ArticleJournal of magnetic resonance imaging : JMRI2019
3D convolutional neural networks for detection and severity staging of meniscus and PFJ cartilage morphological degenerative changes in osteoarthritis and anterior cruciate ligament subjects.
Article in Journal of magnetic resonance imaging : JMRI, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 papers, 5 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
68 citing papers in PubMed, 5 syntheses or guidelines pooled it, 163 citations in OpenAlex.
- MRI deep learning models for assisted diagnosis of knee pathologies: a systematic review.European radiology · 2025Pooled it
- Diagnosis of knee meniscal injuries using artificial intelligence: A systematic review and meta-analysis of diagnostic performance.PloS one · 2025Pooled it
- Artificial intelligence applied to magnetic resonance imaging reliably detects the presence, but not the location, of meniscus tears: a systematic review and meta-analysis.European radiology · 2024Pooled it
- Systematic review of artificial intelligence development and evaluation for MRI diagnosis of knee ligament or meniscus tears.Skeletal radiology · 2024Pooled it
- Pooled it
- Longitudinal 3D quantification of joint space from CT images: Semi-automatic segmentation and feasibility of nnU-Net automation.Osteoarthritis imaging · 2026Article
- AI-based knee osteoarthritis progression prediction: a comprehensive global bibliometric and hotspot evolution analysis (2010-2025).Knee surgery & related research · 2026Article
- Role of Artificial Intelligence and Machine Learning in Diagnosing Knee Lesions: Where Are We Now?Cureus · 2026Review
- Progress in machine learning-assisted medical imaging for osteoarthritis and osteoporosis diagnosis: a narrative review.Quantitative imaging in medicine and surgery · 2026Review
- [Differentiation of osteoarthritis phenotypes on MRI using artificial intelligence].Orthopadie (Heidelberg, Germany) · 2026Review
- Artificial Intelligence Has Varied Diagnostic and Predictive Performance in Diagnosing Patellofemoral Osteoarthritis, Trochlear Dysplasia, and Patellofemoral Tracking Abnormalities: A Systematic Review.Arthroscopy, sports medicine, and rehabilitation · 2025Review
- Dual-center study on AI-driven multi-label deep learning for X-ray screening of knee abnormalities.Scientific reports · 2025Article
- Review
- Artificial intelligence in orthopedics: fundamentals, current applications, and future perspectives.Military Medical Research · 2025Review
- Deep learning-assisted detection of meniscus and anterior cruciate ligament combined tears in adult knee magnetic resonance imaging: a crossover study with arthroscopy correlation.International orthopaedics · 2025Article
- Deep learning analysis for rheumatologic imaging: current trends, future directions, and the role of human.Journal of rheumatic diseases · 2025Review
- Achieving high accuracy in meniscus tear detection using advanced deep learning models with a relatively small data set.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2025Article
- Hierarchical classification of anterior cruciate ligament using deep learning for athletes healthcare.BioImpacts : BI · 2025Article
- Enhancing knee osteoarthritis detection with AI, image denoising, and optimized classification methods and the importance of physical therapy methods.PeerJ. Computer science · 2025Article
- Article
8 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundSemiquantitative assessment of MRI plays a central role in musculoskeletal research; however, in the clinical setting MRI reports often tend to be subjective and qualitative. Grading schemes utilized in research are not used because they are extraordinarily time-consuming and unfeasible in clinical practice. PURPOSE: To evaluate the ability of deep-learning models to detect and stage severity of meniscus and patellofemoral cartilage lesions in osteoarthritis and anterior cruciate ligament (ACL) subjects. STUDY TYPE: Retrospective study aimed to evaluate a technical development. POPULATION: In all, 1478 MRI studies, including subjects at various stages of osteoarthritis and after ACL injury and reconstruction. FIELD STRENGTH/SEQUENCE: 3T MRI, 3D FSE CUBE. ASSESSMENT: Automatic segmentation of cartilage and meniscus using 2D U-Net, automatic detection, and severity staging of meniscus and cartilage lesion with a 3D convolutional neural network (3D-CNN). STATISTICAL TESTS: Receiver operating characteristic (ROC) curve, specificity and sensitivity, and class accuracy.
resultsSensitivity of 89.81% and specificity of 81.98% for meniscus lesion detection and sensitivity of 80.0% and specificity of 80.27% for cartilage were achieved. The best performances for staging lesion severity were obtained by including demographics factors, achieving accuracies of 80.74%, 78.02%, and 75.00% for normal, small, and complex large lesions, respectively. DATA
conclusionIn this study we provide a proof of concept of a fully automated deep-learning pipeline that can identify the presence of meniscal and patellar cartilage lesions. This pipeline has also shown potential in making more in-depth examinations of lesion subjects for multiclass prediction and severity staging. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:400-410.
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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.