Prediction of Stroke Infarct Growth Rates by Baseline Perfusion Imaging.

ABSTRACT:

Background and purpose

Computed tomography perfusion imaging allows estimation of tissue status in patients with acute ischemic stroke. We aimed to improve prediction of the final infarct and individual infarct growth rates using a deep learning approach.

Methods

We trained a deep neural network to predict the final infarct volume in patients with acute stroke presenting with large vessel occlusions based on the native computed tomography perfusion images, time to reperfusion and reperfusion status in a derivation cohort (MR CLEAN trial [Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands]). The model was internally validated in a 5-fold cross-validation and externally in an independent dataset (CRISP study [CT Perfusi

SUBMITTER: Wouters A 

PROVIDER: S-EPMC8792202 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature