ArticleJournal of personalized medicine2022
Deep Learning to Detect Triangular Fibrocartilage Complex Injury in Wrist MRI: Retrospective Study with Internal and External Validation.
Article in Journal of personalized medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Identification of TFCC substructure injury in wrist MRI using computer vision: a diagnostic aid for radiologists.Skeletal radiology · 2026Article
- Artificial Intelligence vs. Human Experts in Temporomandibular Joint MRI Interpretation: A Systematic Review.Healthcare (Basel, Switzerland) · 2026Review
- Artificial Intelligence-Enhanced Quantitative 3D Analysis of Distal Radioulnar Ligament Insertion Footprints of the Triangular Fibrocartilage Complex With Interactive Validation.Orthopaedic surgery · 2026Article
- One-year follow-up of patients with Palmer type IIC central perforation of triangular fibrocartilage complex tears treated with arthroscopic absorbable suture repair and conservative treatment.Frontiers in surgery · 2026Article
- Artificial intelligence in musculoskeletal imaging: realistic clinical applications in the next decade.Skeletal radiology · 2024Review
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7 authors.
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Abstract
objectiveTo use deep learning to predict the probability of triangular fibrocartilage complex (TFCC) injury in patients' MRI scans.
methodsWe retrospectively studied medical records over 11 years and 2 months (1 January 2009-29 February 2019), collecting 332 contrast-enhanced hand MRI scans showing TFCC injury (143 scans) or not (189 scans) from a general hospital. We employed two convolutional neural networks with the MRNet (Algorithm 1) and ResNet50 (Algorithm 2) framework for deep learning. Explainable artificial intelligence was used for heatmap analysis. We tested deep learning using an external dataset containing the MRI scans of 12 patients with TFCC injuries and 38 healthy subjects.
resultsIn the internal dataset, Algorithm 1 had an AUC of 0.809 (95% confidence interval-CI: 0.670-0.947) for TFCC injury detection as well as an accuracy, sensitivity, and specificity of 75.6% (95% CI: 0.613-0.858), 66.7% (95% CI: 0.438-0.837), and 81.5% (95% CI: 0.633-0.918), respectively, and an F1 score of 0.686. Algorithm 2 had an AUC of 0.871 (95% CI: 0.747-0.995) for TFCC injury detection and an accuracy, sensitivity, and specificity of 90.7% (95% CI: 0.787-0.962), 88.2% (95% CI: 0.664-0.966), and 92.3% (95% CI: 0.763-0.978), respectively, and an F1 score of 0.882. The accuracy, sensitivity, and specificity for radiologist 1 were 88.9, 94.4 and 85.2%, respectively, and for radiologist 2, they were 71.1, 100 and 51.9%, respectively.
conclusionsA modified MRNet framework enables the detection of TFCC injury and guides accurate diagnosis.
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