Evaluation of Artificial Intelligence-Powered Identification of Large-Vessel Occlusions in a Comprehensive Stroke Center.

Journal: AJNR. American journal of neuroradiology
Published Date: Dec 31, 2020

Abstract

BACKGROUND AND PURPOSE: Artificial intelligence algorithms have the potential to become an important diagnostic tool to optimize stroke workflow. Viz LVO is a medical product leveraging a convolutional neural network designed to detect large-vessel occlusions on CTA scans and notify the treatment team within minutes via a dedicated mobile application. We aimed to evaluate the detection accuracy of the Viz LVO in real clinical practice at a comprehensive stroke center.

Authors

  • A Yahav-Dovrat
    From the Department of Radiology (A.Y.-D., G.M., A.E., R.S.-H.).
  • M Saban
    Faculty of Social health and Welfare (M.S.), Haifa University, Haifa, Israel.
  • G Merhav
    From the Department of Radiology (A.Y.-D., G.M., A.E., R.S.-H.).
  • I Lankri
    Faculty of Medicine (I.L.), Technion Israel institute of Technology, Haifa, Israel.
  • E Abergel
    Unit of Interventional Neuroradiology (E.A., R.S.-H.).
  • A Eran
    From the Department of Radiology (A.Y.-D., G.M., A.E., R.S.-H.).
  • D Tanne
    Stroke and Cognition Institute (D.T.), Rambam Health Care Campus, Haifa, Israel.
  • R G Nogueira
    Neuroendovascular Service (R.G.N.), Marcus Stroke and Neuroscience Center Grady Memorial Hospital, Atlanta, Georgia.
  • R Sivan-Hoffmann
    From the Department of Radiology (A.Y.-D., G.M., A.E., R.S.-H.) otemsivan3@gmail.com.

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