# Deep learning predicts total knee replacement from magnetic resonance images

arXiv: https://arxiv.org/abs/2002.10591 · HTML: https://mlsanity.com/paper/2002.10591/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Feb 2020.

## Built on by (most cited first, 10 of 10)

- [Clinically-Inspired Multi-Agent Transformers for Disease Trajectory Forecasting from Multimodal Data](https://mlsanity.com/paper/2210.13889/) (2022)
- [Predicting Knee Osteoarthritis Progression from Structural MRI using Deep Learning](https://mlsanity.com/paper/2201.10849/) (2022)
- [Knee Cartilage Defect Assessment by Graph Representation and Surface Convolution](https://mlsanity.com/paper/2201.04318/) (2022)
- [Evidence-aware multi-modal data fusion and its application to total knee replacement prediction](https://mlsanity.com/paper/2303.13810/) (2023)
- [End-To-End Prediction of Knee Osteoarthritis Progression With Multi-Modal Transformers](https://mlsanity.com/paper/2307.00873/) (2023)
- [A Progressive Risk Formulation for Enhanced Deep Learning based Total Knee Replacement Prediction in Knee Osteoarthritis](https://mlsanity.com/paper/2406.10119/) (2024)
- [Self-Supervised Learning for Knee Osteoarthritis: Diagnostic Limitations and Prognostic Value of Hospital Data](https://mlsanity.com/paper/2603.24903/) (2026)
- [MR-Transformer: Vision Transformer for Total Knee Replacement Prediction Using Magnetic Resonance Imaging](https://mlsanity.com/paper/2405.02784/) (2024)
- [Identity-Consistent Diffusion Network for Grading Knee Osteoarthritis Progression in Radiographic Imaging](https://mlsanity.com/paper/2407.21381/) (2024)
- [Improving Generalization in MRI-Based Deep Learning Models for Total Knee Replacement Prediction](https://mlsanity.com/paper/2504.19203/) (2025)

## Abstract (arXiv)

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.