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Research ArticleAdult Brain

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

A. Yahav-Dovrat, M. Saban, G. Merhav, I. Lankri, E. Abergel, A. Eran, D. Tanne, R.G. Nogueira and R. Sivan-Hoffmann
American Journal of Neuroradiology February 2021, 42 (2) 247-254; DOI: https://doi.org/10.3174/ajnr.A6923
A. Yahav-Dovrat
aFrom the Department of Radiology (A.Y.-D., G.M., A.E., R.S.-H.)
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M. Saban
dFaculty of Social health and Welfare (M.S.), Haifa University, Haifa, Israel
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G. Merhav
aFrom the Department of Radiology (A.Y.-D., G.M., A.E., R.S.-H.)
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I. Lankri
eFaculty of Medicine (I.L.), Technion Israel institute of Technology, Haifa, Israel
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E. Abergel
bUnit of Interventional Neuroradiology (E.A., R.S.-H.)
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A. Eran
aFrom the Department of Radiology (A.Y.-D., G.M., A.E., R.S.-H.)
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D. Tanne
cStroke and Cognition Institute (D.T.), Rambam Health Care Campus, Haifa, Israel
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R.G. Nogueira
fNeuroendovascular Service (R.G.N.), Marcus Stroke and Neuroscience Center Grady Memorial Hospital, Atlanta, Georgia
gDepartments of Neurology, Neurosurgery, and Radiology (R.G.N.), Emory University School of Medicine, Atlanta, Georgia
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R. Sivan-Hoffmann
aFrom the Department of Radiology (A.Y.-D., G.M., A.E., R.S.-H.)
bUnit of Interventional Neuroradiology (E.A., R.S.-H.)
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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.

MATERIALS AND METHODS: Viz LVO was installed for this study in a comprehensive stroke center. All consecutive head and neck CTAs performed from January 2018 to March 2019 were scanned by the algorithm for detection of large-vessel occlusions. The system results were compared with the formal reports of senior neuroradiologists used as ground truth for the presence of a large-vessel occlusion.

RESULTS: A total of 1167 CTAs were included in the study. Of these, 404 were stroke protocols. Seventy-five (6.4%) patients had a large-vessel occlusion as ground truth; 61 were detected by the system. Sensitivity was 0.81, negative predictive value was 0.99, and accuracy was 0.94. In the stroke protocol subgroup, 72 (17.8%) of 404 patients had a large-vessel occlusion, with 59 identified by the system, showing a sensitivity of 0.82, negative predictive value of 0.96, and accuracy of 0.89.

CONCLUSIONS: Our experience evaluating Viz LVO shows that the system has the potential for early identification of patients with stroke with large-vessel occlusions, hopefully improving future management and stroke care.

ABBREVIATIONS:

ICA-T
ICA terminus
ICC
intraclass correlation coefficient
LVO
large-vessel occlusion
PPV
positive predictive value
  • © 2021 by American Journal of Neuroradiology
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American Journal of Neuroradiology: 42 (2)
American Journal of Neuroradiology
Vol. 42, Issue 2
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Evaluation of Artificial Intelligence–Powered Identification of Large-Vessel Occlusions in a Comprehensive Stroke Center
A. Yahav-Dovrat, M. Saban, G. Merhav, I. Lankri, E. Abergel, A. Eran, D. Tanne, R.G. Nogueira, R. Sivan-Hoffmann
American Journal of Neuroradiology Feb 2021, 42 (2) 247-254; DOI: 10.3174/ajnr.A6923

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Evaluation of Artificial Intelligence–Powered Identification of Large-Vessel Occlusions in a Comprehensive Stroke Center
A. Yahav-Dovrat, M. Saban, G. Merhav, I. Lankri, E. Abergel, A. Eran, D. Tanne, R.G. Nogueira, R. Sivan-Hoffmann
American Journal of Neuroradiology Feb 2021, 42 (2) 247-254; DOI: 10.3174/ajnr.A6923
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