Artificial Intelligence and Stroke Imaging: A West Coast Perspective.

Journal: Neuroimaging clinics of North America
Published Date: Sep 18, 2020

Abstract

Artificial intelligence (AI) advancements have significant implications for medical imaging. Stroke is the leading cause of disability and the fifth leading cause of death in the United States. AI applications for stroke imaging are a topic of intense research. AI techniques are well-suited for dealing with vast amounts of stroke imaging data and a large number of multidisciplinary approaches used in classification, risk assessment, segmentation tasks, diagnosis, prognosis, and even prediction of therapy responses. This article addresses this topic and seeks to present an overview of machine learning and/or deep learning applied to stroke imaging.

Authors

  • Guangming Zhu
    1 Department of Radiology, Neuroradiology Division, Stanford University School of Medicine, 300 Pasteur Dr, Stanford, CA 94305.
  • Bin Jiang
    Department of Urology, Chinese People's Liberation Army General Hospital, Beijing, 100039 China.
  • Hui Chen
    Xiangyang Central HospitalAffiliated Hospital of Hubei University of Arts and Science Xiangyang 441000 China.
  • Elizabeth Tong
    Department of Radiology, Stanford University, Stanford, California, USA.
  • Yuan Xie
  • Tobias D Faizy
    Department of Neuroradiology, Stanford University, 300 Pasteur Drive, Stanford, CA 94305, USA.
  • Jeremy J Heit
    Department of Neuroradiology, Stanford University, 300 Pasteur Drive, Stanford, CA 94305, USA.
  • Greg Zaharchuk
    Stanford University, Stanford CA 94305, USA.
  • Max Wintermark
    Department of Radiology, Stanford University, Stanford, California, USA.

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