ArticlePloS one2023
Automated 3-dimensional MRI segmentation for the posterosuperior rotator cuff tear lesion using deep learning algorithm.
Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Changes in shoulder range of motion following arthroscopic rotator cuff repair: a systematic review.BMC musculoskeletal disorders · 2025Pooled it
- The implementation of artificial intelligence in upper extremity surgery: a systematic review.Frontiers in artificial intelligence · 2025Pooled it
- Current state of research and future developments of artificial intelligence in pain diagnosis and treatment.Journal of translational medicine · 2026Review
- Artificial intelligence revolution in shoulder magnetic resonance imaging: current evidence and future directions for rotator cuff diagnosis.Clinics in shoulder and elbow · 2026Article
- Patient-Level Classification of Rotator Cuff Tears on Shoulder MRI Using an Explainable Vision Transformer Framework.Journal of clinical medicine · 2026Article
- Artificial intelligence and robotic technologies redefining precision and personalization in orthopedic surgery: a narrative review.Frontiers in bioengineering and biotechnology · 2026Review
- Advances in Shoulder Pain Imaging: A Narrative Review of Current Practice and Emerging Trends.Current medical imaging · 2025Article
- Trustworthy deep learning for the automated quantification of the fatty infiltration of the rotator cuff muscles using magnetic resonance imaging.JSES international · 2025Article
- Detection and classification of supraspinatus pathologies on shoulder magnetic resonance images using a code-free deep learning application.Asia-Pacific journal of sports medicine, arthroscopy, rehabilitation and technology · 2025Article
- Artificial Intelligence in Orthopedic Surgery: Current Applications, Challenges, and Future Directions.MedComm · 2025Review
- Artificial Intelligence in Rotator Cuff Tear Detection: A Systematic Review of MRI-Based Models.Diagnostics (Basel, Switzerland) · 2025Review
- Artificial intelligence demonstrates potential to enhance orthopaedic imaging across multiple modalities: A systematic review.Journal of experimental orthopaedics · 2025Review
- Artificial intelligence applications in the management of musculoskeletal disorders of the shoulder: A systematic review.Journal of experimental orthopaedics · 2025Review
- Application of deep learning for diagnosis of shoulder diseases in older adults: a narrative review.Ewha medical journal · 2025Review
- Application of Artificial Intelligence in Shoulder Pathology.Diagnostics (Basel, Switzerland) · 2024Review
- Review
- Re-tear after arthroscopic rotator cuff repair can be predicted using deep learning algorithm.Frontiers in artificial intelligence · 2024Article
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Authors and funding
7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
introductionRotator cuff tear (RCT) is a challenging and common musculoskeletal disease. Magnetic resonance imaging (MRI) is a commonly used diagnostic modality for RCT, but the interpretation of the results is tedious and has some reliability issues. In this study, we aimed to evaluate the accuracy and efficacy of the 3-dimensional (3D) MRI segmentation for RCT using a deep learning algorithm.
methodsA 3D U-Net convolutional neural network (CNN) was developed to detect, segment, and visualize RCT lesions in 3D, using MRI data from 303 patients with RCTs. The RCT lesions were labeled by two shoulder specialists in the entire MR image using in-house developed software. The MRI-based 3D U-Net CNN was trained after the augmentation of a training dataset and tested using randomly selected test data (training: validation: test data ratio was 6:2:2). The segmented RCT lesion was visualized in a three-dimensional reconstructed image, and the performance of the 3D U-Net CNN was evaluated using the Dice coefficient, sensitivity, specificity, precision, F1-score, and Youden index.
resultsA deep learning algorithm using a 3D U-Net CNN successfully detected, segmented, and visualized the area of RCT in 3D. The model's performance reached a 94.3% of Dice coefficient score, 97.1% of sensitivity, 95.0% of specificity, 84.9% of precision, 90.5% of F1-score, and Youden index of 91.8%.
conclusionThe proposed model for 3D segmentation of RCT lesions using MRI data showed overall high accuracy and successful 3D visualization. Further studies are necessary to determine the feasibility of its clinical application and whether its use could improve care and outcomes.
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