Journal of Multidisciplinary Healthcare (Sep 2023)

Using Neural Networks Algorithm in Ischemic Stroke Diagnosis: A Systematic Review

  • Ruksakulpiwat S,
  • Phianhasin L,
  • Benjasirisan C,
  • Schiltz NK

Journal volume & issue
Vol. Volume 16
pp. 2593 – 2602

Abstract

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Suebsarn Ruksakulpiwat,1 Lalipat Phianhasin,1 Chitchanok Benjasirisan,1 Nicholas K Schiltz2 1Department of Medical Nursing, Faculty of Nursing, Mahidol University, Bangkok, Thailand; 2Frances Payne Bolton School of Nursing, Case Western Reserve University, Cleveland, OH, USACorrespondence: Suebsarn Ruksakulpiwat, Department of Medical Nursing, Faculty of Nursing, Mahidol University, Bangkok, Thailand, Email [email protected]: To evaluate the evidence of artificial neural network (NNs) techniques in diagnosing ischemic stroke (IS) in adults.Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) was utilized as a guideline for this review. PubMed, MEDLINE, Web of Science, and CINAHL Plus Full Text were searched to identify studies published between 2018 and 2022, reporting using NNs in IS diagnosis. The Critical Appraisal Checklist for Diagnostic Test Accuracy Studies was adopted to evaluate the included studies.Results: Nine studies were included in this systematic review. Non-contrast computed tomography (NCCT) (n = 4 studies, 26.67%) and computed tomography angiography (CTA) (n = 4 studies, 26.67%) are among the most common features. Five algorithms were used in the included studies. Deep Convolutional Neural Networks (DCNNs) were commonly used for IS diagnosis (n = 3 studies, 33.33%). Other algorithms including three-dimensional convolutional neural networks (3D-CNNs) (n = 2 studies, 22.22%), two-stage deep convolutional neural networks (Two-stage DCNNs) (n = 2 studies, 22.22%), the local higher-order singular value decomposition denoising algorithm (GL-HOSVD) (n = 1 study, 11.11%), and a new deconvolution network model based on deep learning (AD-CNNnet) (n = 1 study, 11.11%) were also utilized for the diagnosis of IS.Conclusion: The number of studies ensuring the effectiveness of NNs algorithms in IS diagnosis has increased. Still, more feasibility and cost-effectiveness evaluations are needed to support the implementation of NNs in IS diagnosis in clinical settings.Keywords: neural networks, ischemic stroke, systematic review

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