Using Neural Networks Algorithm in Ischemic Stroke Diagnosis: A Systematic Review
Issued Date
2023-01-01
Resource Type
eISSN
11782390
Scopus ID
2-s2.0-85170250646
Journal Title
Journal of Multidisciplinary Healthcare
Volume
16
Start Page
2593
End Page
2602
Rights Holder(s)
SCOPUS
Bibliographic Citation
Journal of Multidisciplinary Healthcare Vol.16 (2023) , 2593-2602
Suggested Citation
Ruksakulpiwat S., Phianhasin L., Benjasirisan C., Schiltz N.K. Using Neural Networks Algorithm in Ischemic Stroke Diagnosis: A Systematic Review. Journal of Multidisciplinary Healthcare Vol.16 (2023) , 2593-2602. 2602. doi:10.2147/JMDH.S421280 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/90048
Title
Using Neural Networks Algorithm in Ischemic Stroke Diagnosis: A Systematic Review
Author's Affiliation
Other Contributor(s)
Abstract
Objective: 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.