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New Devices and Techniques
Review
Artificial intelligence to diagnose ischemic stroke and identify large vessel occlusions: a systematic review

Authors

  • Nick M Murray Department of Neurology & Neurological Sciences, Stanford University, Stanford, California, USA Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, Maryland, USA PubMed articlesGoogle scholar articles
  • Mathias Unberath Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, Maryland, USA Department of Computer Science, Johns Hopkins University, Baltimore, Maryland, USA PubMed articlesGoogle scholar articles
  • Gregory D Hager Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, Maryland, USA Department of Computer Science, Johns Hopkins University, Baltimore, Maryland, USA PubMed articlesGoogle scholar articles
  • Ferdinand K Hui Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, Maryland, USA Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA PubMed articlesGoogle scholar articles
  1. Correspondence to Dr Nick M Murray, Department of Neurology & Neurological Sciences, Stanford University, Stanford, California, USA; nmurray{at}stanford.edu
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Citation

Murray NM, Unberath M, Hager GD, et al
Artificial intelligence to diagnose ischemic stroke and identify large vessel occlusions: a systematic review
Journal of NeuroInterventional Surgery 2020;12:156-164.

Publication history

  • Received May 20, 2019
  • Revised July 29, 2019
  • Accepted July 29, 2019
  • First published October 8, 2019.
Online issue publication 
January 17, 2020

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© Author(s) (or their employer(s)) 2020. No commercial re-use. See rights and permissions. Published by BMJ.