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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