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Using Neural Networks Algorithm in Ischemic Stroke Diagnosis: A Systematic Review
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 C...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Dove
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10478777/ https://www.ncbi.nlm.nih.gov/pubmed/37674890 http://dx.doi.org/10.2147/JMDH.S421280 |
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author | Ruksakulpiwat, Suebsarn Phianhasin, Lalipat Benjasirisan, Chitchanok Schiltz, Nicholas K |
author_facet | Ruksakulpiwat, Suebsarn Phianhasin, Lalipat Benjasirisan, Chitchanok Schiltz, Nicholas K |
author_sort | Ruksakulpiwat, Suebsarn |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10478777 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Dove |
record_format | MEDLINE/PubMed |
spelling | pubmed-104787772023-09-06 Using Neural Networks Algorithm in Ischemic Stroke Diagnosis: A Systematic Review Ruksakulpiwat, Suebsarn Phianhasin, Lalipat Benjasirisan, Chitchanok Schiltz, Nicholas K J Multidiscip Healthc Review 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. Dove 2023-09-01 /pmc/articles/PMC10478777/ /pubmed/37674890 http://dx.doi.org/10.2147/JMDH.S421280 Text en © 2023 Ruksakulpiwat et al. https://creativecommons.org/licenses/by-nc/3.0/This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/ (https://creativecommons.org/licenses/by-nc/3.0/) ). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php). |
spellingShingle | Review Ruksakulpiwat, Suebsarn Phianhasin, Lalipat Benjasirisan, Chitchanok Schiltz, Nicholas K Using Neural Networks Algorithm in Ischemic Stroke Diagnosis: A Systematic Review |
title | Using Neural Networks Algorithm in Ischemic Stroke Diagnosis: A Systematic Review |
title_full | Using Neural Networks Algorithm in Ischemic Stroke Diagnosis: A Systematic Review |
title_fullStr | Using Neural Networks Algorithm in Ischemic Stroke Diagnosis: A Systematic Review |
title_full_unstemmed | Using Neural Networks Algorithm in Ischemic Stroke Diagnosis: A Systematic Review |
title_short | Using Neural Networks Algorithm in Ischemic Stroke Diagnosis: A Systematic Review |
title_sort | using neural networks algorithm in ischemic stroke diagnosis: a systematic review |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10478777/ https://www.ncbi.nlm.nih.gov/pubmed/37674890 http://dx.doi.org/10.2147/JMDH.S421280 |
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