Artificial Neural Network Computer Tomography Perfusion Prediction of Ischemic Core

Abstract

Background and Purpose-Computed tomography perfusion (CTP) is a useful tool in the evaluation of acute ischemic stroke, where it can provide an estimate of the ischemic core and the ischemic penumbra. The optimal CTP parameters to identify the ischemic core remain undetermined. Methods-We used artificial neural networks (ANNs) to optimally predict the ischemic core in acute stroke patients, using diffusion-weighted imaging as the gold standard. We first designed an ANN based on CTP data alone and next designed an ANN based on clinical and CTP data. Results-The ANN based on CTP data predicted the ischemic core with a mean absolute error of 13.8 mL (SD, 13.6 mL) compared with diffusion-weighte..