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Prediction of tissue outcome in acute ischemic stroke based on single-phase CT angiography at admission

IntroductionIn acute ischemic stroke, prediction of the tissue outcome after reperfusion can be used to identify patients that might benefit from mechanical thrombectomy (MT). The aim of this work was to develop a deep learning model that can predict the follow-up infarct location and extent exclusi...

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Principais autores: Frosti Palsson, Nils D. Forkert, Lukas Meyer, Gabriel Broocks, Fabian Flottmann, Máté E. Maros, Matthias Bechstein, Laurens Winkelmeier, Eckhard Schlemm, Jens Fiehler, Susanne Gellißen, Helge C. Kniep
Format: Artigo
Sprog:Inglês
Udgivet: Frontiers Media S.A. 2024-03-01
Serier:Frontiers in Neurology
Fag:
stroke
infarct core
mechanical thrombectomy
deep learning
segmentation
Online adgang:https://www.frontiersin.org/articles/10.3389/fneur.2024.1330497/full
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