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Artificial Intelligence and Acute Stroke Imaging

J.E. Soun, D.S. Chow, M. Nagamine, R.S. Takhtawala, C.G. Filippi, W. Yu and P.D. Chang
American Journal of Neuroradiology November 2020, DOI: https://doi.org/10.3174/ajnr.A6883
J.E. Soun
aFrom the Departments of Radiological Sciences (J.E.S., D.S.C., P.D.C.)
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D.S. Chow
aFrom the Departments of Radiological Sciences (J.E.S., D.S.C., P.D.C.)
cCenter for Artificial Intelligence in Diagnostic Medicine (D.S.C., R.S.T., P.D.C.), University of California, Irvine, Orange, California
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M. Nagamine
bNeurology (M.N., W.Y.)
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R.S. Takhtawala
cCenter for Artificial Intelligence in Diagnostic Medicine (D.S.C., R.S.T., P.D.C.), University of California, Irvine, Orange, California
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C.G. Filippi
dDepartment of Radiology (C.G.F.), Northwell Health, Lenox Hill Hospital, New York, New York
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W. Yu
bNeurology (M.N., W.Y.)
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P.D. Chang
aFrom the Departments of Radiological Sciences (J.E.S., D.S.C., P.D.C.)
cCenter for Artificial Intelligence in Diagnostic Medicine (D.S.C., R.S.T., P.D.C.), University of California, Irvine, Orange, California
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Abstract

SUMMARY: Artificial intelligence technology is a rapidly expanding field with many applications in acute stroke imaging, including ischemic and hemorrhage subtypes. Early identification of acute stroke is critical for initiating prompt intervention to reduce morbidity and mortality. Artificial intelligence can help with various aspects of the stroke treatment paradigm, including infarct or hemorrhage detection, segmentation, classification, large vessel occlusion detection, Alberta Stroke Program Early CT Score grading, and prognostication. In particular, emerging artificial intelligence techniques such as convolutional neural networks show promise in performing these imaging-based tasks efficiently and accurately. The purpose of this review is twofold: first, to describe AI methods and available public and commercial platforms in stroke imaging, and second, to summarize the literature of current artificial intelligence–driven applications for acute stroke triage, surveillance, and prediction.

ABBREVIATIONS:

AI
artificial intelligence
ANN
artificial neural network
AUC
area under the curve
CNN
convolutional neural network
DL
deep learning
ICC
intraclass correlation coefficient
ICH
intracranial hemorrhage
LVO
large vessel occlusion
ML
machine learning
MRP
MR perfusion
RF
random forest
SVM
support vector machine

Footnotes

  • Disclosures: Daniel Chow—RELATED: Grant: Avicenna.ai*; UNRELATED: Consultancy: Canon Medical; Expert Testimony: Cullins & Grandy; Grants/Grants Pending: Canon Medical, Novocure; Stock/Stock Options: Avicenna.ai. Christopher Filippi—UNRELATED: Consultancy: Guerbet, Syntactx, Comments: Advisor on AI (Guerbet) and interpret brain MR for clinical trials (Syntactx); Grants/Grants Pending: FASNR grant and National MS Society grant; Stock/Stock Options: Minority stakeholder in start-up Avicenna. Wengui Yu—UNRELATED: Employment: University of California Irvine. Peter Chang—UNRELATED: Consultancy: Canon Medical, Comments: Consulting, travel expenses, honorarium for invited keynote speaker (RSNA); Stock/Stock Options: Avicenna.ai, Comments: Co-founder, board member. *Money paid to institution.

  • © 2021 by American Journal of Neuroradiology

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J.E. Soun, D.S. Chow, M. Nagamine, R.S. Takhtawala, C.G. Filippi, W. Yu, P.D. Chang
Artificial Intelligence and Acute Stroke Imaging
American Journal of Neuroradiology Nov 2020, DOI: 10.3174/ajnr.A6883

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Artificial Intelligence and Acute Stroke Imaging
J.E. Soun, D.S. Chow, M. Nagamine, R.S. Takhtawala, C.G. Filippi, W. Yu, P.D. Chang
American Journal of Neuroradiology Nov 2020, DOI: 10.3174/ajnr.A6883
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  • Article
    • Abstract
    • ABBREVIATIONS:
    • Overview of AI
    • Evaluation of AI Performance
    • AI Platforms in Stroke and Hemorrhage
    • AI Evaluation of Ischemic Stroke
    • AI Evaluation of Hemorrhage
    • Conclusions
    • Acknowledgments
    • Footnotes
    • References
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