Accuracy of Automated Computer-Aided Diagnosis for Stroke Imaging: A critical evaluation of current evidence

Joanna M. Wardlaw (Lead / Corresponding author), Grant Mair, Rüdiger von Kummer, Michelle C. Williams, Wenwen Li, Amos J. Storkey, Emanuel Trucco, David S. Liebeskind, Andrew Farrall, Philip M. Bath, Philip White

Research output: Contribution to journalReview articlepeer-review

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Abstract

There is increasing interest in computer applications, using artificial intelligence methodologies, to perform health care tasks previously performed by humans, particularly in medical imaging for diagnosis. In stroke, there are now commercial artificial intelligence software for use with computed tomography or MR imaging to identify acute ischemic brain tissue pathology, arterial obstruction on computed tomography angiography or as hyperattenuated arteries on computed tomography, brain hemorrhage, or size of perfusion defects. A rapid, accurate diagnosis may aid treatment decisions for individual patients and could improve outcome if it leads to effective and safe treatment; or conversely, to disaster if a delayed or incorrect diagnosis results in inappropriate treatment. Despite this potential clinical impact, diagnostic tools including artificial intelligence methods are not subjected to the same clinical evaluation standards as are mandatory for drugs. Here, we provide an evidence-based review of the pros and cons of commercially available automated methods for medical imaging diagnosis, including those based on artificial intelligence, to diagnose acute brain pathology on computed tomography or magnetic resonance imaging in patients with stroke.

Original languageEnglish
Pages (from-to)2393-2403
Number of pages11
JournalStroke
Volume53
Issue number7
Early online date20 Apr 2022
DOIs
Publication statusPublished - 1 Jul 2022

Keywords

  • stroke
  • machine learning
  • artificial intelligence
  • brain
  • perfusion

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