ArticleScientific reports2023
Prediction of total knee replacement using deep learning analysis of knee MRI.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 2 of them syntheses that pooled it.
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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.
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Who cites it
23 citing papers in PubMed, 2 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
- Longitudinal 3D quantification of joint space from CT images: Semi-automatic segmentation and feasibility of nnU-Net automation.Osteoarthritis imaging · 2026Article
- 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
- Supervised machine learning model to predict total knee replacement in a large osteoartrhitis real-world evidence dataset using retrospectively 20-year insurance data from Israel.BMC medical informatics and decision making · 2026Article
- Article
- 3D CSFA-UNet: a unified attention-driven deep learning framework for accurate knee MRI segmentation and osteoarthritis severity classification.Scientific reports · 2026Article
- Robust disease prognosis via diagnostic knowledge preservation: A sequential learning approach.PloS one · 2026Article
- Article
- Cartilage loss is greater in knees with virtual joint replacement status than in matched controls without.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
- Estimation of time-to-total knee replacement surgery with multimodal modeling and artificial intelligence.Computers in biology and medicine · 2025Article
- ShapeMed-Knee: A Dataset and Neural Shape Model Benchmark for Modeling 3D Femurs.IEEE transactions on medical imaging · 2025Article
- A new method for early diagnosis and treatment of meniscus injury of knee joint in student physical fitness tests based on deep learning method.BioImpacts : BI · 2025Article
- Generative AI in orthopedics: an explainable deep few-shot image augmentation pipeline for plain knee radiographs and Kellgren-Lawrence grading.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- ShapeMed-Knee: A Dataset and Neural Shape Model Benchmark for Modeling 3D Femurs.medRxiv : the preprint server for health sciences · 2024Article
- Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Current methods for assessing knee osteoarthritis (OA) do not provide comprehensive information to make robust and accurate outcome predictions. Deep learning (DL) risk assessment models were developed to predict the progression of knee OA to total knee replacement (TKR) over a 108-month follow-up period using baseline knee MRI. Participants of our retrospective study consisted of 353 case-control pairs of subjects from the Osteoarthritis Initiative with and without TKR over a 108-month follow-up period matched according to age, sex, ethnicity, and body mass index. A traditional risk assessment model was created to predict TKR using baseline clinical risk factors. DL models were created to predict TKR using baseline knee radiographs and MRI. All DL models had significantly higher (p < 0.001) AUCs than the traditional model. The MRI and radiograph ensemble model and MRI ensemble model (where TKR risk predicted by several contrast-specific DL models were averaged to get the ensemble TKR risk prediction) had the highest AUCs of 0.90 (80% sensitivity and 85% specificity) and 0.89 (79% sensitivity and 86% specificity), respectively, which were significantly higher (p < 0.05) than the AUCs of the radiograph and multiple MRI models (where the DL models were trained to predict TKR risk using single contrast or 2 contrasts together as input). DL models using baseline MRI had a higher diagnostic performance for predicting TKR than a traditional model using baseline clinical risk factors and a DL model using baseline knee radiographs.
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