ArticleArthritis research & therapy2023
Machine learning identification of thresholds to discriminate osteoarthritis and rheumatoid arthritis synovial inflammation.
Article in Arthritis research & therapy, 2023. 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, 33 citations in OpenAlex.
- Synovial transcriptional clusters link cartilage degeneration to cell-type-specific gene expression in knee osteoarthritis.bioRxiv : the preprint server for biology · 2026Article
- Osteoarthritis as a systemic disease.Nature reviews. Rheumatology · 2026Review
- Human Knee as an Organ: Joint Tissue Collection, Processing, and Scoring for Multimodal Analyses.Laboratory investigation; a journal of technical methods and pathology · 2026Review
- From detection to grading: A hybrid KOA-YOLOv5-RF model for knee osteoarthritis diagnosis.MethodsX · 2025Article
- Observational
- Biomarker-Guided Imaging and AI-Augmented Diagnosis of Degenerative Joint Disease.Diagnostics (Basel, Switzerland) · 2025Review
- Advancing osteoarthritis research: the role of AI in clinical, imaging and omics fields.Bone research · 2025Review
- The role of the immune system in osteoarthritis: mechanisms, challenges and future directions.Nature reviews. Rheumatology · 2025Review
- Effectively alleviate rheumatoid arthritis via maintaining redox balance, inducing macrophage repolarization and restoring homeostasis of fibroblast-like synoviocytes by metformin-derived carbon dots.Journal of nanobiotechnology · 2025Article
- CPP-calcification of articular cartilage is associated with elevated cytokine levels in synovial fluid.Frontiers in cell and developmental biology · 2025Article
- Association between neutrophil percentage-to-albumin ratio (NPAR) and sarcopenia in individuals with arthritis: a cross-sectional study.Frontiers in medicine · 2025Article
- Current application, possibilities, and challenges of artificial intelligence in the management of rheumatoid arthritis, axial spondyloarthritis, and psoriatic arthritis.Therapeutic advances in musculoskeletal disease · 2025Review
- Predicting Rheumatoid Arthritis Development Using Hand Ultrasound and Machine Learning-A Two-Year Follow-Up Cohort Study.Diagnostics (Basel, Switzerland) · 2024Article
- The presence of Mott cells in the lymph nodes of rats with experimental autoimmune encephalomyelitis.Histochemistry and cell biology · 2024Article
- Mimicking the Human Articular Joint withInternational journal of stem cells · 2024Article
- Broken-fat pad sign: a characteristic radiographic finding to distinguish between knee rheumatoid arthritis and osteoarthritis.Insights into imaging · 2024Article
- Advancing precision rheumatology: applications of machine learning for rheumatoid arthritis management.Frontiers in immunology · 2024Review
Corrections and comments
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Authors and funding
19 authors at 7 institutions in 1 country.
Funding
Abstract
backgroundWe sought to identify features that distinguish osteoarthritis (OA) and rheumatoid arthritis (RA) hematoxylin and eosin (H&E)-stained synovial tissue samples.
methodsWe compared fourteen pathologist-scored histology features and computer vision-quantified cell density (147 OA and 60 RA patients) in H&E-stained synovial tissue samples from total knee replacement (TKR) explants. A random forest model was trained using disease state (OA vs RA) as a classifier and histology features and/or computer vision-quantified cell density as inputs.
resultsSynovium from OA patients had increased mast cells and fibrosis (p < 0.001), while synovium from RA patients exhibited increased lymphocytic inflammation, lining hyperplasia, neutrophils, detritus, plasma cells, binucleate plasma cells, sub-lining giant cells, fibrin (all p < 0.001), Russell bodies (p = 0.019), and synovial lining giant cells (p = 0.003). Fourteen pathologist-scored features allowed for discrimination between OA and RA, producing a micro-averaged area under the receiver operating curve (micro-AUC) of 0.85±0.06. This discriminatory ability was comparable to that of computer vision cell density alone (micro-AUC = 0.87±0.04). Combining the pathologist scores with the cell density metric improved the discriminatory power of the model (micro-AUC = 0.92±0.06). The optimal cell density threshold to distinguish OA from RA synovium was 3400 cells/mm
conclusionsH&E-stained images of TKR explant synovium can be correctly classified as OA or RA in 82% of samples. Cell density greater than 3400 cells/mm
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Registered trials
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