institute, Sydney, NSW, Australia.
Allan A YoungSydney Shoulder Research Institute, Sydney, NSW, Australia.
James M LinklaterCastlereagh Imaging, Sydney, NSW, Australia.
Jeremy SzajerCastlereagh Imaging, Sydney, NSW, Australia.
Kushalappa SubbiahSydney Shoulder Research Institute, Sydney, NSW, Australia.
James M ElliottThe Kolling Institute, The University of Sydney, Faculty of Medicine and Health & The Northern Sydney Local Health District, St Leonards, NSW, Australia; School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia.
Kenneth A WeberDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, USA.

Funding

Dermatomal Mapping with Spinal Cord Functional Magnetic Resonance ImagingR01NS133305 · NINDS · STANFORD UNIVERSITY · PI Kenneth Arnold Weber · 2023 to 2026
$2.3M
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
NINDS NIH HHS K23 NS104211NINDS NIH HHS L30 NS108301NINDS NIH HHS R01 NS128478NINDS NIH HHS R01 NS133305
6 · The paper itself

Abstract

backgroundPreoperative intramuscular fat (IMF) is a strong predictor of tendon failure after a rotator cuff repair. Due to the contemporary labor intensive and time-dependent manual segmentation required for quantitative assessment of IMF, clinical implementation remains a challenge. The emergence of accurate three-dimensional evaluation of the rotator cuff may permit implementation with greater inter-rater reliability than common subjective scales (eg, Goutallier classification (GC)). Here, we developed and validated a convolutional neural network (CNN) model for auto-segmentation of the shoulder on Dixon magnetic resonance imaging. Also, we aimed to assess the agreement among GC, two-dimensional (2D), and 3D IMF, including their discriminatory ability for the identification of muscles above an IMF threshold shown to negatively impact surgical outcomes (ie, GC ≥ 3).

methodsThis study retrospectively obtained fat-water Dixon shoulder magnetic resonance imagings between March 2023 and March 2024 to develop and validate a CNN model for the segmentation of individual rotator cuff muscles and surrounding tissues. The CNN model was trained using a modified U-Net architecture (n = 80) and tested on an external dataset (n = 25). Accuracy was primarily evaluated using the Dice Similarity Coefficient (DSC) compared to manual segmentation. Reliability was evaluated by the intraclass correlation coefficient (ICC

resultsThe model after training (37 male and 43 female, mean age = 55.8 ± 15.6 years) and testing (15 male and 10 female, mean age = 56.6 ± 19.7 years) produced DSCs of ≥0.89 except for teres minor (DSC = 0.86 ± 0.03). The model demonstrated excellent reliability for volume (ICC

conclusionThe development of a CNN model allows for efficient, accurate segmentation of muscle and bone, enabling reliable evaluation of muscle quality. The model demonstrates that 2D evaluation of IMF is insufficient for differentiating between rotator cuff muscles on either side of a clinically meaningful IMF threshold on the GC scheme, whereas 3D IMF shows excellent discriminant validity across all rotator cuff muscles.

Indexed as

Imaging, Three-DimensionalMagnetic Resonance ImagingNeural Networks, ComputerRotator CuffRotator Cuff InjuriesAdipose TissueAgedConvolutional Neural NetworksFemaleHumansMaleMiddle AgedReproducibility of ResultsRetrospective StudiesArtificial intelligenceconvolutional neural networkfatty infiltrationMRImuscle volumerotator cuffU-Net

Identifiers

PMID39921123
PMCPMC12327696

What OpenQuestion holds

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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.