• Rajiv Gupta, Sanjith Prahas Krishnam, Pamela W Schaefer, Michael H Lev, and Gilberto GonzalezRRDepartment of Radiology, Division of Neuroradiology, Massachusetts General Hospital, Harvard Medical School, Room: GRB-273A, 55 Fruit Street, Boston, MA 02114, USA..
• Department of Radiology, Division of Neuroradiology, Massachusetts General Hospital, Harvard Medical School, Room: GRB-273A, 55 Fruit Street, Boston, MA 02114, USA. Electronic address: Rgupta1@mgh.harvard.edu.
• Neuroimaging Clin. N. Am. 2020 Nov 1; 30 (4): 459-466.

AbstractHemorrhagic stroke is a medical emergency. Artificial intelligence techniques and algorithms may be used to automatically detect and quantitate intracranial hemorrhage in a semiautomated fashion. This article reviews the use of deep learning convolutional neural networks for managing hemorrhagic stroke. Such a capability may be used to alert appropriate care teams, make decisions about patient transport from a primary care center to a comprehensive stroke center, and assist in treatment selection. This article reviews artificial intelligence algorithms for intracranial hemorrhage detection, quantification, and prognostication. Multiple algorithms currently being explored are described and illustrated with the help of examples.Copyright © 2020 Elsevier Inc. All rights reserved.

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