Machine learning-based patient classification system for adults with stroke: A systematic review

Ruksakulpiwat, Suebsarn; Thongking, Witchuda; Zhou, Wendie; Benjasirisan, Chitchanok; Phianhasin, Lalipat; Schiltz, Nicholas K.; Brahmbhatt, Smit

doi:10.1177/17423953211067435

Cited by 9 publications

(10 citation statements)

References 28 publications

(62 reference statements)

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“…The features are one of the essential combinations of a model as it demonstrates the correlation of the target variable of a model and attributes the appropriate of the model’s predictions and performance. 11 Overall, in nine included studies, a total of seven main features were included. Notably, one study can report using more than one feature.…”

Section: Resultsmentioning

confidence: 99%

“… 10 In light of ischemic stroke diagnosis, multiple types of ML have shown effectiveness in detecting ischemic stroke among adults. 11 One of the promising and powerful methods is neural networks, also known as artificial neural networks or simulated neural networks, which are a class of algorithms loosely modeled on connections between neurons in the brain. 12 As is evident from previous studies, the use of the neural networks algorithm is shown to improve the accuracy of several medical diagnoses, including cancer diseases such as lung cancer, skin cancer, and gastric cancer, or even time-sensitive diseases, such as coronary artery disease.…”

Section: Introductionmentioning

confidence: 99%

“… 13–16 However, the previous review pointed out that the neural networks algorithm helped improve accuracy in ischemic stroke classification and prediction but with limited research evidence. 11 …”

Section: Introductionmentioning

confidence: 99%

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

Ruksakulpiwat,

Phianhasin,

Benjasirisan

et al. 2023

JMDH

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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.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Ruksakulpiwat,

Phianhasin,

Benjasirisan

et al. 2023

JMDH

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“…In this study, the following ve machine learning algorithm models were constructed as follows: Support Vector Machine (SVM) [42] , k Nearest Neighbour (KNN) [43] , Random Forest (RF) [44] . Decision tree (TREE) [45] .…”

Section: Machine Learningmentioning

confidence: 99%

Functional connectivity and graph theory of impaired central visual pathways in acute ischemic stroke based on fMRI

Chu,

Xue,

et al. 2024

Preprint

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In the study of this paper, we first performed the analysis of whole brain static functional connectivity, we divided the whole brain into 90 regions of interest (ROIs) by applying AAL mapping, we compared the whole brain static functional connectivity analysis of the 14 patients and 26 healthy volunteers (HC) who completed the 3-months experiment (3months), the 14 patients and 26 healthy volunteers who completed the 7-days experiment (7days), the 12 patients and the 12 patients who completed the 7-days experiment (7days), the 12 patients and the 12 patients who completed the 3-months experiment (7days), and the 12 patients and 26 healthy volunteers ( HC), 14 patients who completed the 7-day experiment (7days), and 14 patients who completed the 3-month experiment (3months) were analysed for whole-brain static functional connectivity in all three groups, and 90 ROIs were mapped to the Yeo7 functional network for analysis. sFC analyses revealed significant alterations in the patients' VAN, and DMN networks. Secondly, we performed dynamic functional connectivity analysis based on AAL mapping with the sliding window method separately, and identified two dynamic functional connectivity pattern characteristics, i.e., state 1 with a connectivity pattern dominated by high-frequency weak connectivity, and state 2 with a connectivity pattern dominated by low-frequency strong connectivity.Stroke patients spent significantly more time in state 1, and the number of state switches of the stroke patients in 7days significantly higher and were more likely to switch to the low-frequency strong connectivity mode state 2. Significant changes in connectivity were observed for DMN, VIS, FPN, and LIM. Finally, we built five machine learning models based on SFC features that differ between groups, namely linear support vector machine (SVM), radial basis function support vector machine (SVM-RBF), k nearest neighbours (KNN), random forest (RF), and decision tree (TREE). Based on the maximum AUC we identified the optimal feature subset and found that the SFC within the VIS, DMN, and LIM networks contributed significantly to the classification of AIS patients and HCs alike.The variation of FC within the VIS, DMN, and LIM networks may provide new insights into the neural mechanisms of AIS patients.

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“…The result found that machine learning models have the potential to help healthcare providers with stroke diagnoses that can lead to early treatment and improve patient outcomes. 7 …”

Section: Introductionmentioning

confidence: 99%

Using ChatGPT in Medical Research: Current Status and Future Directions

Ruksakulpiwat¹,

Kumar²,

Ajibade³

2023

JMDH

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Objective This review aims to evaluate the current evidence on the use of the Generative Pre-trained Transformer (ChatGPT) in medical research, including but not limited to treatment, diagnosis, or medication provision. Methods This review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched Google Scholar, Web of Science, PubMed, and Medline to identify studies published between 2022 and 2023 that aimed to utilize ChatGPT in medical research. All identified references were stored in EndNote. Results We initially identified 114 articles, out of which six studies met the inclusion and exclusion criteria for full-text screening. Among the six studies, two focused on drug development (33.33%), two on literature review writing (33.33%), and one each on medical report improvement, provision of medical information, improving research conduct, data analysis, and personalized medicine (16.67% each). Conclusion ChatGPT has the potential to revolutionize medical research in various ways. However, its accuracy, originality, academic integrity, and ethical issues must be thoroughly discussed and improved before its widespread implementation in clinical research and medical practice.

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Machine learning-based patient classification system for adults with stroke: A systematic review

Cited by 9 publications

References 28 publications

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

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

Functional connectivity and graph theory of impaired central visual pathways in acute ischemic stroke based on fMRI

Using ChatGPT in Medical Research: Current Status and Future Directions

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