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Abstract #0241

Deep Learning Predicts Total Knee Replacement from Magnetic Resonance Images

Aniket A. Tolpadi1,2, Jinhee J. Lee1, Valentina Pedoia1, and Sharmila Majumdar1
1Department of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, United States, 2Department of Bioengineering, University of California, Berkeley, Berkeley, CA, United States

Total Knee Replacement (TKR) can relieve pain from osteoarthritis (OA), but patient dissatisfaction is not uncommon, making TKR delay advisable until absolutely necessary. Models could identify at-risk patients requiring nonsurgical treatment, prolonging good health and delaying TKR. We present a pipeline that uses DenseNet-121 to predict TKR onset from MRI images, integrates clinical information by ensembling logistic regression models, and sensitively and specifically predicts TKR, particularly at early-stage OA. Occlusion maps show many OA progression imaging biomarkers are implicated in TKR, and many tissues involved in knee flexion and extension preferentially affect TKR probability at early-stage and late-stage OA, respectively.

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