ArticleScientific reports2020
Deep Learning Predicts Total Knee Replacement from Magnetic Resonance Images.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06190977 (Effects of the Otago Exercise Program on Rehabilitation Outcomes of Balance, Strength and Functional Outcomes in Post-Operative Total Knee Replacement Patients), which is not on this map. Cited by 68 papers, 3 of them syntheses that pooled it.
What it found
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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.
Effects of the Otago Exercise Program on Rehabilitation Outcomes of Balance, Strength and Functional Outcomes in Post-Operative Total Knee Replacement Patients
Who cites it
68 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Accuracy of machine learning in identifying candidates for total knee arthroplasty (TKA) surgery: a systematic review and meta-analysis.European journal of medical research · 2025Pooled it
- Artificial intelligence in total and unicompartmental knee arthroplasty.BMC musculoskeletal disorders · 2024Pooled it
- Pooled it
- Diffusion Tensor Imaging of Knee Cartilage for Detection of Early Osteoarthritis and Prediction of Radiographic Progression.Journal of magnetic resonance imaging : JMRI · 2026Article
- Artificial intelligence in knee osteotomy: evolution of alignment assessment and surgical decision-making.Journal of robotic surgery · 2026Review
- Forecasting knee arthroplasty surgery with deep learning : an integrated approach using routine clinical data and radiographs.Bone & joint open · 2026Article
- Osteoarthritis: Epidemiology, Diagnosis, and Treatment.MedComm · 2026Review
- The relationship between real-time ultrasound-detected extra-articular soft tissue pathologies and knee pain in osteoarthritis: a cross-sectional study.Clinical rheumatology · 2026Article
- Targeting NAV2-mediated Wnt/β-catenin signaling in rheumatoid arthritis: current evidence and emerging therapeutic approaches.Molecular biology reports · 2026Review
- Micro-Strain Responsive Near-Infrared Mechanoluminescence for Potential Nondestructive Artificial Joint Stress Imaging.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Robust disease prognosis via diagnostic knowledge preservation: A sequential learning approach.PloS one · 2026Article
- [Differentiation of osteoarthritis phenotypes on MRI using artificial intelligence].Orthopadie (Heidelberg, Germany) · 2026Review
- Article
- Diffusion model-empowered patella shape analysis predicts knee osteoarthritis outcomes.Osteoarthritis and cartilage open · 2025Article
- 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
- MR-Transformer: A Vision Transformer-based Deep Learning Model for Total Knee Replacement Prediction Using MRI.Radiology. Artificial intelligence · 2025Article
- Predicting Knee Osteoarthritis Severity from Radiographic Predictors: Data from the Osteoarthritis Initiative.Annals of biomedical engineering · 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
- Estimation of time-to-total knee replacement surgery with multimodal modeling and artificial intelligence.Computers in biology and medicine · 2025Article
8 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Knee Osteoarthritis (OA) is a common musculoskeletal disorder in the United States. When diagnosed at early stages, lifestyle interventions such as exercise and weight loss can slow OA progression, but at later stages, only an invasive option is available: total knee replacement (TKR). Though a generally successful procedure, only 2/3 of patients who undergo the procedure report their knees feeling "normal" post-operation, and complications can arise that require revision. This necessitates a model to identify a population at higher risk of TKR, particularly at less advanced stages of OA, such that appropriate treatments can be implemented that slow OA progression and delay TKR. Here, we present a deep learning pipeline that leverages MRI images and clinical and demographic information to predict TKR with AUC 0.834 ± 0.036 (p < 0.05). Most notably, the pipeline predicts TKR with AUC 0.943 ± 0.057 (p < 0.05) for patients without OA. Furthermore, we develop occlusion maps for case-control pairs in test data and compare regions used by the model in both, thereby identifying TKR imaging biomarkers. As such, this work takes strides towards a pipeline with clinical utility, and the biomarkers identified further our understanding of OA progression and eventual TKR onset.
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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.