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 Deniz^4,5

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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