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Abstract #4431

Prediction of Total Knee Replacement using Vision Transformers

Chaojie Zhang1, Haresh Rengaraj Rajamohan2, Kyunghyun Cho2, Gregory Chang1, and Cem M. Deniz1
1Department of Radiology, New York University Langone Health, New York, NY, United States, 2Center for Data Science, New York University, New York, NY, United States


Vision transformers were used to predict total knee replacement within 9 years from magnetic resonance images. Inspired by MRNet, 2D slices of an MR image were encoded by a vision transformer and these encodings were aggregated to provide a single prediction outcome from a 3D MR volume. Our results suggest that the prediction performance of vision transformers was comparable with the models based on convolutional neural networks for the outcome prediction task. Moreover, training models with stochastic gradient descent optimizer provided a better performance compared with the Adam optimizer.

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