ArticleSkeletal radiology2022
Deep learning for accurately recognizing common causes of shoulder pain on radiographs.
Article in Skeletal radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The implementation of artificial intelligence in upper extremity surgery: a systematic review.Frontiers in artificial intelligence · 2025Pooled it
- Artificial Intelligence in Fracture Detection: A Systematic Review and Meta-Analysis.Radiology · 2022Pooled it
- Current state of research and future developments of artificial intelligence in pain diagnosis and treatment.Journal of translational medicine · 2026Review
- Hybrid deep learning ensemble model for detecting small to medium rotator cuff tears from shoulder radiographs.Journal of orthopaedic surgery and research · 2026Article
- Dynamic ultrasound motion metrics combined with deep learning for clinical differentiation of subacromial impingement syndrome.La Radiologia medica · 2026Article
- Fracture detection of distal radius using deep-learning-based dual-channel feature fusion algorithm.Chinese journal of traumatology = Zhonghua chuang shang za zhi · 2026Article
- [Comparison of the Clinical Examination, Magnetic Resonance Imaging and Intraoperative].Acta chirurgiae orthopaedicae et traumatologiae Cechoslovaca · 2025Article
- Comparative accuracy of two commercial AI algorithms for musculoskeletal trauma detection in emergency radiographs.Emergency radiology · 2025Article
- A first-of-its-kind two-body statistical shape model of the arthropathic shoulder: enhancing biomechanics and surgical planning.Journal of orthopaedic surgery and research · 2025Article
- AE-BoNet: A Deep Learning Method for Pediatric Bone Age Estimation using an Unsupervised Pre-Trained Model.Journal of biomedical physics & engineering · 2025Article
- Quantitative Spatial Analysis on Radiographic Features of Rotator Cuff Calcifications: An Exploratory Study.Biomedicines · 2025Article
- Detection of Acromion Types in Shoulder Magnetic Resonance Image Examination with Developed Convolutional Neural Network and Textural-Based Content-Based Image Retrieval System.Journal of clinical medicine · 2025Article
- Role of Artificial Intelligence in Minimizing Missed and Undiagnosed Fractures Among Trainee Residents.Journal of multidisciplinary healthcare · 2025Review
- Application of deep learning for diagnosis of shoulder diseases in older adults: a narrative review.Ewha medical journal · 2025Review
- Artificial intelligence can be used in the identification and classification of shoulder osteoarthritis and avascular necrosis on plain radiographs: a training study of 7,139 radiograph sets.Acta orthopaedica · 2024Article
- Application of Artificial Intelligence in Shoulder Pathology.Diagnostics (Basel, Switzerland) · 2024Review
- Current clinical applications of artificial intelligence in shoulder surgery: what the busy shoulder surgeon needs to know and what's coming next.JSES reviews, reports, and techniques · 2023Review
- Artificial intelligence-based applications in shoulder surgery leaves much to be desired: a systematic review.JSES reviews, reports, and techniques · 2023Review
- Artificial intelligence in musculoskeletal imaging: a perspective on value propositions, clinical use, and obstacles.Skeletal radiology · 2022Review
Corrections and comments
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Authors and funding
7 authors.
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Abstract
objectiveTraining a convolutional neural network (CNN) to detect the most common causes of shoulder pain on plain radiographs and to assess its potential value in serving as an assistive device to physicians. MATERIALS AND
methodsWe used a CNN of the ResNet-50 architecture which was trained on 2700 shoulder radiographs from clinical practice of multiple institutions. All radiographs were reviewed and labeled for six findings: proximal humeral fractures, joint dislocation, periarticular calcification, osteoarthritis, osteosynthesis, and joint endoprosthesis. The trained model was then evaluated on a separate test dataset, which was previously annotated by three independent expert radiologists. Both the training and the test datasets included radiographs of highly variable image quality to reflect the clinical situation and to foster robustness of the CNN. Performance of the model was evaluated using receiver operating characteristic (ROC) curves, the thereof derived AUC as well as sensitivity and specificity.
resultsThe developed CNN demonstrated a high accuracy with an area under the curve (AUC) of 0.871 for detecting fractures, 0.896 for joint dislocation, 0.945 for osteoarthritis, and 0.800 for periarticular calcifications. It also detected osteosynthesis and endoprosthesis with near perfect accuracy (AUC 0.998 and 1.0, respectively). Sensitivity and specificity were 0.75 and 0.86 for fractures, 0.95 and 0.65 for joint dislocation, 0.90 and 0.86 for osteoarthrosis, and 0.60 and 0.89 for calcification.
conclusionCNNs have the potential to serve as an assistive device by providing clinicians a means to prioritize worklists or providing additional safety in situations of increased workload.
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