Evidence map›Paper›PMID 40801163›Full record

ArticleKnee surgery, sports traumatology, arthroscopy : official journal of the ESSKA2025

Development of a multimodal vision transformer model for predicting traumatic versus degenerative rotator cuff tears on magnetic resonance imaging: A single-centre retrospective study.

Felix C Oettl, Ali B Malayeri, Pascal R Furrer, Karl Wieser, Philipp Fürnstahl, Samy Bouaicha

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Article in Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Felix C OettlDepartment of Orthopedic Surgery, Balgrist University Hospital, University of Zürich, Zurich, Switzerland.ORCID https://orcid.org/0000-0001-9721-685X
Ali B MalayeriResearch in Orthopedic Computer Science Group, Balgrist University Hospital, University of Zurich, Zurich, Switzerland.
Pascal R FurrerDepartment of Orthopedic Surgery, Balgrist University Hospital, University of Zürich, Zurich, Switzerland.
Karl WieserDepartment of Orthopedic Surgery, Balgrist University Hospital, University of Zürich, Zurich, Switzerland.ORCID https://orcid.org/0000-0001-8495-6189
Philipp FürnstahlResearch in Orthopedic Computer Science Group, Balgrist University Hospital, University of Zurich, Zurich, Switzerland.ORCID https://orcid.org/0000-0001-6484-6206
Samy BouaichaDepartment of Orthopedic Surgery, Balgrist University Hospital, University of Zürich, Zurich, Switzerland.ORCID https://orcid.org/0000-0002-1111-9213

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThe differentiation between traumatic and degenerative rotator cuff tears (RCTs remains a diagnostic challenge with significant implications for treatment planning. While magnetic resonance imaging (MRI) is standard practice, traditional radiological interpretation has shown limited reliability in distinguishing these etiologies. This study evaluates the potential of artificial intelligence (AI) models, specifically a multimodal vision transformer (ViT), to differentiate between traumatic and degenerative RCT.

methodsIn this retrospective, single-centre study, 99 shoulder MRIs were analysed from patients who underwent surgery at a specialised university shoulder unit between 2016 and 2019. The cohort was divided into training (n = 79) and validation (n = 20) sets. The traumatic group required a documented relevant trauma (excluding simple lifting injuries), previously asymptomatic shoulder and MRI within 3 months posttrauma. The degenerative group was of similar age and injured tendon, with patients presenting with at least 1 year of constant shoulder pain prior to imaging and no trauma history. The ViT was subsequently combined with demographic data to finalise in a multimodal ViT. Saliency maps are utilised as an explainability tool.

resultsThe multimodal ViT model achieved an accuracy of 0.75 ± 0.08 with a recall of 0.8 ± 0.08, specificity of 0.71 ± 0.11 and a F1 score of 0.76 ± 0.1. The model maintained consistent performance across different patient subsets, demonstrating robust generalisation. Saliency maps do not show a consistent focus on the rotator cuff.

conclusionAI shows potential in supporting the challenging differentiation between traumatic and degenerative RCT on MRI. The achieved accuracy of 75% is particularly significant given the similar groups which presented a challenging diagnostic scenario. Saliency maps were utilised to ensure explainability, the given lack of consistent focus on rotator cuff tendons hints towards underappreciated aspects in the differentiation. LEVEL OF EVIDENCE: Not applicable.

Indexed as

Artificial IntelligenceMagnetic Resonance ImagingRotator Cuff InjuriesAdultAgedDiagnosis, DifferentialFemaleHumansMaleMiddle AgedReproducibility of ResultsRetrospective Studiesartificial intelligencemagnetic resonance imagingrotator cuff tearshoulder surgeryvision transformer

Identifiers

PMID40801163
PMCPMC12582230

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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.