Abstract

Motivation: Current deep learning methods for assessing knee osteoarthritis have limitations in learning long-range spatial information from magnetic resonance imaging (MRI). Goal(s): This study aims to develop a new deep learning model for total knee replacement (TKR) prediction using MRI. Approach: We proposed a novel transformer-based model, MR-Transformer, adapted from the ImageNet pre-trained vision transformer DeiT-Ti. The model can capture long-range spatial information from MR images with transformer architecture. We evaluated our model on TKR prediction using MR images with different tissue contrasts. Results: The experimental results demonstrated an improved performance of MR-Transformer compared to conventional deep learning models. Impact: Our proposed MR-Transformer enhances computer-aided diagnosis accuracy in total knee replacement prediction using MRI. It has the potential to provide rapid and quality diagnostic outcomes, assisting physicians in making timely and informed treatment decisions.