Prediction of response to thrombolysis in acute stroke using neural network analysis of CT perfusion imaging.

ABSTRACT:

Background

In ischaemic stroke patients undergoing reperfusion therapy, the amount of salvageable tissue, that is, extent of the ischaemic penumbra, predicts the clinical outcomes. CT perfusion (CTP) enables quantification of penumbral tissues to guide decision making, and current programmes have automated its analysis. More advanced machine learning techniques utilising the CTP maps may improve prediction beyond the ischaemic volume measures.

Method

We determined whether applying convolutional neural networks (CNN), a key machine learning technique in modelling image-label relationships, to post-processed CTP maps improved prediction of outcome, assessed by 3 months modified Rankin scale (mRS). Patients who underwent thrombolysis but not thrombectomy were included. CTP maps

SUBMITTER: Chen Y 

PROVIDER: S-EPMC10472959 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature