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Related Experiment Video

Updated: Feb 14, 2026

Arthroscopic Management of Massive Irreparable Rotator Cuff Tears: Whole Rotator Cable Reconstruction Using Proximal Biceps Tendon Autograft
07:22

Arthroscopic Management of Massive Irreparable Rotator Cuff Tears: Whole Rotator Cable Reconstruction Using Proximal Biceps Tendon Autograft

Published on: June 6, 2025

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Patient-Level Classification of Rotator Cuff Tears on Shoulder MRI Using an Explainable Vision Transformer Framework.

Murat Aşçı1, Sergen Aşık2,3, Ahmet Yazıcı3,4

  • 1Department of Orthopedics and Traumatology, Faculty of Medicine, Bilecik Şeyh Edebali University, Bilecik 11230, Türkiye.

Journal of Clinical Medicine
|February 13, 2026
PubMed
Summary

This study introduces the Patient-Aware Vision Transformer (Pa-ViT) for diagnosing rotator cuff tears (RCTs) using MRI. The explainable AI model achieves 91% accuracy, improving detection of partial-thickness tears.

Keywords:
automated diagnosisdeep learningexplainable aimagnetic resonance imagingmedical image analysismultiple instance learningrotator cuff tearvision transformer

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Area of Science:

  • Orthopedics
  • Radiology
  • Artificial Intelligence

Background:

  • Diagnosing rotator cuff tears (RCTs) with MRI is challenging due to complex anatomy and interobserver variability.
  • Slice-centric Convolutional Neural Networks (CNNs) struggle with volumetric context for accurate RCT grading.
  • This study addresses the need for improved automated RCT classification.

Purpose of the Study:

  • Develop and validate the Patient-Aware Vision Transformer (Pa-ViT) for automated, patient-level RCT classification.
  • Enhance diagnostic sensitivity, especially for subtle partial-thickness tears.
  • Provide an explainable deep-learning framework for clinical decision support.

Main Methods:

  • Utilized a retrospective dataset of 2447 T2-weighted coronal shoulder MRI scans.
  • Employed a Vision Transformer (ViT-Base) backbone within a Weakly-Supervised Multiple Instance Learning (MIL) paradigm.
  • Trained the model with weighted cross-entropy loss and benchmarked against CNNs and traditional classifiers.

Main Results:

  • Pa-ViT achieved 91% overall accuracy and a 0.91 macro-averaged F1-score, outperforming VGG-16 (87%).
  • Demonstrated superior performance for partial-thickness tears (ROC AUC: 0.903).
  • Attention Rollout visualizations confirmed reliance on anatomical features, not artifacts.

Conclusions:

  • Pa-ViT effectively models long-range dependencies, offering a robust alternative to CNNs for RCT diagnosis.
  • The framework provides a clinically viable, explainable tool for enhanced diagnostic sensitivity.
  • Pa-ViT shows promise for improving the detection of subtle partial-thickness rotator cuff tears.