Abstract #3797

Estimation of Time-to-Total Knee Replacement Surgery with Deep Learning using MRI and Clinical Data

Ozkan Cigdem^1,2, Eisa Hedayati^1,2, Haresh R. Rajamohan³, Kyunghyun Cho³, Gregory Chang⁴, Richard Kijowski⁴, and Cem M. Deniz^1,2

¹Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States, ²Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York, NY, United States, ³Center of Data Science, New York University, New York, NY, United States, ⁴Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States

Synopsis

Keywords: Diagnosis/Prediction, Machine Learning/Artificial Intelligence, Osteoarthritis

Motivation: The combination of deep learning, MRI, and clinical data for predicting the time to total knee replacement surgery in knee osteoarthritis patients has been investigated.

Goal(s): The 3D Resnet18 model was employed to extract features from MRI scans, and relevant clinical variables were integrated to establish a comprehensive predictive model.

Approach: Time-to-surgery probabilities were estimated using the ensemble random survival forest model. The model’s performance was evaluated across clinical variables, two MRI sequences, and their combinations.

Results: The proposed approach aims to help the precision of TKR surgery decision-making using artificial intelligence.

Impact: This study fuses deep learning, survival analysis, MRI, and clinical data to accurately predict time-to-TKR surgery. Our approach has the potential to enhance TKR surgery decision precision.

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