Artificial intelligence in stroke imaging: Current and future perspectives

Vivek S. Yedavalli, Elizabeth Tong, Dann Martin, Kristen W. Yeom, Nils D. Forkert

Research output: Contribution to journalReview articlepeer-review


Artificial intelligence (AI) is a fast-growing research area in computer science that aims to mimic cognitive processes through a number of techniques. Supervised machine learning, a subfield of AI, includes methods that can identify patterns in high-dimensional data using labeled ‘ground truth’ data and apply these learnt patterns to analyze, interpret, or make predictions on new datasets. Supervised machine learning has become a significant area of interest within the medical community. Radiology and neuroradiology in particular are especially well suited for application of machine learning due to the vast amount of data that is generated. One devastating disease for which neuroimaging plays a significant role in the clinical management is stroke. Within this context, AI techniques can play pivotal roles for image-based diagnosis and management of stroke. This overview focuses on the recent advances of artificial intelligence methods – particularly supervised machine learning and deep learning – with respect to workflow, image acquisition and reconstruction, and image interpretation in patients with acute stroke, while also discussing potential pitfalls and future applications.

Original languageEnglish (US)
Pages (from-to)246-254
Number of pages9
JournalClinical Imaging
StatePublished - Jan 2021


  • Image optimization and analysis
  • Perfusion imaging
  • Stroke
  • Supervised artificial intelligence

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging


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