ArticleTherapeutic advances in musculoskeletal disease2020
Identification of the most important features of knee osteoarthritis structural progressors using machine learning methods.
Article in Therapeutic advances in musculoskeletal disease, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Predictive Value of Machine Learning in Knee Osteoarthritis Progression: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Smartphone-Based Physical Performance and Multidimensional Determinants of Self-Reported Knee Pain in Community-Dwelling Older Adults: Cross-Sectional Machine Learning and Network Analysis Study.JMIR mHealth and uHealth · 2026Article
- Acute and chronic injury of the knee articular cartilage: prevalence, injury mechanisms, MRI assessment and association with osteoarthritis.Skeletal radiology · 2026Review
- AI-based knee osteoarthritis progression prediction: a comprehensive global bibliometric and hotspot evolution analysis (2010-2025).Knee surgery & related research · 2026Article
- Machine learning models for clinical and structural knee osteoarthritis prediction: Recent advancements and future directions.Osteoarthritis and cartilage open · 2025Article
- Predictive validity of consensus-based MRI definition of osteoarthritis plus radiographic osteoarthritis for the progression of knee osteoarthritis: A longitudinal cohort study.Osteoarthritis and cartilage open · 2025Article
- Next-Level Prediction of Structural Progression in Knee Osteoarthritis: A Perspective.International journal of molecular sciences · 2025Review
- Article
- Gaussian Aquila optimizer based dual convolutional neural networks for identification and grading of osteoarthritis using knee joint images.Scientific reports · 2024Article
- Machine Learning Models for Prediction of Progression of Knee Osteoarthritis: A Comprehensive Analysis.Journal of pharmacy & bioallied sciences · 2024Article
- Articular Cartilage-From Basic Science Structural Imaging to Non-Invasive Clinical Quantitative Molecular Functional Information for AI Classification and Prediction.International journal of molecular sciences · 2023Review
- Automated Prediction of Osteoarthritis Level in Human Osteochondral Tissue Using Histopathological Images.Bioengineering (Basel, Switzerland) · 2023Article
- Patterns of variation among baseline femoral and tibial cartilage thickness and clinical features: Data from the osteoarthritis initiative.Osteoarthritis and cartilage open · 2023Article
- Article
- A Machine Learning Model to Predict Knee Osteoarthritis Cartilage Volume Changes over Time Using Baseline Bone Curvature.Biomedicines · 2022Article
- Explainable machine learning for knee osteoarthritis diagnosis based on a novel fuzzy feature selection methodology.Physical and engineering sciences in medicine · 2022Article
- AI MSK clinical applications: cartilage and osteoarthritis.Skeletal radiology · 2022Review
- Machine learning to predict incident radiographic knee osteoarthritis over 8 Years using combined MR imaging features, demographics, and clinical factors: data from the Osteoarthritis Initiative.Osteoarthritis and cartilage · 2022Article
- Biclustering reveals potential knee OA phenotypes in exploratory analyses: Data from the Osteoarthritis Initiative.PloS one · 2022Article
- Identifying Robust Risk Factors for Knee Osteoarthritis Progression: An Evolutionary Machine Learning Approach.Healthcare (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThe aim was to identify the most important features of structural knee osteoarthritis (OA) progressors and classification using machine learning methods.
methodsParticipants, features and outcomes were from the Osteoarthritis Initiative. Features were from baseline (1107), including articular knee tissues (135) assessed by quantitative magnetic resonance imaging (MRI). OA progressors were ascertained by four outcomes: cartilage volume loss in medial plateau at 48 and 96 months (Prop_CV_48M, 96M), Kellgren-Lawrence (KL) grade ⩾ 2 and medial joint space narrowing (JSN) ⩾ 1 at 48 months. Six feature selection models were used to identify the common features in each outcome. Six classification methods were applied to measure the accuracy of the selected features in classifying the subjects into progressors and non-progressors. Classification of the best features was done using an automatic machine learning interface and the area under the curve (AUC). To prioritize the top five features, sparse partial least square (sPLS) method was used.
resultsFor the classification of the best common features in each outcome, Multi-Layer Perceptron (MLP) achieved the highest AUC in Prop_CV_96M, KL and JSN (0.80, 0.88, 0.95), and Gradient Boosting Machine for Prop_CV_48M (0.70). sPLS showed the baseline top five features to predict knee OA progressors are the joint space width, mean cartilage thickness of the medial tibial plateau and sub-regions and JSN.
conclusionIn this comprehensive study using a large number of features (
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