ArticleOsteoarthritis and cartilage2023
The KNee OsteoArthritis Prediction (KNOAP2020) challenge: An image analysis challenge to predict incident symptomatic radiographic knee osteoarthritis from MRI and X-ray images.
Article in Osteoarthritis and cartilage, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it, 56 citations in OpenAlex.
- Systematic review of artificial intelligence competitions in radiology: a focus on design, evaluation, and trends.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Pooled it
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- Predicting Knee osteoarthritis progression using explainable machine learning and clinical imaging data.Osteoarthritis and cartilage open · 2026Article
- Beyond the algorithms: Challenges in predicting knee osteoarthritis pain from a machine learning point of view.Osteoarthritis imaging · 2026Article
- Baseline 3D joint space width on weight-bearing CT in comparison with 2D JSWOsteoarthritis imaging · 2026Article
- Accurate classification and prediction of knee osteoarthritis based on Al-Biruni Earth Radius metaheuristic optimizer and LSTM classifier.Scientific reports · 2026Article
- Usefulness of 3D joint space width on weight-bearing CT in comparison with 2D joint space width on radiographs for predicting 24-month worsening of knee osteoarthritis pain and function in the MOST study.Osteoarthritis and cartilage · 2026Article
- Interleukin-6 as a Marker of Joint Damage in Osteoarthritis: A Study of Its Pro-inflammatory Impacts.Clinical medicine insights. Arthritis and musculoskeletal disorders · 2026Article
- Peripheral blood DNA methylation changes are predictive of incident radiographic osteoarthritis development: Data and biospecimens from the osteoarthritis initiative.Osteoarthritis and cartilage open · 2025Article
- Diffusion model-empowered patella shape analysis predicts knee osteoarthritis outcomes.Osteoarthritis and cartilage open · 2025Article
- Review
- Osteoarthritis: Mechanisms and Therapeutic Advances.MedComm · 2025Review
- Estimation of time-to-total knee replacement surgery with multimodal modeling and artificial intelligence.Computers in biology and medicine · 2025Article
- Article
- Artificial intelligence in musculoskeletal applications: a primer for radiologists.Diagnostic and interventional radiology (Ankara, Turkey) · 2025Review
- ShapeMed-Knee: A Dataset and Neural Shape Model Benchmark for Modeling 3D Femurs.IEEE transactions on medical imaging · 2025Article
- Foundations of a knee joint digital twin from qMRI biomarkers for osteoarthritis and knee replacement.NPJ digital medicine · 2025Article
- Two-Stage Classification of Future Knee Osteoarthritis Severity After 8 Years Using MRI: Data from the Osteoarthritis Initiative.Annals of biomedical engineering · 2024Article
- ShapeMed-Knee: A Dataset and Neural Shape Model Benchmark for Modeling 3D Femurs.medRxiv : the preprint server for health sciences · 2024Article
- Biomechanical modeling and imaging for knee osteoarthritis - is there a role for AI?Osteoarthritis imaging · 2024Article
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Authors and funding
32 authors at 8 institutions in 4 countries.
Funding
Abstract
objectivesThe KNee OsteoArthritis Prediction (KNOAP2020) challenge was organized to objectively compare methods for the prediction of incident symptomatic radiographic knee osteoarthritis within 78 months on a test set with blinded ground truth.
designThe challenge participants were free to use any available data sources to train their models. A test set of 423 knees from the Prevention of Knee Osteoarthritis in Overweight Females (PROOF) study consisting of magnetic resonance imaging (MRI) and X-ray image data along with clinical risk factors at baseline was made available to all challenge participants. The ground truth outcomes, i.e., which knees developed incident symptomatic radiographic knee osteoarthritis (according to the combined ACR criteria) within 78 months, were not provided to the participants. To assess the performance of the submitted models, we used the area under the receiver operating characteristic curve (ROCAUC) and balanced accuracy (BACC).
resultsSeven teams submitted 23 entries in total. A majority of the algorithms were trained on data from the Osteoarthritis Initiative. The model with the highest ROCAUC (0.64 (95% confidence interval (CI): 0.57-0.70)) used deep learning to extract information from X-ray images combined with clinical variables. The model with the highest BACC (0.59 (95% CI: 0.52-0.65)) ensembled three different models that used automatically extracted X-ray and MRI features along with clinical variables.
conclusionThe KNOAP2020 challenge established a benchmark for predicting incident symptomatic radiographic knee osteoarthritis. Accurate prediction of incident symptomatic radiographic knee osteoarthritis is a complex and still unsolved problem requiring additional investigation.
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