Abstract #1881

Total Knee Replacement Prediction using Twin Class Distribution Estimation

Chaojie Zhang¹, Shengjia Chen¹, Haoxu Huang², Haresh Rengaraj Rajamohan³, Jungkyu Park¹, Noah Kasmanoff¹, Kyunghyun Cho³, Gregory Chang¹, Richard Kijowski¹, and Cem M. Deniz¹

¹Department of Radiology, New York University Langone Health, New York, NY, United States, ²Courant Institute of Mathematical Sciences, New York University, New York, NY, United States, ³Center for Data Science, New York University, New York, NY, United States

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Machine Learning/Artificial IntelligenceOur study implemented the self-supervised learning method, Twin Class Distribution Estimation, with unlabeled knee MR images. The self-supervised pretraining improves the downstream analysis in predicting total knee replacement within 9 years using labeled knee MR images. The self-supervised features are shown to be efficient classifiers in TKR prediction.

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