Final infarct prediction in acute ischemic stroke
This work tackles a critical medical problem for stroke patients, but it appears incremental as it builds on existing core-penumbra concepts and mismatch criteria.
The paper addresses the prediction of final infarct in acute ischemic stroke using medical imaging analysis, employing machine learning methods such as deconvolution and convolutional neural networks to enhance treatment planning.
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.