Automated Vessel Occlusion Software in Acute Ischemic Stroke: Pearls and Pitfalls.

Journal: Stroke
Published Date: Jun 9, 2025

Abstract

Software programs leveraging artificial intelligence to detect vessel occlusions are now widely available to aid in stroke triage. Given their proprietary use, there is a surprising lack of information regarding how the software works, who is using the software, and their performance in an unbiased real-world setting. In this educational review of automated vessel occlusion software, we discuss emerging evidence of their utility, underlying algorithms, real-world diagnostic performance, and limitations. The intended audience includes specialists in stroke care in neurology, emergency medicine, radiology, and neurosurgery. Practical tips for onboarding and utilization of this technology are provided based on the multidisciplinary experience of the authorship team.

Authors

  • Yasmin N Aziz
    University of Cincinnati Gardner Neuroscience Institute, University of Cincinnati, OH.
  • Aakanksha Sriwastwa
    Philips Innovative Technologies, Röntgenstr. 24-26, 22335 Hamburg, Germany. Electronic address: sriwasaa@ucmail.uc.edu.
  • Kambiz Nael
    Department of Radiology, University of California, Los Angeles, Los Angeles, CA, USA.
  • Pablo Harker
    Department of Neurosurgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
  • Eva A Mistry
    Department of Neurology and Rehabilitation Medicine (Y.N.A., P.H., E.A.M., P.K.), University of Cincinnati, OH.
  • Pooja Khatri
    Department of Radiology, University of Cincinnati, 234 Goodman Street, Cincinnati, OH, 45267, USA.
  • Arindam R Chatterjee
    Departments of Neurosurgery and Neurology, Washington University School of Medicine in St Louis, Mallinckrodt Institute of Radiology, MO (A.R.C.).
  • Jeremy J Heit
    Department of Neuroradiology, Stanford University, 300 Pasteur Drive, Stanford, CA 94305, USA.
  • Ashutosh Jadhav
    IBM Almaden Research Center, San Jose, CA.
  • Vivek Yedavalli
    Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.
  • Achala S Vagal
    Department of Radiology (A.S., A.S.V.), University of Cincinnati, OH.

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