ArticleScientific reports2021
Deep learning for large scale MRI-based morphological phenotyping of osteoarthritis.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Evaluation of Pre-Operative Cartilage and Meniscus Ultrashort Echo Time T2* as Biomarkers of Long-Term Clinical Outcomes Following Anterior Cruciate Ligament Reconstruction.Journal of magnetic resonance imaging : JMRI · 2026Article
- Are magnetic resonance imaging features associated with intermittent and constant pain in knee osteoarthritis? A cross-sectional study.Osteoarthritis and cartilage open · 2026Article
- From undruggable to degradable: A deep learning-enabled framework for precision orthopaedic protein degradation.Journal of orthopaedic translation · 2026Review
- Tissue and extracellular matrix remodeling of the subchondral bone during osteoarthritis of knee joints as revealed by spatial mass spectrometry imaging.Bone research · 2026Article
- Bibliometric analysis of the application of artificial intelligence in orthopedic imaging.Quantitative imaging in medicine and surgery · 2025Article
- Phenotypic variations in knee osteoarthritis: insights from MRI and radiographic comparisons.Skeletal radiology · 2025Article
- Advancing osteoarthritis research: the role of AI in clinical, imaging and omics fields.Bone research · 2025Review
- Deep learning analysis for rheumatologic imaging: current trends, future directions, and the role of human.Journal of rheumatic diseases · 2025Review
- ShapeMed-Knee: A Dataset and Neural Shape Model Benchmark for Modeling 3D Femurs.IEEE transactions on medical imaging · 2025Article
- ShapeMed-Knee: A Dataset and Neural Shape Model Benchmark for Modeling 3D Femurs.medRxiv : the preprint server for health sciences · 2024Article
- Deep learning in rheumatological image interpretation.Nature reviews. Rheumatology · 2024Review
- Unsupervised domain adaptation for automated knee osteoarthritis phenotype classification.Quantitative imaging in medicine and surgery · 2023Article
- Comparison of evaluation metrics of deep learning for imbalanced imaging data in osteoarthritis studies.Osteoarthritis and cartilage · 2023Article
- FDA/Arthritis Foundation osteoarthritis drug development workshop recap: Assessment of long-term benefit.Seminars in arthritis and rheumatism · 2022Review
- Studying osteoarthritis with artificial intelligence applied to magnetic resonance imaging.Nature reviews. Rheumatology · 2022Review
- Latest advancements in imaging techniques in OA.Therapeutic advances in musculoskeletal disease · 2022Review
- The Challenges of Telemedicine in Rheumatology.Frontiers in medicine · 2021Review
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Osteoarthritis (OA) develops through heterogenous pathophysiologic pathways. As a result, no regulatory agency approved disease modifying OA drugs are available to date. Stratifying knees into MRI-based morphological phenotypes may provide insight into predicting future OA incidence, leading to improved inclusion criteria and efficacy of therapeutics. We trained convolutional neural networks to classify bone, meniscus/cartilage, inflammatory, and hypertrophy phenotypes in knee MRIs from participants in the Osteoarthritis Initiative (n = 4791). We investigated cross-sectional association between baseline morphological phenotypes and baseline structural OA (Kellgren Lawrence grade > 1) and symptomatic OA. Among participants without baseline OA, we evaluated association of baseline phenotypes with 48-month incidence of structural OA and symptomatic OA. The area under the curve of bone, meniscus/cartilage, inflammatory, and hypertrophy phenotype neural network classifiers was 0.89 ± 0.01, 0.93 ± 0.03, 0.96 ± 0.02, and 0.93 ± 0.02, respectively (mean ± standard deviation). Among those with no baseline OA, bone phenotype (OR: 2.99 (95%CI: 1.59-5.62)) and hypertrophy phenotype (OR: 5.80 (95%CI: 1.82-18.5)) each respectively increased odds of developing incident structural OA and symptomatic OA at 48 months. All phenotypes except meniscus/cartilage increased odds of undergoing total knee replacement within 96 months. Artificial intelligence can rapidly stratify knees into structural phenotypes associated with incident OA and total knee replacement, which may aid in stratifying patients for clinical trials of targeted therapeutics.
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