The Simplified mRS Questionnaire Reflects Stroke Severity

Bruno, Askiel; Close, Brian; Gomadam, Arvind; Akinwuntan, Abiodun Emmanuel; Switzer, Jeffrey A.

doi:10.1111/ijs.12113

Cited by 3 publications

(2 citation statements)

References 6 publications

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“… 85 , 86 Modified Rankin scale (mRS) is a clinical tool that assess the functional status of the patient. 87 It is a 5-point scale with worsening a status while the score is increasing. 88 Hoa et al 89 built a CNN to predict mRS as a dichotomized target with cutoff score of 2 on DWI.…”

Section: Clinical and Research Applicationsmentioning

confidence: 99%

Artificial Intelligence in Stroke Imaging: A Comprehensive Review

Koska,

Selver

2024

The Eurasian Journal of Medicine

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The aging population challenges the health-care system with chronic diseases. Cerebrovascular diseases are important components of these chronic conditions. Stroke is the acute cessation of blood in the brain, which can lead to rapid tissue loss. Therefore, fast, accurate, and reliable automatic methods are required to facilitate stroke management. The performance of artificial intelligence (AI) methods is increasing in all domains. Vision tasks, including natural images and medical images, are particularly benefiting from the skills of AI models. The AI methods that can be applied to stroke imaging have a broad range, including classical machine learning tools such as support vector machines, random forests, logistic regression, and linear discriminant analysis, as well as deep learning models, such as convolutional neural networks, recurrent neural networks, autoencoders, and U-Net. Both tools can be applied to various aspects of stroke management, including time-to-event onset determination, stroke confirmation, large vessel occlusion detection, diffusion restriction, perfusion deficit, core and penumbra identification, affected region segmentation, and functional outcome prediction. While building these AI models, maximum care should be exercised in order to reduce bias and build generalizable models. One of the most important prerequisites for building unbiased models is collecting large, diverse, and quality data that reflects the underlying population well and splitting the training and testing parts in a way that both represent a similar distribution. Explainability and trustworthiness are other important properties of machine learning models that could be widely adopted in clinical practices.

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Section: Clinical and Research Applicationsmentioning

confidence: 99%

Artificial Intelligence in Stroke Imaging: A Comprehensive Review

Koska,

Selver

2024

The Eurasian Journal of Medicine

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“…It can guide clinicians in advising patients and relatives about possible outcomes. The modified Rankin scale (mRS) is usually assessed within 90 days after stroke onset and has represented final clinical outcomes in several clinical trials [ 105 ]. It is often used to evaluate the prognosis of stroke and to determine the curative effect of the functional disability of patients during rehabilitation.…”

Section: Clinical Applications Of Deep Learning In Aismentioning

confidence: 99%

Deep Learning in Ischemic Stroke Imaging Analysis: A Comprehensive Review

Cui

Fan

Yang

et al. 2022

BioMed Research International

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Ischemic stroke is a cerebrovascular disease with a high morbidity and mortality rate, which poses a serious challenge to human health and life. Meanwhile, the management of ischemic stroke remains highly dependent on manual visual analysis of noncontrast computed tomography (CT) or magnetic resonance imaging (MRI). However, artifacts and noise of the equipment as well as the radiologist experience play a significant role on diagnostic accuracy. To overcome these defects, the number of computer-aided diagnostic (CAD) methods for ischemic stroke is increasing substantially during the past decade. Particularly, deep learning models with massive data learning capabilities are recognized as powerful auxiliary tools for the acute intervention and guiding prognosis of ischemic stroke. To select appropriate interventions, facilitate clinical practice, and improve the clinical outcomes of patients, this review firstly surveys the current state-of-the-art deep learning technology. Then, we summarized the major applications in acute ischemic stroke imaging, particularly in exploring the potential function of stroke diagnosis and multimodal prognostication. Finally, we sketched out the current problems and prospects.

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The positive effects of Xueshuan Xinmai tablets on brain functional connectivity in acute ischemic stroke: a placebo controlled randomized trial

Wei

Xie

Chen

et al. 2017

Sci Rep

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Through a placebo controlled randomized study, the purpose of this report was to investigate the effects of Xueshuan Xinmai tablets (XXMT) on neurologic deficits, quality of life and brain functional connectivity in acute ischemic stroke patients and to explore the mechanism of action of XXMT. In total, 44 acute ischemic stroke patients were randomly divided to the XXMT treatment group (n = 22) or the placebo group (n = 22) in a 2-week trial. Before and after the treatment, the neurological assessment and functional magnetic resonance imaging examinations were carried out. Compared to the placebo group, the scores of the National Institutes of Health Stroke Scale (NIHSS) and Stroke-Specific Quality of Life Scale (SSQOL) significantly improved in the treatment group. In addition, XXMT-treated patients demonstrated significantly enhanced functional connectivity within the default mode, frontal-parietal, and motor control networks. Furthermore, the changed connectivity in the left precuneus was positively correlated to the improvement of NIHSS and SSQOL scores. The present study indicated that XXMT treatment significantly improved the neurologic deficit and quality of life of acute ischemic stroke patients and that the therapeutic effect may be based on the modulation of XXMT on the functional connectivity of brain networks.

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The Simplified mRS Questionnaire Reflects Stroke Severity

Cited by 3 publications

References 6 publications

Artificial Intelligence in Stroke Imaging: A Comprehensive Review

Artificial Intelligence in Stroke Imaging: A Comprehensive Review

Deep Learning in Ischemic Stroke Imaging Analysis: A Comprehensive Review

The positive effects of Xueshuan Xinmai tablets on brain functional connectivity in acute ischemic stroke: a placebo controlled randomized trial

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