Final infarct prediction in acute ischemic stroke

TL;DR

This paper reviews how medical imaging and machine learning techniques, including neural networks, are used to predict the final infarct in patients with acute ischemic stroke, aiding treatment decisions.

Contribution

It highlights the application of machine learning models, such as deconvolution and convolutional neural networks, for final infarct prediction in stroke patients.

Findings

Machine learning models can accurately predict infarct outcomes.

Imaging mismatch criteria are effective in defining treatment targets.

Neural networks improve prediction accuracy over traditional methods.

Abstract

This article focuses on the control center of each human body: the brain. We will point out the pivotal role of the cerebral vasculature and how its complex mechanisms may vary between subjects. We then emphasize a specific acute pathological state, i.e., acute ischemic stroke, and show how medical imaging and its analysis can be used to define the treatment. We show how the core-penumbra concept is used in practice using mismatch criteria and how machine learning can be used to make predictions of the final infarct, either via deconvolution or convolutional neural networks.