  A survival analysis model for predicting time-to-total knee replacement (TKR)
was developed using features from medical images and clinical measurements.
Supervised and self-supervised deep learning approaches were utilized to
extract features from radiographs and magnetic resonance images. Extracted
features were combined with clinical and image assessments for survival
analysis using random survival forests. The proposed model demonstrated high
discrimination power by combining deep learning features and clinical and image
assessments using a fusion of multiple modalities. The model achieved an
accuracy of 75.6% and a C-Index of 84.8% for predicting the time-to-TKR
surgery. Accurate time-to-TKR predictions have the potential to help assist
physicians to personalize treatment strategies and improve patient outcomes.
