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Abstract #2879

Predicting Time-to-Total Knee Replacement: Deep Learning vs Radiomics

Ozkan Cigdem1, Eros Montin1, Shengjia Chen2, Chaojie Zhang1, Kyunghyun Cho3, Riccardo Lattanzi1, and Cem M Deniz1
1Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States, 2New York University Grossman School of Medicine, New York, NY, United States, 3Center of Data Science, New York University, New York, NY, United States

Synopsis

Keywords: Radiomics, Radiomics

Motivation: Predicting time-to-total knee replacement (TKR) is essential for tailoring treatment strategies. Reliable models can identify knee osteoarthritis progression, assisting physicians in customizing care plans and potentially enhancing patient outcomes.

Goal(s): Evaluate and compare the effectiveness of supervised and self-supervised deep learning (DL)-extracted features, alongside radiomics features, in predicting time-to-TKR within a 9-year timeframe.

Approach: Radiomics features, and features from DL models trained in supervised and self-supervised modes were extracted from images. A Lasso feature selection was applied, and XGBoost was used for prediction.

Results: Features extracted from supervised DL model achieved the highest accuracy of 71.6%, outperforming features from radiomics and self-supervised learning.

Impact: This study advances predictive models for estimating time-to-TKR, aiming to support clinicians in decision-making for knee osteoarthritis management. Accurate prediction models can help patients and healthcare providers make informed treatment decisions.

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Keywords