ArticleOsteoarthritis and cartilage2020
Deep learning risk assessment models for predicting progression of radiographic medial joint space loss over a 48-MONTH follow-up period.
Article in Osteoarthritis and cartilage, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 2 of them syntheses that pooled it.
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Who cites it
29 citing papers in PubMed, 2 syntheses or guidelines pooled it, 65 citations in OpenAlex.
- Effectiveness of manual therapy targeting hamstring tightness in knee osteoarthritis: a systematic review of rehabilitation outcomes.Frontiers in medicine · 2026Pooled it
- Deep learning in knee imaging: a systematic review utilizing a Checklist for Artificial Intelligence in Medical Imaging (CLAIM).European radiology · 2022Pooled it
- Nomograms Based on X-Ray Radiomics for Predicting Pain Progression in Knee Osteoarthritis Using Data From the Foundation for the National Institutes of Health: Development and Validation Study.JMIR medical informatics · 2026Article
- Simplifying Knee OA Prognosis: A Deep Learning Approach Using Radiographs and Minimal Clinical Inputs.Diagnostics (Basel, Switzerland) · 2025Article
- Deep learning classification models demonstrate high accuracy and clinical potential in radiograph interpretation in the arthroplasty clinical pathway: A systematic review and meta-analysis.Journal of experimental orthopaedics · 2025Review
- A metaheuristic optimization-based approach for accurate prediction and classification of knee osteoarthritis.Scientific reports · 2025Article
- Biomechanical modeling and imaging for knee osteoarthritis - is there a role for AI?Osteoarthritis imaging · 2024Article
- Gaussian Aquila optimizer based dual convolutional neural networks for identification and grading of osteoarthritis using knee joint images.Scientific reports · 2024Article
- Contralateral knee osteoarthritis is a risk factor for ipsilateral knee osteoarthritis progressing: a case control study.BMC musculoskeletal disorders · 2024Article
- Artificial intelligence in planned orthopaedic care.SICOT-J · 2024Article
- Clinically-Inspired Multi-Agent Transformers for Disease Trajectory Forecasting From Multimodal Data.IEEE transactions on medical imaging · 2024Article
- Machine Learning Approaches to the Prediction of Osteoarthritis Phenotypes and Outcomes.Current rheumatology reports · 2023Review
- Deep learning applications in osteoarthritis imaging.Skeletal radiology · 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
- Deep learning-assisted knee osteoarthritis automatic grading on plain radiographs: the value of multiview X-ray images and prior knowledge.Quantitative imaging in medicine and surgery · 2023Article
- A joint multi-modal learning method for early-stage knee osteoarthritis disease classification.Heliyon · 2023Article
- Radiographic Biomarkers for Knee Osteoarthritis: A Narrative Review.Life (Basel, Switzerland) · 2023Review
- The KNee OsteoArthritis Prediction (KNOAP2020) challenge: An image analysis challenge to predict incident symptomatic radiographic knee osteoarthritis from MRI and X-ray images.Osteoarthritis and cartilage · 2023Article
- Automatic assessment of knee osteoarthritis severity in portable devices based on deep learning.Journal of orthopaedic surgery and research · 2022Article
- Adversarial Evolving Neural Network for Longitudinal Knee Osteoarthritis Prediction.IEEE transactions on medical imaging · 2022Article
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 1 country.
Funding
Abstract
objectiveTo develop and evaluate deep learning (DL) risk assessment models for predicting the progression of radiographic medial joint space loss using baseline knee X-rays.
methodsKnees from the Osteoarthritis Initiative without and with progression of radiographic joint space loss (defined as ≥ 0.7 mm decrease in medial joint space width measurement between baseline and 48-month follow-up X-rays) were randomly stratified into training (1400 knees) and hold-out testing (400 knees) datasets. A DL network was trained to predict the progression of radiographic joint space loss using the baseline knee X-rays. An artificial neural network was used to develop a traditional model for predicting progression utilizing demographic and radiographic risk factors. A combined joint training model was developed using a DL network to extract information from baseline knee X-rays as a feature vector, which was further concatenated with the risk factor data vector. Area under the curve (AUC) analysis was performed using the hold-out test dataset to evaluate model performance.
resultsThe traditional model had an AUC of 0.660 (61.5% sensitivity and 64.0% specificity) for predicting progression. The DL model had an AUC of 0.799 (78.0% sensitivity and 75.5% specificity), which was significantly higher (P < 0.001) than the traditional model. The combined model had an AUC of 0.863 (80.5% sensitivity and specificity), which was significantly higher than the DL (P = 0.015) and traditional (P < 0.001) models.
conclusionDL models using baseline knee X-rays had higher diagnostic performance for predicting the progression of radiographic joint space loss than the traditional model using demographic and radiographic risk factors.
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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.