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A Review on Computer Aided Diagnosis of Acute Brain Stroke

Inamdar MA 1 , Raghavendra U 2 , Gudigar A 2 , Chakole Y 2 , Hegde A 3 , Menon GR 3 Show all authors , Barua P 4 , Palmer EE 5 , Cheong KH 6 , Chan WY 7 , Ciaccio EJ 8 , Acharya UR 9

Affiliations 

  • 1 Department of Mechatronics, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India
  • 2 Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India
  • 3 Department of Neurosurgery, Kasturba Medical College, Manipal Academy of Higher Education, Manipal 576104, India
  • 4 School of Management & Enterprise, University of Southern Queensland, Toowoomba, QLD 4350, Australia
  • 5 School of Women's and Children's Health, University of New South Wales, Sydney, NSW 2052, Australia
  • 6 Science, Mathematics and Technology Cluster, Singapore University of Technology and Design, Singapore 487372, Singapore
  • 7 Department of Biomedical Imaging, Research Imaging Centre, University of Malaya, Kuala Lumpur 59100, Malaysia
  • 8 Department of Medicine, Columbia University, New York, NY 10032, USA
  • 9 Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia
Sensors (Basel), 2021 Dec 20;21(24).
PMID: 34960599 DOI: 10.3390/s21248507

Abstract

Amongst the most common causes of death globally, stroke is one of top three affecting over 100 million people worldwide annually. There are two classes of stroke, namely ischemic stroke (due to impairment of blood supply, accounting for ~70% of all strokes) and hemorrhagic stroke (due to bleeding), both of which can result, if untreated, in permanently damaged brain tissue. The discovery that the affected brain tissue (i.e., 'ischemic penumbra') can be salvaged from permanent damage and the bourgeoning growth in computer aided diagnosis has led to major advances in stroke management. Abiding to the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines, we have surveyed a total of 177 research papers published between 2010 and 2021 to highlight the current status and challenges faced by computer aided diagnosis (CAD), machine learning (ML) and deep learning (DL) based techniques for CT and MRI as prime modalities for stroke detection and lesion region segmentation. This work concludes by showcasing the current requirement of this domain, the preferred modality, and prospective research areas.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.

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