25dkou518 10.3205/25dkou518 urn:nbn:de:0183-25dkou5187 Meeting Abstract Öttl Öttl Felix Conrad FC

Universitätsklinik Balgrist, Zürich, Schweiz Hospital for Special Surgery, New York City, USA

author Bahari Malayeri Bahari Malayeri Ali A

Research in Orthopedic Computer Science, Balgrist University Hospital, University of Zurich, Zürich, Schweiz

author Furrer Furrer Pascal P

Universitätsklinik Balgrist, Zürich, Schweiz

author Fürnstahl Fürnstahl Philipp P

Universitätsklinik Balgrist, Zürich, Schweiz

author Wieser Wieser Karl K

Universitätsklinik Balgrist, Zürich, Schweiz

author Bouaicha Bouaicha Samy S

Universitätsklinik Balgrist, Zürich, Schweiz

author German Medical Science GMS Publishing House

Düsseldorf

610 20251031 engl This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 License. Dieser Artikel ist ein Open-Access-Artikel und steht unter den Lizenzbedingungen der Creative Commons Attribution 4.0 License (Namensnennung). M0634 518 Deutsche Gesellschaft für Orthopädie und Unfallchirurgie Deutsche Gesellschaft für Orthopädie und Orthopädische Chirurgie Deutsche Gesellschaft für Unfallchirurgie Berufsverband für Orthopädie und Unfallchirurgie Deutscher Kongress für Orthopädie und Unfallchirurgie (DKOU 2025) Abstracts | Digitalisierung 5 Berlin 20251028 20251031 AB82-4041 TextObjectives and questions: The differentiation between traumatic and degenerative rotator cuff tears (RCT) 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 Vision Transformer (ViT) (Figure 1 ), to differentiate between traumatic and degenerative shoulder pathologies on MRI.Material and methods: In this retrospective, single-center study, we analyzed 99 clinically validated shoulder MRI datasets from patients who underwent surgery at a specialized university shoulder unit between 2016 and 2019. The cohort was divided into training (n=79) and validation (n=20) sets. Inclusion criteria for the traumatic group required a documented relevant trauma (excluding simple lifting injuries), previously asymptomatic shoulder, and MRI within 3 months post-trauma. The degenerative group was matched for age and affected tendon, with patients presenting with at least one year of constant shoulder pain prior to imaging and no trauma history. Exclusion criteria included age >66 years, Goutallier Grade >II muscle atrophy in the affected muscle, and previous shoulder surgery. The ViT model was trained using the Adam optimizer with a learning rate of 1×10−5 over 60 epochs, employing 5-fold cross-validation. Saliency maps are utilized as an explainability tool. All research was conducted according to the TRIPOD guidelines.Results: The ViT model achieved an accuracy of 0.72 ± 0.03 with a recall of 0.71 ± 0.09, specificity of 0.72 ± 0.06 and a F1 score of 0.7 ± 0.07. Analysis of the confusion matrix revealed that the model correctly identified an average of 7.2 ± 2.5 traumatic and 7.2 ± 2.48 degenerative cases per validation set. The model maintained consistent performance across different patient subsets, demonstrating robust generalization. Saliency maps do not show a consistent focus on the rotator cuff (Figure 2 ).Discussion and conclusions: AI shows potential in supporting the challenging differentiation between traumatic and degenerative rotator cuff tears on MRI. The achieved accuracy of 72% is particularly significant given our stringent inclusion criteria and age-matched cohorts, which presented a challenging diagnostic scenario. Saliency maps were utilized to ensure explainability, the given lack of consistent focus on rotator cuff tendons hints towards underappreciated aspects in the differentiation. These findings establish a foundation for developing AI-assisted diagnostic tools in musculoskeletal radiology, potentially enhancing both diagnostic accuracy. 0

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