ArticleScientific reports2020
Multi-classifier prediction of knee osteoarthritis progression from incomplete imbalanced longitudinal data.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
36 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Structural tissue damage and 24-month progression of semi-quantitative MRI biomarkers of knee osteoarthritis in the IMI-APPROACH cohort.BMC musculoskeletal disorders · 2022Trial
- 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
- AI-based knee osteoarthritis progression prediction: a comprehensive global bibliometric and hotspot evolution analysis (2010-2025).Knee surgery & related research · 2026Article
- Redox-Related Genetic and Biological Ageing Signals in Rapid Pain Progression of Knee Osteoarthritis: A Hypothesis-Generating Analysis in the Osteoarthritis Initiative.Antioxidants (Basel, Switzerland) · 2026Article
- Machine learning predicts clinical response to platelet-rich plasma therapy in knee osteoarthritis.Frontiers in medicine · 2026Article
- Predicting Knee Osteoarthritis Severity from Radiographic Predictors: Data from the Osteoarthritis Initiative.Annals of biomedical engineering · 2025Article
- Dual-energy computed tomography-based radiomics for differentiating patients with and without gout flares.Clinical rheumatology · 2024Article
- Two-Stage Classification of Future Knee Osteoarthritis Severity After 8 Years Using MRI: Data from the Osteoarthritis Initiative.Annals of biomedical engineering · 2024Article
- Assessing the Value of Imaging Data in Machine Learning Models to Predict Patient-Reported Outcome Measures in Knee Osteoarthritis Patients.Bioengineering (Basel, Switzerland) · 2024Article
- Performance of Radiological and Biochemical Biomarkers in Predicting Radio-Symptomatic Knee Osteoarthritis Progression.Biomedicines · 2024Article
- Clinically-Inspired Multi-Agent Transformers for Disease Trajectory Forecasting From Multimodal Data.IEEE transactions on medical imaging · 2024Article
- Short-term variations in trabecular bone texture parameters associated to radio-clinical biomarkers improve the prediction of radiographic knee osteoarthritis progression.Scientific reports · 2023Article
- Development and validation of a machine learning-supported strategy of patient selection for osteoarthritis clinical trials: the IMI-APPROACH study.Osteoarthritis and cartilage open · 2023Article
- Machine Learning Approaches to the Prediction of Osteoarthritis Phenotypes and Outcomes.Current rheumatology reports · 2023Review
- DeepKOA: a deep-learning model for predicting progression in knee osteoarthritis using multimodal magnetic resonance images from the osteoarthritis initiative.Quantitative imaging in medicine and surgery · 2023Article
- Predicted and actual 2-year structural and pain progression in the IMI-APPROACH knee osteoarthritis cohort.Rheumatology (Oxford, England) · 2022Article
- Forecasting individual progression trajectories in Huntington disease enables more powered clinical trials.Scientific reports · 2022Article
- Narrative Review of Machine Learning in Rheumatic and Musculoskeletal Diseases for Clinicians and Researchers: Biases, Goals, and Future Directions.The Journal of rheumatology · 2022Review
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Conventional inclusion criteria used in osteoarthritis clinical trials are not very effective in selecting patients who would benefit from a therapy being tested. Typically majority of selected patients show no or limited disease progression during a trial period. As a consequence, the effect of the tested treatment cannot be observed, and the efforts and resources invested in running the trial are not rewarded. This could be avoided, if selection criteria were more predictive of the future disease progression. In this article, we formulated the patient selection problem as a multi-class classification task, with classes based on clinically relevant measures of progression (over a time scale typical for clinical trials). Using data from two long-term knee osteoarthritis studies OAI and CHECK, we tested multiple algorithms and learning process configurations (including multi-classifier approaches, cost-sensitive learning, and feature selection), to identify the best performing machine learning models. We examined the behaviour of the best models, with respect to prediction errors and the impact of used features, to confirm their clinical relevance. We found that the model-based selection outperforms the conventional inclusion criteria, reducing by 20-25% the number of patients who show no progression. This result might lead to more efficient clinical trials.
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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.