TY - JOUR T1 - Artificial Intelligence and Acute Stroke Imaging JF - American Journal of Neuroradiology JO - Am. J. Neuroradiol. DO - 10.3174/ajnr.A6883 AU - J.E. Soun AU - D.S. Chow AU - M. Nagamine AU - R.S. Takhtawala AU - C.G. Filippi AU - W. Yu AU - P.D. Chang Y1 - 2020/11/26 UR - http://www.ajnr.org/content/early/2020/11/26/ajnr.A6883.abstract N2 - SUMMARY: Artificial intelligence technology is a rapidly expanding field with many applications in acute stroke imaging, including ischemic and hemorrhage subtypes. Early identification of acute stroke is critical for initiating prompt intervention to reduce morbidity and mortality. Artificial intelligence can help with various aspects of the stroke treatment paradigm, including infarct or hemorrhage detection, segmentation, classification, large vessel occlusion detection, Alberta Stroke Program Early CT Score grading, and prognostication. In particular, emerging artificial intelligence techniques such as convolutional neural networks show promise in performing these imaging-based tasks efficiently and accurately. The purpose of this review is twofold: first, to describe AI methods and available public and commercial platforms in stroke imaging, and second, to summarize the literature of current artificial intelligence–driven applications for acute stroke triage, surveillance, and prediction.AIartificial intelligenceANNartificial neural networkAUCarea under the curveCNNconvolutional neural networkDLdeep learningICCintraclass correlation coefficientICHintracranial hemorrhageLVOlarge vessel occlusionMLmachine learningMRPMR perfusionRFrandom forestSVMsupport vector machine ER -