Abstract #4880

3D Convolutional Networks to predict Total Knee Replacement using Structural MRI

Tianyu Wang¹, Kevin Leung^2,3, Kyunghyun Cho^1,3, Gregory Chang^4,5, and Cem M. Deniz^4,6,7

¹Center for Data Science, New York University, New York, NY, United States, ²Leonard N. Stern School of Business, New York University, New York, NY, United States, ³Courant Institute of Mathematical Sciences, New York University, New York, NY, United States, ⁴Department of Radiology, New York University Langone Medical Center, New York, NY, United States, ⁵Center for Musculoskeletal Care, New York University Langone Medical Center, New York, NY, United States, ⁶Center for Advanced Imaging Innovation and Research (CAI2R) and Bernard and Irene Schwartz Center for Biomedical Imaging, New York University Langone Medical Center, New York, NY, United States, ⁷The Sackler Institute of Graduate Biomedical Sciences, New York University School of Medicine, New York, NY, United States

Osteoarthritis (OA) is a chronic degenerative disorder of joints and is the most common reason leading to total knee joint replacement (TKR). In this work, we developed an automated OA-relevant imaging biomarker identification system based on MR images and deep learning (DL) methods to predict knee OA progression. Our results indicate that the combination of multiple MR images with different contrast and resolution provides the best model to predict TKR with AUC 0.88±0.01.

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