ArticleSkeletal radiology2022
Deep learning approach to predict pain progression in knee osteoarthritis.
Article in Skeletal radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 2 of them syntheses that pooled it.
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Who cites it
42 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Predictive Value of Machine Learning in Knee Osteoarthritis Progression: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- The value of deep learning-based X-ray techniques in detecting and classifying K-L grades of knee osteoarthritis: a systematic review and meta-analysis.European radiology · 2025Pooled it
- 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
- Beyond the algorithms: Challenges in predicting knee osteoarthritis pain from a machine learning point of view.Osteoarthritis imaging · 2026Article
- Who Is the Surgeon Now: Human Hands or Machine Minds? Artificial Intelligence in Orthopedics from Diagnosis to Follow-Up-A Structured Narrative Review.Journal of clinical medicine · 2026Review
- Usefulness of 3D joint space width on weight-bearing CT in comparison with 2D joint space width on radiographs for predicting 24-month worsening of knee osteoarthritis pain and function in the MOST study.Osteoarthritis and cartilage · 2026Article
- 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
- Predicting Long-Term Pain Resilience in Knee Osteoarthritis: An Osteoarthritis Initiative Nomogram.Bioengineering (Basel, Switzerland) · 2026Article
- From structural recognition to mechanistic forecasting: repositioning artificial intelligence in osteoarthritis prevention.Frontiers in immunology · 2026Article
- Early diagnosis of knee osteoarthritis severity using vision transformer.BMC musculoskeletal disorders · 2025Article
- TurkerNeXtV2: An Innovative CNN Model for Knee Osteoarthritis Pressure Image Classification.Diagnostics (Basel, Switzerland) · 2025Article
- Machine learning models for clinical and structural knee osteoarthritis prediction: Recent advancements and future directions.Osteoarthritis and cartilage open · 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
- Pain Assessment in Osteoarthritis: Present Practices and Future Prospects Including the Use of Biomarkers and Wearable Technologies, and AI-Driven Personalized Medicine.Journal of orthopaedic research : official publication of the Orthopaedic Research Society · 2025Review
- AE-BoNet: A Deep Learning Method for Pediatric Bone Age Estimation using an Unsupervised Pre-Trained Model.Journal of biomedical physics & engineering · 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
- Predicting joint space changes in knee osteoarthritis over 6 years: a combined model of TransUNet and XGBoost.Quantitative imaging in medicine and surgery · 2025Article
- Review
- Assessing the Value of Imaging Data in Machine Learning Models to Predict Patient-Reported Outcome Measures in Knee Osteoarthritis Patients.Bioengineering (Basel, Switzerland) · 2024Article
- Survival analysis on subchondral bone length for total knee replacement.Skeletal radiology · 2024Article
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Authors and funding
7 authors.
Funding
Abstract
objectiveTo develop and evaluate deep learning (DL) risk assessment models for predicting pain progression in subjects with or at risk of knee osteoarthritis (OA). MATERIALS AND
methodsThe incidence and progression cohorts of the Osteoarthritis Initiative, a multi-center longitudinal study involving 9348 knees in 4674 subjects with or at risk of knee OA that began in 2004 and is ongoing, were used to conduct this retrospective analysis. A subset of knees without and with pain progression (defined as a 9-point or greater increase in pain score between baseline and two or more follow-up time points over the first 48 months) was randomly stratified into training (4200 knees with a mean age of 61.0 years and 60% female) and hold-out testing (500 knees with a mean age of 60.8 years and 60% female) datasets. A DL model was developed to predict pain progression using baseline knee radiographs. An artificial neural network was used to develop a traditional risk assessment model to predict pain progression using demographic, clinical, and radiographic risk factors. A combined model was developed to combine demographic, clinical, and radiographic risk factors with DL analysis of baseline knee radiographs. Area under the curve (AUC) analysis was performed using the hold-out testing dataset to evaluate model performance.
resultsThe traditional model had an AUC of 0.692 (66.9% sensitivity and 64.1% specificity). The DL model had an AUC of 0.770 (76.7% sensitivity and 70.5% specificity), which was significantly higher (p < 0.001) than the traditional model. The combined model had an AUC of 0.807 (72.3% sensitivity and 80.9% specificity), which was significantly higher (p < 0.05) than the traditional and DL models.
conclusionsDL models using baseline knee radiographs had higher diagnostic performance for predicting pain progression than traditional models using demographic, clinical, and radiographic risk factors.
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Registered trials
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