  Knee Osteoarthritis (OA) is a common musculoskeletal disorder in the United
States. When diagnosed at early stages, lifestyle interventions such as
exercise and weight loss can slow OA progression, but at later stages, only an
invasive option is available: total knee replacement (TKR). Though a generally
successful procedure, only 2/3 of patients who undergo the procedure report
their knees feeling ''normal'' post-operation, and complications can arise that
require revision. This necessitates a model to identify a population at higher
risk of TKR, particularly at less advanced stages of OA, such that appropriate
treatments can be implemented that slow OA progression and delay TKR. Here, we
present a deep learning pipeline that leverages MRI images and clinical and
demographic information to predict TKR with AUC $0.834 \pm 0.036$ (p < 0.05).
Most notably, the pipeline predicts TKR with AUC $0.943 \pm 0.057$ (p < 0.05)
for patients without OA. Furthermore, we develop occlusion maps for
case-control pairs in test data and compare regions used by the model in both,
thereby identifying TKR imaging biomarkers. As such, this work takes strides
towards a pipeline with clinical utility, and the biomarkers identified further
our understanding of OA progression and eventual TKR onset.
