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Updated: Jul 17, 2025

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
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Using Neural Networks Algorithm in Ischemic Stroke Diagnosis: A Systematic Review.

Suebsarn Ruksakulpiwat1, Lalipat Phianhasin1, Chitchanok Benjasirisan1

  • 1Department of Medical Nursing, Faculty of Nursing, Mahidol University, Bangkok, Thailand.

Journal of Multidisciplinary Healthcare
|September 7, 2023
PubMed
Summary
This summary is machine-generated.

Artificial neural networks (NNs) show increasing evidence for diagnosing ischemic stroke (IS). Further research is needed to confirm their clinical feasibility and cost-effectiveness for widespread adoption.

Keywords:
ischemic strokeneural networkssystematic review

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Neurology

Background:

  • Ischemic stroke (IS) diagnosis relies on advanced imaging techniques.
  • Artificial neural networks (NNs) offer potential for improving diagnostic accuracy.

Purpose of the Study:

  • To systematically review the evidence on the effectiveness of artificial neural networks (NNs) for diagnosing ischemic stroke (IS) in adults.

Main Methods:

  • A systematic review following PRISMA guidelines.
  • Searched PubMed, MEDLINE, Web of Science, and CINAHL Plus Full Text (2018-2022).
  • Evaluated studies using the Critical Appraisal Checklist for Diagnostic Test Accuracy Studies.

Main Results:

  • Nine studies were included, focusing on Non-contrast computed tomography (NCCT) and computed tomography angiography (CTA).
  • Deep Convolutional Neural Networks (DCNNs) were the most utilized algorithm (33.33%).
  • Other algorithms included 3D-CNNs, Two-stage DCNNs, GL-HOSVD, and AD-CNNnet.

Conclusions:

  • Evidence supporting NNs for IS diagnosis is growing.
  • Further studies on feasibility and cost-effectiveness are required for clinical implementation.