Leveraging artificial intelligence in ischemic stroke imaging

Omid Shafaat, Joshua D. Bernstock, Amir Shafaat, Vivek S. Yedavalli, Galal Elsayed, Saksham Gupta, Ehsan Sotoudeh, Haris I. Sair, David M. Yousem, Houman Sotoudeh

Research output: Contribution to journalReview articlepeer-review

Abstract

Artificial intelligence (AI) is having a disruptive and transformative effect on clinical medicine. Prompt clinical diagnosis and imaging are critical for minimizing the morbidity and mortality associated with ischemic strokes. Clinicians must understand the current strengths and limitations of AI to provide optimal patient care. Ischemic stroke is one of the medical fields that have been extensively evaluated by artificial intelligence. Presented herein is a review of artificial intelligence applied to clinical management of stroke, geared toward clinicians. In this review, we explain the basic concept of AI and machine learning. This review is without coding and mathematical details and targets the clinicians involved in stroke management without any computer or mathematics’ background. Here the AI application in ischemic stroke is summarized and classified into stroke imaging (automated diagnosis of brain infarction, automated ASPECT score calculation, infarction segmentation), prognosis prediction, and patients’ selection for treatment.

Original languageEnglish (US)
JournalJournal of Neuroradiology
DOIs
StateAccepted/In press - 2021

Keywords

  • Artificial intelligence
  • Brain ischemia
  • Machine learning
  • Neural network
  • Stroke

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Clinical Neurology

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