ArticleArthritis research & therapy2021
A deep learning method for predicting knee osteoarthritis radiographic progression from MRI.
Article in Arthritis research & therapy, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
35 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
- Evaluation of Pre-Operative Cartilage and Meniscus Ultrashort Echo Time T2* as Biomarkers of Long-Term Clinical Outcomes Following Anterior Cruciate Ligament Reconstruction.Journal of magnetic resonance imaging : JMRI · 2026Article
- Construction of a radiomics-based diagnostic nomogram for patellofemoral osteoarthritis-using lateral knee X-ray images from a South China population.Quantitative imaging in medicine and surgery · 2026Article
- [Advances in deep learning multimodal fusion for early diagnosis of knee osteoarthritis].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Review
- 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
- Review of CNN-Based Approaches for Preprocessing, Segmentation and Classification of Knee Osteoarthritis.Diagnostics (Basel, Switzerland) · 2026Review
- [Differentiation of osteoarthritis phenotypes on MRI using artificial intelligence].Orthopadie (Heidelberg, Germany) · 2026Review
- Robust disease prognosis via diagnostic knowledge preservation: A sequential learning approach.PloS one · 2026Article
- Structured MOAKS-based MRI features for predicting medial joint space narrowing progression: a comparative osteoarthritis initiative study.Frontiers in physiology · 2026Article
- Deep learning-based diagnosis of temporomandibular joint osteoarthritis using whole-body bone scans.iScience · 2025Article
- Leveraging data to transform surgical outcomes and precision in orthopaedics.Journal of clinical orthopaedics and trauma · 2025Review
- Robust Disease Prognosis via Diagnostic Knowledge Preservation: A Sequential Learning Approach.medRxiv : the preprint server for health sciences · 2025Article
- Machine learning models for clinical and structural knee osteoarthritis prediction: Recent advancements and future directions.Osteoarthritis and cartilage open · 2025Article
- Deep learning based classification of tibio-femoral knee osteoarthritis from lateral view knee joint X-ray images.Scientific reports · 2025Article
- Intelligent Manufacturing for Osteoarthritis Organoids.Cell proliferation · 2025Review
- A comparative analysis of sagittal, coronal, and axial magnetic resonance imaging planes in diagnosing anterior cruciate ligament and meniscal tears via a deep learning model: emphasizing the unexpected importance of the axial plane.Quantitative imaging in medicine and surgery · 2025Article
- Knee Osteoarthritis SCAENet: Adaptive Knee Osteoarthritis Severity Assessment Using Spatial Separable Convolution with Attention-Based Ensemble Networks with Hybrid Optimization Strategy.Journal of imaging informatics in medicine · 2025Article
- Next-Level Prediction of Structural Progression in Knee Osteoarthritis: A Perspective.International journal of molecular sciences · 2025Review
- Personalized Predictions for Changes in Knee Pain Among Patients With Osteoarthritis Participating in Supervised Exercise and Education: Prognostic Model Study.JMIR rehabilitation and assistive technologies · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe identification of patients with knee osteoarthritis (OA) likely to progress rapidly in terms of structure is critical to facilitate the development of disease-modifying drugs.
methodsUsing 9280 knee magnetic resonance (MR) images (3268 patients) from the Osteoarthritis Initiative (OAI) database , we implemented a deep learning method to predict, from MR images and clinical variables including body mass index (BMI), further cartilage degradation measured by joint space narrowing at 12 months.
resultsUsing COR IW TSE images, our classification model achieved a ROC AUC score of 65%. On a similar task, trained radiologists obtained a ROC AUC score of 58.7% highlighting the difficulty of the classification task. Additional analyses conducted in parallel to predict pain grade evaluated by the WOMAC pain index achieved a ROC AUC score of 72%. Attention maps provided evidence for distinct specific areas as being relevant in those two predictive models, including the medial joint space for JSN progression and the intra-articular space for pain prediction.
conclusionsThis feasibility study demonstrates the interest of deep learning applied to OA, with a potential to support even trained radiologists in the challenging task of identifying patients with a high-risk of disease progression.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.