ArticleOsteoarthritis and cartilage open2021
A machine learning-based approach to decipher multi-etiology of knee osteoarthritis onset and deterioration.
Article in Osteoarthritis and cartilage open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 38 citations in OpenAlex.
- Predictive Value of Machine Learning in Knee Osteoarthritis Progression: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Beyond the algorithms: Challenges in predicting knee osteoarthritis pain from a machine learning point of view.Osteoarthritis imaging · 2026Article
- Novel algorithm for knee localization and diagnosis and grading of knee osteoarthritis based on a priori information: data from OAI.BMC medical imaging · 2026Article
- Combined therapeutic effects of Renshenguben and swimming on knee osteoarthritis associated with the gut-joint axis and HDAC3/PPAR-Frontiers in microbiology · 2026Article
- Next-generation osteoarthritis models: integrating biological, computational, and engineering approaches.Stem cell research & therapy · 2025Review
- Dual-energy computed tomography-based radiomics for differentiating patients with and without gout flares.Clinical rheumatology · 2024Article
- Article
- Assessing the Value of Imaging Data in Machine Learning Models to Predict Patient-Reported Outcome Measures in Knee Osteoarthritis Patients.Bioengineering (Basel, Switzerland) · 2024Article
- Gaussian Aquila optimizer based dual convolutional neural networks for identification and grading of osteoarthritis using knee joint images.Scientific reports · 2024Article
- Radiomics signature of osteoarthritis: Current status and perspective.Journal of orthopaedic translation · 2024Review
- Meniscus injury prediction model based on metric learning.PeerJ. Computer science · 2024Article
- Investigating artificial intelligence models for predicting joint pain from serum biochemistry.Revista da Associacao Medica Brasileira (1992) · 2024Article
- Improved Prediction of Knee Osteoarthritis by the Machine Learning Model XGBoost.Indian journal of orthopaedics · 2023Article
- From Admission to Discharge: Predicting National Institutes of Health Stroke Scale Progression in Stroke Patients Using Biomarkers and Explainable Machine Learning.Journal of personalized medicine · 2023Article
- Evaluation of Blood Biomarkers and Parameters for the Prediction of Stroke Survivors' Functional Outcome upon Discharge Utilizing Explainable Machine Learning.Diagnostics (Basel, Switzerland) · 2023Article
- Adversarial Evolving Neural Network for Longitudinal Knee Osteoarthritis Prediction.IEEE transactions on medical imaging · 2022Article
- An Explainable Machine Learning Pipeline for Stroke Prediction on Imbalanced Data.Diagnostics (Basel, Switzerland) · 2022Article
- Recognition of Knee Osteoarthritis (KOA) Using YOLOv2 and Classification Based on Convolutional Neural Network.Life (Basel, Switzerland) · 2022Article
- Discovering Knee Osteoarthritis Imaging Features for Diagnosis and Prognosis: Review of Manual Imaging Grading and Machine Learning Approaches.Journal of healthcare engineering · 2022Review
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: By deploying a novel combination of machine learning approaches, we aim to investigate the contributions of each local and systemic risk factors in multi-etiology of knee osteoarthritis (KOA) to disease onset and deterioration. Methods: A machine-learning-based KOA progression prediction model is developed using the data from the National Institute of Health Osteoarthritis Biomarkers Consortium. According to Kellgren-Lawrence (KL) grade of plain radiographs at baseline, the subjects are divided into either KOA onset or deterioration study groups. The disease progression is defined as the changes in both joint space width (JSW) and WOMAC pain score. In addition to radiographic and symptomatic data, the anthropological particulars, history of the knee injury and surgery, metabolic syndrome and living habits were deployed in a multi-layer perceptron (MLP) to predict disease progression in each study group. The relative contributions of each risk factors were weighted via DeepLIFT gradient. Additionally, statistical interactions among risk factors were identified compared. Results: Our model achieved AUC of 0.843 (95% CI 0.824, 0.862) and 0.765 (95% CI 0.756, 0.774) in prediction of KOA onset and deterioration, respectively. For KOA onset prediction, history of injury has attained the highest DeepLIFT gradient except medial joint space narrowing; while for KOA deterioration prediction, diabetes and habit of smoking obtained second and third highest gradients respectively aside from medial joint space narrowing, surpassing the impact of the injury. Conclusion: We developed a machine learning workflow which effectively dissects the risk factors' contributions and their mutual interactions for onset and deterioration of KOA respectively.
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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.