Artificial intelligence to diagnose ischemic stroke and identify large vessel occlusions: a systematic review.

Journal: Journal of neurointerventional surgery
PMID: 31594798

Abstract

BACKGROUND AND PURPOSE: Acute stroke caused by large vessel occlusions (LVOs) requires emergent detection and treatment by endovascular thrombectomy. However, radiologic LVO detection and treatment is subject to variable delays and human expertise, resulting in morbidity. Imaging software using artificial intelligence (AI) and machine learning (ML), a branch of AI, may improve rapid frontline detection of LVO strokes. This report is a systematic review of AI in acute LVO stroke identification and triage, and characterizes LVO detection software.

Authors

  • Nick M Murray
    Department of Neurology & Neurological Sciences, Stanford University, Stanford, California, USA.
  • Mathias Unberath
    Johns Hopkins University, Baltimore, MD, USA.
  • Gregory D Hager
    Department of Computer Science, The Johns Hopkins University, 3400 N. Charles St., Malone Hall Room 340, Baltimore, MD, 21218, USA.
  • Ferdinand K Hui
    The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland.

Keywords

External Resources

Popular Topics
Recent Journals