Estimation of Time-to-Total Knee Replacement Surgery with Deep Learning using MRI and Clinical Data

Motivation: The combination of deep learning, MRI, and clinical data for predicting the time to total knee replacement surgery in knee osteoarthritis patients has been investigated. Goal(s): The 3D Resnet18 model was employed to extract features from MRI scans, and relevant clinical variables were integrated to establish a comprehensive predictive model. Approach: Time-to-surgery probabilities were estimated using the ensemble random survival forest model. The model’s performance was evaluated across clinical variables, two MRI sequences, and their combinations. Results: The proposed approach aims to help the precision of TKR surgery decision-making using artificial intelligence. Impact: This study fuses deep learning, survival analysis, MRI, and clinical data to accurately predict time-to-TKR surgery. Our approach has the potential to enhance TKR surgery decision precision.