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Review
Deep learning guided stroke management: a review of clinical applications
  1. Rui Feng1,
  2. Marcus Badgeley2,
  3. J Mocco1,
  4. Eric K Oermann1
  1. 1Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, USA
  2. 2Icahn School of Medicine at Mount Sinai, New York, USA
  1. Correspondence to Rui Feng, Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, One Gustav Levy Place, New York, NY 10029, USA; rui.feng{at}icahn.mssm.edu

Abstract

Stroke is a leading cause of long-term disability, and outcome is directly related to timely intervention. Not all patients benefit from rapid intervention, however. Thus a significant amount of attention has been paid to using neuroimaging to assess potential benefit by identifying areas of ischemia that have not yet experienced cellular death. The perfusion–diffusion mismatch, is used as a simple metric for potential benefit with timely intervention, yet penumbral patterns provide an inaccurate predictor of clinical outcome. Machine learning research in the form of deep learning (artificial intelligence) techniques using deep neural networks (DNNs) excel at working with complex inputs. The key areas where deep learning may be imminently applied to stroke management are image segmentation, automated featurization (radiomics), and multimodal prognostication. The application of convolutional neural networks, the family of DNN architectures designed to work with images, to stroke imaging data is a perfect match between a mature deep learning technique and a data type that is naturally suited to benefit from deep learning’s strengths. These powerful tools have opened up exciting opportunities for data-driven stroke management for acute intervention and for guiding prognosis. Deep learning techniques are useful for the speed and power of results they can deliver and will become an increasingly standard tool in the modern stroke specialist’s arsenal for delivering personalized medicine to patients with ischemic stroke.

  • stroke
  • intervention
  • thrombectomy
  • ct perfusion
  • technology

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Footnotes

  • Contributors RF and EKO contributed to the conception and drafting of the manuscript. All authors contributed to the critical revision and approval of the final manuscript.

  • Competing interests None declared.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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