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Specialty: Neurology
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Total 1309 results found since Jan 2013.
Correction: Risk prediction of 30-day mortality after stroke using machine learning: a nationwide registry-based cohort study
Source: BMC Neurology - August 25, 2022 Category: Neurology Authors: Wenjuan Wang, Anthony G. Rudd, Yanzhong Wang, Vasa Curcin, Charles D. Wolfe, Niels Peek and Benjamin Bray Tags: Correction Source Type: research
Ischemic and haemorrhagic stroke risk estimation using a machine-learning-based retinal image analysis
ConclusionA fast and fully automatic method can be used for stroke subtype risk assessment and classification based on fundus photographs alone.
Source: Frontiers in Neurology - August 22, 2022 Category: Neurology Source Type: research
Machine learning to predict futile recanalization of large vessel occlusion before and after endovascular thrombectomy
ConclusionsThe “Early” XGBoost and the “Late” XGBoost allowed us to predict futile recanalization before and after EVT accurately. Our study suggests that including peri-interventional characteristics may lead to superior predictive performance compared to a model based on baseline characteristics only. In addition, NIHSS after 24 h was the most important prognostic factor for futile recanalization.
Source: Frontiers in Neurology - August 19, 2022 Category: Neurology Source Type: research
Bimanual motor skill learning after stroke: Combining robotics and anodal tDCS over the undamaged hemisphere: An exploratory study
ConclusionA short motor skill learning session with a robotic device resulted in the retention and generalization of a complex skill involving bimanual cooperation. The tDCS strategy that would best enhance bimanual motor skill learning after stroke remains unknown.Clinical trial registrationhttps://clinicaltrials.gov/ct2/show/NCT02308852, identifier: NCT02308852.
Source: Frontiers in Neurology - August 18, 2022 Category: Neurology Source Type: research
Machine learning based reanalysis of clinical scores for distinguishing between ischemic and hemorrhagic stroke in low resource setting
Identifying ischemic or hemorrhagic strokes clinically may help in situations where neuroimaging is unavailable to provide primary-care prior to referring to stroke-ready facility. Stroke classification-based solely on clinical scores faces two unresolved issues. One pertains to overestimation of score performance, while other is biased performance due to class-imbalance inherent in stroke datasets. After correcting the issues using Machine Learning theory, we quantitatively compared existing scores to study the capabilities of clinical attributes for stroke classification.
Source: Journal of Stroke and Cerebrovascular Diseases - August 1, 2022 Category: Neurology Authors: Aman Bhardwaj, MV Padma Srivastava, Pulikottil Vinny Wilson, Amit Mehndiratta, Venugopalan Y Vishnu, Rahul Garg Source Type: research
Deep Convolution Generative Adversarial Network-Based Electroencephalogram Data Augmentation for Post-Stroke Rehabilitation with Motor Imagery
Int J Neural Syst. 2022 Jul 25:2250039. doi: 10.1142/S0129065722500393. Online ahead of print.ABSTRACTThe motor imagery brain-computer interface (MI-BCI) system is currently one of the most advanced rehabilitation technologies, and it can be used to restore the motor function of stroke patients. The deep learning algorithms in the MI-BCI system require lots of training samples, but the electroencephalogram (EEG) data of stroke patients is quite scarce. Therefore, the expansion of EEG data has become an important part of stroke clinical rehabilitation research. In this paper, a deep convolution generative adversarial networ...
Source: International Journal of Neural Systems - July 26, 2022 Category: Neurology Authors: Fangzhou Xu Gege Dong Jincheng Li Qingbo Yang Lei Wang Yanna Zhao Yihao Yan Jinzhao Zhao Shaopeng Pang Dongju Guo Yang Zhang Jiancai Leng Source Type: research
Collateral-Core Ratio as a Novel Predictor of Clinical Outcomes in Acute Ischemic Stroke
AbstractThe interaction effect between collateral circulation and ischemic core size on stroke outcomes has been highlighted in acute ischemic stroke (AIS). However, biomarkers that assess the magnitude of this interaction are still lacking. We aimed to present a new imaging marker, the collateral-core ratio (CCR), to quantify the interaction effect between these factors and evaluate its ability to predict functional outcomes using machine learning (ML) in AIS. Patients with AIS caused by anterior circulation large vessel occlusion (LVO) were recruited from a prospective multicenter study. CCR was calculated as collateral ...
Source: Translational Stroke Research - July 25, 2022 Category: Neurology Source Type: research
Editorial: Machine Learning in Action: Stroke Diagnosis and Outcome Prediction
Source: Frontiers in Neurology - July 20, 2022 Category: Neurology Source Type: research
Remote Training of Neurointerventions by Audiovisual Streaming
ConclusionOnline streaming technology facilitates location-independent training of complex neurointerventional procedures through high levels of situational awareness and can therefore supplement live hands-on-training. In addition, it leads to a training effect for fellows with a perceived improvement of their neurointerventional knowledge.
Source: Clinical Neuroradiology - July 13, 2022 Category: Neurology Source Type: research
Machine learning algorithms identify demographics, dietary features, and blood biomarkers associated with stroke records
We conducted a comprehensive evaluation of features associated with stroke records.
Source: Journal of the Neurological Sciences - July 8, 2022 Category: Neurology Authors: Jundong Liu, Elizabeth L. Chou, Kui Kai Lau, Peter Y.M. Woo, Jun Li, Kei Hang Katie Chan Source Type: research
