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Deep Learning Predicts Total Knee Replacement from Magnetic Resonance Images.


ABSTRACT: 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 ± 0.036 (p?

SUBMITTER: Tolpadi AA 

PROVIDER: S-EPMC7156761 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Deep Learning Predicts Total Knee Replacement from Magnetic Resonance Images.

Tolpadi Aniket A AA   Lee Jinhee J JJ   Pedoia Valentina V   Majumdar Sharmila S  

Scientific reports 20200414 1


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 mo  ...[more]

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