An East Coast Perspective on Artificial Intelligence and Machine Learning: Part 2: Ischemic Stroke Imaging and Triage.

Journal: Neuroimaging clinics of North America
Published Date: Nov 1, 2020

Abstract

Acute ischemic stroke constitutes approximately 85% of strokes. Most strokes occur in community settings; thus, automatic algorithms techniques are attractive for managing these cases. This article reviews the use of deep learning convolutional neural networks in the management of ischemic stroke. Artificial intelligence-based algorithms may be used in patient triage to detect and sound the alarm based on early imaging, alert care teams, and assist in treatment selection. This article reviews algorithms for artificial intelligence techniques that may be used to detect and localize acute ischemic stroke. We describe artificial intelligence algorithms for these tasks and illustrate them with examples.

Authors

  • Rajiv Gupta
    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.
  • Sanjith Prahas Krishnam
    Department of Neurology, University of Alabama at Birmingham, SC 350, 1720 2nd Avenue South, Birmingham, AL 35294, USA.
  • Pamela W Schaefer
    Department of Radiology, Division of Neuroradiology, Massachusetts General Hospital, Harvard Medical School, Room: GRB-273A, 55 Fruit Street, Boston, MA 02114, USA.
  • Michael H Lev
    Department of Radiology, Massachusetts General Hospital, Boston, MA, USA.
  • R Gilberto Gonzalez
    Department of Radiology, Division of Neuroradiology, Massachusetts General Hospital, Harvard Medical School, Room: GRB-273A, 55 Fruit Street, Boston, MA 02114, USA.

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