Predicting Total Knee Replacement Surgery Using Radiomic Features Extracted from MRI Scans

Motivation: Predicting total knee replacement surgery can help patients and healthcare providers make informed treatment decisions. Goal(s): To develop a machine learning model for predicting patients that will undergo a total knee replacement using radiomics. Approach: To extract radiomic features from images of patients and healthy subjects and train different machine learning models to predict patients' outcomes. Results: The best model achieved an accuracy of 87.2%. Three out of the four most significant radiomic features selected in the All subset were derived from the meniscus areas, suggesting that the meniscus may play a crucial role in predicting patient outcomes. Impact: Radiomic features from MRI scans effectively classify TKR-positive patients, particularly those incorporating meniscus features. These models potentially can predict patient outcomes and guide treatment decisions, but further research is needed to enhance performance and validate findings in broader patient populations.