ArticleRadiology2018
Deep Learning Approach for Evaluating Knee MR Images: Achieving High Diagnostic Performance for Cartilage Lesion Detection.
Article in Radiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 124 papers, 2 of them syntheses that pooled it.
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Who cites it
124 citing papers in PubMed, 2 syntheses or guidelines pooled it, 292 citations in OpenAlex.
- MRI deep learning models for assisted diagnosis of knee pathologies: a systematic review.European radiology · 2025Pooled it
- Does Artificial Intelligence Outperform Natural Intelligence in Interpreting Musculoskeletal Radiological Studies? A Systematic Review.Clinical orthopaedics and related research · 2020Pooled it
- Two-stage deep learning framework for magnetic resonance imaging-based detection of temporomandibular joint osteoarthritis.Oral radiology · 2026Article
- Comparison of Deep-Learning-Based Reconstruction of Knee MRI with Conventional Sequences in Patients with Knee Pain.Diagnostics (Basel, Switzerland) · 2026Article
- PET/CT-based quantification of intratumoral heterogeneity for predicting pathological complete response in breast cancer patients treated with neoadjuvant chemotherapy: A multicentre study.European journal of nuclear medicine and molecular imaging · 2026Article
- MedNet-FS: a few-shot learning framework for 3D MRI-based knee injury classification.Scientific reports · 2026Article
- AI-based knee osteoarthritis progression prediction: a comprehensive global bibliometric and hotspot evolution analysis (2010-2025).Knee surgery & related research · 2026Article
- Advances in cartilage imaging techniques.Nature reviews. Rheumatology · 2026Review
- Accuracy of Diagnosis and Anticipation of Future Treatment in Pediatric Peripelvic Infections: The Role of Multisurgeon Review and Implications for Future Machine Learning Algorithms.Journal of the American Academy of Orthopaedic Surgeons. Global research & reviews · 2026Article
- Applications of artificial intelligence for real-world evidence generation: a protocol for a living scoping review.BMJ open · 2026Article
- [Differentiation of osteoarthritis phenotypes on MRI using artificial intelligence].Orthopadie (Heidelberg, Germany) · 2026Review
- Effective automatic classification methods via deep learning for multi-type infectious keratitis diagnosis.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Article
- Revolutionizing emergency care: an overview of the transformative role of artificial intelligence in diagnosis, triage, and patient management.International journal of emergency medicine · 2025Review
- Development of a multi-task deep learning system for classification of nine common knee abnormalities on MRI: a large-scale, multicentre, stepwise validation study.EClinicalMedicine · 2025Article
- Comparative analysis of deep learning models for early detection of osteoarthritis using knee radiographs: A retrospective study.Journal of clinical orthopaedics and trauma · 2025Article
- Magnetic Resonance Imaging-Based 3-Dimensional Models of the Pelvis and Hip Using Machine Learning for Automatic Bone Segmentation in a Dynamic Hip Impingement Simulation.Orthopaedic journal of sports medicine · 2025Article
- AI for Lesion Detection in Musculoskeletal Radiology.Journal of the Korean Society of Radiology · 2025Review
- Oncogene-driven lung cancer in the era of radiogenomics: current evidence and future developments.Discover oncology · 2025Review
- Effect of Deep Learning-Based Artificial Intelligence on Radiologists' Performance in Identifying Nigrosome 1 Abnormalities on Susceptibility Map-Weighted Imaging.Korean journal of radiology · 2025Article
- Exploration of 3D Few-Shot Learning Techniques for Classification of Knee Joint Injuries on MR Images.Diagnostics (Basel, Switzerland) · 2025Article
64 more citing papers are in PubMed but not listed here.
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
Abstract
Purpose To determine the feasibility of using a deep learning approach to detect cartilage lesions (including cartilage softening, fibrillation, fissuring, focal defects, diffuse thinning due to cartilage degeneration, and acute cartilage injury) within the knee joint on MR images. Materials and Methods A fully automated deep learning-based cartilage lesion detection system was developed by using segmentation and classification convolutional neural networks (CNNs). Fat-suppressed T2-weighted fast spin-echo MRI data sets of the knee of 175 patients with knee pain were retrospectively analyzed by using the deep learning method. The reference standard for training the CNN classification was the interpretation provided by a fellowship-trained musculoskeletal radiologist of the presence or absence of a cartilage lesion within 17 395 small image patches placed on the articular surfaces of the femur and tibia. Receiver operating curve (ROC) analysis and the κ statistic were used to assess diagnostic performance and intraobserver agreement for detecting cartilage lesions for two individual evaluations performed by the cartilage lesion detection system. Results The sensitivity and specificity of the cartilage lesion detection system at the optimal threshold according to the Youden index were 84.1% and 85.2%, respectively, for evaluation 1 and 80.5% and 87.9%, respectively, for evaluation 2. Areas under the ROC curve were 0.917 and 0.914 for evaluations 1 and 2, respectively, indicating high overall diagnostic accuracy for detecting cartilage lesions. There was good intraobserver agreement between the two individual evaluations, with a κ of 0.76. Conclusion This study demonstrated the feasibility of using a fully automated deep learning-based cartilage lesion detection system to evaluate the articular cartilage of the knee joint with high diagnostic performance and good intraobserver agreement for detecting cartilage degeneration and acute cartilage injury. © RSNA, 2018 Online supplemental material is available for this article .
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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.