ArticleDiagnostics (Basel, Switzerland)2023
Deep-Learning-Based Automated Rotator Cuff Tear Screening in Three Planes of Shoulder MRI.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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The trial behind it
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Who cites it
16 citing papers in PubMed.
- Hierarchical Multimodal Fusion of Multi-Sequence MRI and Clinical Metadata for the Classification of Rotator Cuff Tears.Journal of clinical medicine · 2026Article
- AI-Assisted Detection of Supraspinatus Tendon Pathologies Using a Hierarchical Deep Learning Model to Improve Clinical Applicability: Development and Evaluation Study.JMIR medical informatics · 2026Article
- Artificial intelligence in shoulder arthroplasty: latest concepts and clinical integration.Journal of orthopaedic surgery and research · 2026Review
- Artificial intelligence and machine learning: an important new set of tools for clinical shoulder arthroplasty research.JSES international · 2026Review
- Artificial intelligence revolution in shoulder magnetic resonance imaging: current evidence and future directions for rotator cuff diagnosis.Clinics in shoulder and elbow · 2026Article
- Discriminative and predictive power of radiomics features from preoperative MRI studies in patients with supraspinatus lesions.BMC musculoskeletal disorders · 2026Article
- Identification of TFCC substructure injury in wrist MRI using computer vision: a diagnostic aid for radiologists.Skeletal radiology · 2026Article
- A Systematic Literature Review of You Only Look Once Architectures (v1-v12) in Healthcare Systems.Diagnostics (Basel, Switzerland) · 2026Review
- Who Is the Surgeon Now: Human Hands or Machine Minds? Artificial Intelligence in Orthopedics from Diagnosis to Follow-Up-A Structured Narrative Review.Journal of clinical medicine · 2026Review
- Patient-Level Classification of Rotator Cuff Tears on Shoulder MRI Using an Explainable Vision Transformer Framework.Journal of clinical medicine · 2026Article
- Prediction of lymph node metastasis and recurrence risk in early-stage oral tongue squamous cell carcinoma with fully automated MRI deep learning.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Research on real-time detection and staging technology for pressure injuries in critically ill patients based on the YOLOv8 deep learning model.Frontiers in public health · 2026Article
- Advances in Shoulder Pain Imaging: A Narrative Review of Current Practice and Emerging Trends.Current medical imaging · 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 Rotator Cuff Tear Detection: A Systematic Review of MRI-Based Models.Diagnostics (Basel, Switzerland) · 2025Review
- Artificial intelligence applications in the management of musculoskeletal disorders of the shoulder: A systematic review.Journal of experimental orthopaedics · 2025Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
This study aimed to develop a screening model for rotator cuff tear detection in all three planes of routine shoulder MRI using a deep neural network. A total of 794 shoulder MRI scans (374 men and 420 women; aged 59 ± 11 years) were utilized. Three musculoskeletal radiologists labeled the rotator cuff tear. The YOLO v8 rotator cuff tear detection model was then trained; training was performed with all imaging planes simultaneously and with axial, coronal, and sagittal images separately. The performances of the models were evaluated and compared using receiver operating curves and the area under the curve (AUC). The AUC was the highest when using all imaging planes (0.94;
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Registered trials
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.