Identification of early invisible acute ischemic stroke in non-contrast computed tomography using two-stage deep-learning model

Zhou, Yiran; Lv, Wenzhi; Zhu, Hongqing; Tian, Tian; Su, Yan; Xie, Yuan; Wu, Di; Li, Yuanhao; Liu, Yuanyuan; Gao, Luyue; Wang, Fan; Ye, Nan; Zhang, Shun; Peng, Xiaolong; Zhang, Guiling; Zhu, Wenzhen

doi:10.7150/thno.74125

Cited by 13 publications

(6 citation statements)

References 25 publications

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“…25 The study suggested that the detection system involving two-stage DCNNs could significantly improve radiologists' sensitivity in detecting AIS, similar to a study by Lu et al, who found that using NCCT features in two-stage DCNNs to identify AIS shows high diagnostic value with AUC of 83.61%, sensitivity = 68.99%, specificity = 98.22%, and accuracy = 89.87%. 31 Our results are consistent with previous studies that used two-stage DCNNs models for lung nodule detection 33 and breast cancer classification. 34 The result showed high accuracy and competitiveness compared to existing traditional methods.…”

supporting

confidence: 92%

“… 25 The study suggested that the detection system involving two-stage DCNNs could significantly improve radiologists’ sensitivity in detecting AIS, similar to a study by Lu et al, who found that using NCCT features in two-stage DCNNs to identify AIS shows high diagnostic value with AUC of 83.61%, sensitivity = 68.99%, specificity = 98.22%, and accuracy = 89.87%. 31 …”

Section: Discussionmentioning

confidence: 99%

“…Two-stage deep convolutional neural networks or two-stage DCNNs models were used in two of our included studies. 25,31 The research from Nishio et al developed and evaluated an automatic acute ischemic stroke (AIS) detection system utilizing CT and MRI image features. 25 The result showed that a board-certified radiologist combined with two-stage DCNNs models had improved AIS detection (sensitivity = 41.3%, precision = 62%, false positive per one case = 0.388).…”

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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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supporting

confidence: 92%

Section: Discussionmentioning

confidence: 99%

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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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“…The detection of subtle findings that can otherwise be missed by physicians encompasses the most promising aspects of AI (Nishio et al, 2020;Kaothanthong et al, 2022;Lu et al, 2022). To aid in the detection of "invisible" acute IS, Lu and colleagues developed a deeplearning model comprised of two deep CNNs.…”

Section: Detection Of Acute Ischemic Strokementioning

confidence: 99%

Advances in Artificial Intelligence, Robotics, Augmented and Virtual Reality in Neurosurgery

Mofatteh,

Bydon,

Ali Alvi

2024

Frontiers Research Topics

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Frontiers is more than just an open access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals. Frontiers journal seriesThe Frontiers journal series is a multi-tier and interdisciplinary set of openaccess, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. All Frontiers journals are driven by researchers for researchers; therefore, they constitute a service to the scholarly community. At the same time, the Frontiers journal series operates on a revolutionary invention, the tiered publishing system, initially addressing specific communities of scholars, and gradually climbing up to broader public understanding, thus serving the interests of the lay society, too. Dedication to qualityEach Frontiers article is a landmark of the highest quality, thanks to genuinely collaborative interactions between authors and review editors, who include some of the world's best academicians. Research must be certified by peers before entering a stream of knowledge that may eventually reach the public -and shape society; therefore, Frontiers only applies the most rigorous and unbiased reviews. Frontiers revolutionizes research publishing by freely delivering the most outstanding research, evaluated with no bias from both the academic and social point of view. By applying the most advanced information technologies, Frontiers is catapulting scholarly publishing into a new generation. What are Frontiers Research Topics?Frontiers Research Topics are very popular trademarks of the Frontiers journals series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area.

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Section: Detection Of Acute Ischemic Strokementioning

confidence: 99%

Role of artificial intelligence and machine learning in the diagnosis of cerebrovascular disease

Gilotra,

Swarna,

Mani

et al. 2023

Front. Hum. Neurosci.

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IntroductionCerebrovascular diseases are known to cause significant morbidity and mortality to the general population. In patients with cerebrovascular disease, prompt clinical evaluation and radiographic interpretation are both essential in optimizing clinical management and in triaging patients for critical and potentially life-saving neurosurgical interventions. With recent advancements in the domains of artificial intelligence (AI) and machine learning (ML), many AI and ML algorithms have been developed to further optimize the diagnosis and subsequent management of cerebrovascular disease. Despite such advances, further studies are needed to substantively evaluate both the diagnostic accuracy and feasibility of these techniques for their application in clinical practice. This review aims to analyze the current use of AI and MI algorithms in the diagnosis of, and clinical decision making for cerebrovascular disease, and to discuss both the feasibility and future applications of utilizing such algorithms.MethodsWe review the use of AI and ML algorithms to assist clinicians in the diagnosis and management of ischemic stroke, hemorrhagic stroke, intracranial aneurysms, and arteriovenous malformations (AVMs). After identifying the most widely used algorithms, we provide a detailed analysis of the accuracy and effectiveness of these algorithms in practice.ResultsThe incorporation of AI and ML algorithms for cerebrovascular patients has demonstrated improvements in time to detection of intracranial pathologies such as intracerebral hemorrhage (ICH) and infarcts. For ischemic and hemorrhagic strokes, commercial AI software platforms such as RapidAI and Viz.AI have bene implemented into routine clinical practice at many stroke centers to expedite the detection of infarcts and ICH, respectively. Such algorithms and neural networks have also been analyzed for use in prognostication for such cerebrovascular pathologies. These include predicting outcomes for ischemic stroke patients, hematoma expansion, risk of aneurysm rupture, bleeding of AVMs, and in predicting outcomes following interventions such as risk of occlusion for various endovascular devices. Preliminary analyses have yielded promising sensitivities when AI and ML are used in concert with imaging modalities and a multidisciplinary team of health care providers.ConclusionThe implementation of AI and ML algorithms to supplement clinical practice has conferred a high degree of accuracy, efficiency, and expedited detection in the clinical and radiographic evaluation and management of ischemic and hemorrhagic strokes, AVMs, and aneurysms. Such algorithms have been explored for further purposes of prognostication for these conditions, with promising preliminary results. Further studies should evaluate the longitudinal implementation of such techniques into hospital networks and residency programs to supplement clinical practice, and the extent to which these techniques improve patient care and clinical outcomes in the long-term.

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Identification of early invisible acute ischemic stroke in non-contrast computed tomography using two-stage deep-learning model

Cited by 13 publications

References 25 publications

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

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

Advances in Artificial Intelligence, Robotics, Augmented and Virtual Reality in Neurosurgery

Role of artificial intelligence and machine learning in the diagnosis of cerebrovascular disease

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