ArticleScientific reports2021
Interpretable and parameter optimized ensemble model for knee osteoarthritis assessment using radiographs.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Localizing Knee Pain via Explainable Bayesian Generative Models and Counterfactual MRI: Data from the Osteoarthritis Initiative.Journal of imaging informatics in medicine · 2026Article
- Review of CNN-Based Approaches for Preprocessing, Segmentation and Classification of Knee Osteoarthritis.Diagnostics (Basel, Switzerland) · 2026Review
- Blood-based DNA methylation markers for autism spectrum disorder identification using machine learning.Epigenomics · 2025Article
- A Review for automated classification of knee osteoarthritis using KL grading scheme for X-rays.Biomedical engineering letters · 2025Review
- CDK: A novel high-performance transfer feature technique for early detection of osteoarthritis.Journal of pathology informatics · 2024Article
- Accurate, automated classification of radiographic knee osteoarthritis severity using a novel method of deep learning: Plug-in modules.Knee surgery & related research · 2024Article
- A Retrospective Study of Biological Risk Factors Associated with Primary Knee Osteoarthritis and the Development of a Nomogram Model.International journal of general medicine · 2024Article
- Article
- Improved Prediction of Knee Osteoarthritis by the Machine Learning Model XGBoost.Indian journal of orthopaedics · 2023Article
- E pluribus unum interpretable convolutional neural networks.Scientific reports · 2023Article
- A Simple Nomogram for Predicting Osteoarthritis Severity in Patients with Knee Osteoarthritis.Computational and mathematical methods in medicine · 2022Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Knee osteoarthritis (KOA) is an orthopedic disorder with a substantial impact on mobility and quality of life. An accurate assessment of the KOA levels is imperative in prioritizing meaningful patient care. Quantifying osteoarthritis features such as osteophytes and joint space narrowing (JSN) from low-resolution images (i.e., X-ray images) are mostly subjective. We implement an objective assessment and quantification of KOA to aid practitioners. In particular, we developed an interpretable ensemble of convolutional neural network (CNN) models consisting of three modules. First, we developed a scale-invariant and aspect ratio preserving model to localize Knee joints. Second, we created multiple instances of "hyperparameter optimized" CNN models with diversity and build an ensemble scoring system to assess the severity of KOA according to the Kellgren-Lawrence grading (KL) scale. Third, we provided visual explanations of the predictions by the ensemble model. We tested our models using a collection of 37,996 Knee joints from the Osteoarthritis Initiative (OAI) dataset. Our results show a superior (13-27%) performance improvement compared to the state-of-the-art methods.
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Registered trials
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