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Abstract #2744

Early Prediction of Total Knee Replacement using Structural MRI and 3D Deep Convolutional Neural Networks

Kevin Leung1, Gregory Chang2, Kyunghyun Cho3, and Cem Deniz4,5

1Courant Institute of Mathematical Sciences and Leonard N. Stern School of Business, New York University, New York, NY, United States, 2Department of Radiology, Center for Musculoskeletal Care, New York University Langone Medical Center, New York, NY, United States, 3Courant Institute of Mathematical Science and Center for Data Science, New York University, New York, NY, United States, 4Department of Radiology, Center 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, 5Sackler Institute of Graduate Biomedical Sciences, New University School of Medicine, New York, NY, United States

The early prediction of individuals who will eventually require total knee replacement (TKR) remains a challenging problem. In this project, we propose to use 3D deep convolutional neural networks (CNN) to predict the likelihood of a patient receiving a TKR within nine years using 718 subjects from the Osteoarthritis Initiative1 (OAI) dataset. We found that our model results in better performance compared to a logistic regression model using clinical risk factors2 (AUC: 0.8480±.0173 vs 0.7716±.0229 and accuracy: 77.15±1.88% vs. 71.16±2.70%).

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