Ruksakulpiwat Suebsarn, Phianhasin Lalipat, Benjasirisan Chitchanok, Schiltz Nicholas K
Department of Medical Nursing, Faculty of Nursing, Mahidol University, Bangkok, Thailand.
Frances Payne Bolton School of Nursing, Case Western Reserve University, Cleveland, OH, USA.
J Multidiscip Healthc. 2023 Sep 1;16:2593-2602. doi: 10.2147/JMDH.S421280. eCollection 2023.
To evaluate the evidence of artificial neural network (NNs) techniques in diagnosing ischemic stroke (IS) in adults.
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.
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.
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.
评估人工神经网络（NNs）技术在诊断成人缺血性卒中（IS）方面的证据。
本综述以系统评价和Meta分析的首选报告项目（PRISMA）为指导方针。检索了PubMed、MEDLINE、科学网和CINAHL Plus全文数据库，以识别2018年至2022年间发表的使用NNs进行IS诊断的研究。采用诊断试验准确性研究的关键评估清单对纳入的研究进行评估。
本系统评价纳入了9项研究。非增强计算机断层扫描（NCCT）（4项研究，26.67%）和计算机断层血管造影（CTA）（4项研究，26.67%）是最常见的特征。纳入的研究使用了5种算法。深度卷积神经网络（DCNNs）常用于IS诊断（3项研究，33.33%）。其他算法包括三维卷积神经网络（3D-CNNs）（2项研究，22.22%）、两阶段深度卷积神经网络（两阶段DCNNs）（2项研究，22.22%）、局部高阶奇异值分解去噪算法（GL-HOSVD）（1项研究，11.11%）以及基于深度学习的新反卷积网络模型（AD-CNNnet）（1项研究，11.11%）也被用于IS的诊断。
确保NNs算法在IS诊断中有效性的研究数量有所增加。然而，仍需要更多的可行性和成本效益评估来支持在临床环境中使用NNs进行IS诊断。
