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Source: Journal of Stroke and Cerebrovascular Diseases
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Total 47 results found since Jan 2013.
Deep Learning-Based Approach for the Diagnosis of Moyamoya Disease
Moyamoya disease is a unique cerebrovascular disorder that is characterized by chronic progressive bilateral stenosis of the terminal portion of the internal carotid arteries (ICAs), and it is associated with the formation of an abnormal vascular network at the base of the brain.1,2 For the diagnosis of the moyamoya disease, digital subtraction angiography (DSA), which helps evaluate collateral circulation from the view point of the hemorrhagic risk, is the gold standard.3,4 On the contrary, magnetic resonance imaging (MRI) and magnetic resonance angiography (MRA) can be used as alternatives to conventional angiography bec...
Source: Journal of Stroke and Cerebrovascular Diseases - September 25, 2020 Category: Neurology Authors: Yukinori Akiyama, Takeshi Mikami, Nobuhiro Mikuni Source Type: research
Radiomic Model for Distinguishing Dissecting Aneurysms from Complicated Saccular Aneurysms on high-Resolution Magnetic Resonance Imaging
To build radiomic model in differentiating dissecting aneurysm (DA) from complicated saccular aneurysm (SA) based on high-resolution magnetic resonance imaging (HR-MRI) through machine-learning algorithm.
Source: Journal of Stroke and Cerebrovascular Diseases - September 8, 2020 Category: Neurology Authors: Xin Cao, Wei Xia, Ye Tang, Bo Zhang, Jinming Yang, Yanwei Zeng, Daoying Geng, Jun Zhang Source Type: research
Toward automatic evaluation of medical abstracts: The current value of sentiment analysis and machine learning for classification of the importance of PubMed abstracts of randomized trials for stroke
The exponential growth of the number of medical publications makes it more and more difficult for the clinical practitioner to keep track of relevant scientific progress. Computer-aided screening and analysis might help to overview and classify the content of medical publications more efficiently. The new wave of enthusiasm for machine learning algorithms has launched several new possibilities to analyze and categorize documents, ranging from sports news to hospital discharge summaries. Search algorithms for the medical literature, i.e.
Source: Journal of Stroke and Cerebrovascular Diseases - August 15, 2020 Category: Neurology Authors: Igor Fischer, Hans-Jakob Steiger Source Type: research
Machine Learning for Brain Stroke: A Review
Machine Learning (ML) delivers an accurate and quick prediction outcome and it has become a powerful tool in health settings, offering personalized clinical care for stroke patients. An application of ML and Deep Learning in health care is growing however, some research areas do not catch enough attention for scientific investigation though there is real need of research. Therefore, the aim of this work is to classify state-of-arts on ML techniques for brain stroke into 4 categories based on their functionalities or similarity, and then review studies of each category systematically.
Source: Journal of Stroke and Cerebrovascular Diseases - August 11, 2020 Category: Neurology Authors: Manisha Sanjay Sirsat, Eduardo Ferm é, Joana Câmara Tags: Review Article Source Type: research
Vascular Cellular Adhesion Molecule-1 (VCAM-1) and Memory Impairment in African-Americans after Small Vessel-Type Stroke
We examined whether inflammatory/endothelial dysfunction biomarkers are associated with cognition after SVS in AAs. Methods: Biomarkers were obtained in 24 subjects (median age 56.5 years, 54% women, median 12 years education). Cognition was assessed more than 6 weeks poststroke using the memory composite score (MCS), which was generated using recall from the Hopkins Verbal Learning Test-II and Brief Visuospatial Memory Test-Revised.
Source: Journal of Stroke and Cerebrovascular Diseases - February 13, 2020 Category: Neurology Authors: Nada El Husseini, Cheryl Bushnell, Candice M. Brown, Deborah Attix, Natalia S. Rost, Gregory P. Samsa, Carol A. Colton, Larry B. Goldstein Source Type: research
Automating Ischemic Stroke Subtype Classification Using Machine Learning and Natural Language Processing
This study sought to use natural language processing of electronic health records (EHR) combined with machine learning methods to automate IS subtyping. Methods: Among IS patients from an observational registry with TOAST subtyping adjudicated by board-certified vascular neurologists, we analyzed unstructured text-based EHR data including neurology progress notes and neuroradiology reports using natural language processing.
Source: Journal of Stroke and Cerebrovascular Diseases - May 14, 2019 Category: Neurology Authors: Ravi Garg, Elissa Oh, Andrew Naidech, Konrad Kording, Shyam Prabhakaran Source Type: research
Effect of Stride Management Assist Gait Training for Poststroke Hemiplegia: A Single Center, Open-Label, Randomized Controlled Trial
Poststroke gait disorders negatively impact activities of daily living. Rehabilitation for stroke patients is aimed at improving their walking ability, balance, and quality of life. Robot-assisted gait training (RAGT) is associated with an increased number of task-specific exercises, which may benefit poststroke motor learning. We investigated the effects of RAGT using Stride Management Assist (SMA, which increases walk ratio by inducing hip-joint flexion and extension) in subacute stroke patients with hemiplegia.
Source: Journal of Stroke and Cerebrovascular Diseases - November 9, 2018 Category: Neurology Authors: Naojiro Tanaka, Shinro Matsushita, Yasushi Sonoda, Yoshikatsu Maruta, Yuta Fujitaka, Masashi Sato, Miki Simomori, Rhyuki Onaka, Keiji Harada, Takashi Hirata, Shoji Kinoshita, Takatsugu Okamoto, Hitoshi Okamura Source Type: research
Corrigendum to “Predicting Motor and Cognitive Improvement Through Machine Learning Algorithm in Human Subject That Underwent a Rehabilitation Treatment in the Early Stage of Stroke” J Stroke Cerebrovasc Dis 27/11 (2018) 2962-2972
The authors would like to inform the readers of a needed clarification in this article.
Source: Journal of Stroke and Cerebrovascular Diseases - October 24, 2018 Category: Neurology Authors: Patrizio Sale, Giorgio Ferriero, Lucio Ciabattoni, Anna Maria Cortese, Giovanni Gentile, Francesco Ferracuti, Luca Romeo, Francesco Piccione, Stefano Masiero Tags: Corrigendum Source Type: research
Improving the Accuracy of Scores to Predict Gastrostomy after Intracerebral Hemorrhage with Machine Learning
Gastrostomy placement after intracerebral hemorrhage indicates the need for continued medical care and predicts patient dependence. Our objective was to determine the optimal machine learning technique to predict gastrostomy.
Source: Journal of Stroke and Cerebrovascular Diseases - September 7, 2018 Category: Neurology Authors: Ravi Garg, Shyam Prabhakaran, Jane L. Holl, Yuan Luo, Roland Faigle, Konrad Kording, Andrew M. Naidech Source Type: research
Predicting Motor and Cognitive Improvement Through Machine Learning Algorithm in Human Subject that Underwent a Rehabilitation Treatment in the Early Stage of Stroke
The objective of this study was to investigate, in subject with stroke, the exact role as prognostic factor of common inflammatory biomarkers and other markers in predicting motor and/or cognitive improvement after rehabilitation treatment from early stage of stroke.
Source: Journal of Stroke and Cerebrovascular Diseases - August 2, 2018 Category: Neurology Authors: Patrizio Sale, Giorgio Ferriero, Lucio Ciabattoni, Anna Maria Cortese, Francesco Ferracuti, Luca Romeo, Francesco Piccione, Stefano Masiero Source Type: research
Effect of Telmisartan on Preventing Learning and Memory Deficits Via Peroxisome Proliferator-Activated Receptor- γ in Vascular Dementia Spontaneously Hypertensive Rats
This study aimed to explore the effect of telmisartan (TEL), as a partial peroxisome proliferator-activated receptor- γ (PPAR-γ) agonist, in vascular dementia (VaD) rats induced by middle cerebral artery occlusion (MCAO).
Source: Journal of Stroke and Cerebrovascular Diseases - December 11, 2017 Category: Neurology Authors: Yuan Gao, Wei Li, Yali Liu, Yan Wang, Jianchao Zhang, Miao Li, Mengsen Bu Source Type: research
Virtual Rehabilitation through Nintendo Wii in Poststroke Patients: Follow-Up
To evaluate in the follow-up the sensory-motor recovery and quality of life patients 2 months after completion of the Nintendo Wii console intervention and determine whether learning retention was obtained through the technique.
Source: Journal of Stroke and Cerebrovascular Diseases - October 31, 2017 Category: Neurology Authors: Adriani A. Carregosa, Luan Rafael Aguiar dos Santos, Marcelo R. Masruha, Mar ília Lira da S. Coêlho, Tácia C. Machado, Daniele Costa B. Souza, Gustavo Luan L. Passos, Erika P. Fonseca, Nildo Manoel da S. Ribeiro, Ailton de Souza Melo Source Type: research
Measuring Functional Arm Movement after Stroke Using a Single Wrist-Worn Sensor and Machine Learning
We present a novel, inexpensive, and feasible method for separating UE functional use from nonfunctional movement after stroke using a single wrist-worn accelerometer.
Source: Journal of Stroke and Cerebrovascular Diseases - August 4, 2017 Category: Neurology Authors: Elaine M. Bochniewicz, Geoff Emmer, Adam McLeod, Jessica Barth, Alexander W. Dromerick, Peter Lum Source Type: research
Methodology of the Stroke Self-Management Rehabilitation Trial: An International, Multisite Pilot Trial
Stroke is a major cause of long-term adult disability with many survivors living in the community relying on family members for on-going support. However, reports of inadequate understanding of rehabilitation techniques are common. A self-management DVD-based observational learning tool may help improve functional outcomes for survivors of stroke and reduce caregivers' burden.
Source: Journal of Stroke and Cerebrovascular Diseases - December 9, 2014 Category: Neurology Authors: Kelly M. Jones, Rohit Bhattacharjee, Rita Krishnamurthi, Sarah Blanton, Alice Theadom, Suzanne Barker-Collo, Amanda Thrift, Priya Parmar, Annick Maujean, Annemarei Ranta, Emmanuel Sanya, Valery L. Feigin, SMART Study Group Source Type: research
Initial Experience with Upfront Arterial and Perfusion Imaging among Ischemic Stroke Patients Presenting within the 4.5-hour Time Window
Conclusions: An upfront CTA/CTP protocol aided stroke team decision-making in nearly half of cases. Implementation of a CTA/CTP protocol was associated with a learning curve of 6 months before door to needle time ≤60 minutes returned to similar rates as the pre-CTA/CTP protocol.
Source: Journal of Stroke and Cerebrovascular Diseases - January 24, 2013 Category: Neurology Authors: Ali Reza Noorian, Katja Bryant, Ashley Aiken, Andrew D. Nicholson, Adam B. Edwards, Mason P. Markowski, Seena Dehkharghani, Jemisha C. Bouloute, Jacquelyn Abney, Fadi Nahab Tags: Original Articles Source Type: research
