• Filter By:
• Specialty
• Source
• Condition
• Cancer
• Infectious Disease
• Drug
• Training
• Management
• Procedure
• Therapy
• Vaccine
• Nutrition
• Country

Assistance from Automated ASPECTS Software Improves Reader Performance
To compare physicians ’ ability to read Alberta Stroke Program Early CT Score (ASPECTS) in patients with a large vessel occlusion within 6 hours of symptom onset when assisted by a machine learning-based automatic software tool, compared with their unassisted score.
Source: Journal of Stroke and Cerebrovascular Diseases - May 11, 2021 Category: Neurology Authors: Philip R Delio, Matthew L Wong, Jenny P. Tsai, H.E. Hinson, John McMenamy, Thang Q Le, Divya Prabhu, Barry S Mann, Karen Copeland, Keith Kwok, Hafez Haerian, Maarten J Lansberg, Jeremy J Heit Source Type: research

Prediction of Motor Function in Stroke Patients Using Machine Learning Algorithm: Development of Practical Models
Machine learning (ML) techniques are being increasingly adopted in the medical field.
Source: Journal of Stroke and Cerebrovascular Diseases - May 19, 2021 Category: Neurology Authors: Jeoung Kun Kim, Yoo Jin Choo, Min Cheol Chang Source Type: research

Large Vessel Occlusion Prediction in the Emergency Department with National Institutes of Health Stroke Scale Components: A Machine Learning Approach
To determine the feasibility of using a machine learning algorithm to screen for large vessel occlusions (LVO) in the Emergency Department (ED).
Source: Journal of Stroke and Cerebrovascular Diseases - August 15, 2021 Category: Neurology Authors: Donglai Huo, Michelle Leppert, Rebecca Pollard, Sharon N. Poisson, Xiang Fang, David Rubinstein, Igor Malenky, Kelsey Eklund, Eric Nyberg Tags: Short Communication Source Type: research

A Magnetic Resonance Angiography-Based Study Comparing Machine Learning and Clinical Evaluation: Screening Intracranial Regions Associated with the Hemorrhagic Stroke of Adult Moyamoya Disease
Moyamoya disease (MMD) is a chronic occlusive cerebrovascular disease characterized by bilateral progressive steno-occlusive changes of unknown etiology at the distal portion of the internal carotid artery or proximal portion of the anterior arteries and middle cerebral arteries, accompanied by the presence of an abnormal vessel network (moyamoya vessels) at the base of the brain.1 The incidence and prevalence of MMD are increasing worldwide, which may indicate an increase in the number of MMD patients or an underestimation of the actual number of MMD patients in the past.
Source: Journal of Stroke and Cerebrovascular Diseases - February 17, 2022 Category: Neurology Authors: Hao-lin Yin, Yu Jiang, Wen-jun Huang, Shi-hong Li, Guang-wu Lin 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

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

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

Cerebral venous thrombosis in patients with COVID-19 infection: a case series and systematic review
: There has been increasing reports associating the coronavirus disease 2019 (COVID-19) with thromboembolic phenomenon including ischemic strokes and venous thromboembolism. Cerebral venous thrombosis (CVT) is a rare neurovascular emergency that has been observed in some COVID-19 patients, yet much remains to be learnt of its underlying pathophysiology.
Source: Journal of Stroke and Cerebrovascular Diseases - October 5, 2020 Category: Neurology Authors: Tian Ming Tu, Claire Goh, Ying Kiat Tan, Aloysius ST Leow, Yu Zhi Pang, Jaime Chien, Humaira Shafi, Bernard PL Chan, Andrew Hui, Jasmine Koh, Benjamin YQ Tan, N. Thirugnanam Umapathi, Leonard LL Yeo Source Type: research

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

A Machine Learning Approach to First Pass Reperfusion in Mechanical Thrombectomy: Prediction and Feature Analysis
Novel machine learning (ML) methods are being investigated across medicine for their predictive capabilities while boasting increased adaptability and generalizability. In our study, we compare logistic regression with machine learning for feature importance analysis and prediction in first-pass reperfusion.
Source: Journal of Stroke and Cerebrovascular Diseases - April 19, 2021 Category: Neurology Authors: Lohit Velagapudi, Nikolaos Mouchtouris, Richard F. Schmidt, David Vuong, Omaditya Khanna, Ahmad Sweid, Bryan Sadler, Fadi Al Saiegh, M. Reid Gooch, Pascal Jabbour, Robert H. Rosenwasser, Stavropoula Tjoumakaris Source Type: research

Novel Approaches to Detection of Cerebral Microbleeds: Single Deep Learning Model to Achieve a Balanced Performance
Cerebral microbleeds (CMBs) are considered essential indicators for the diagnosis of cerebrovascular disease and cognitive disorders. Traditionally, CMBs are manually interpreted based on criteria including the shape, diameter, and signal characteristics after an MR examination, such as susceptibility-weighted imaging or gradient echo imaging (GRE). In this paper, an efficient method for CMB detection in GRE scans is presented.
Source: Journal of Stroke and Cerebrovascular Diseases - June 24, 2021 Category: Neurology Authors: Min Jae Myung, Kyung Mi Lee, Hyug-Gi Kim, Janghoon Oh, Ji Young Lee, Ilah Shin, Eui Jong Kim, Jin San Lee Source Type: research

Machine Learning Models Prognosticate Functional Outcomes Better than Clinical Scores in Spontaneous Intracerebral Haemorrhage
Spontaneous intracerebral haemorrhage (SICH) was associated with an overall incidence of 24.6 per 100,000 person-years worldwide and was most common in Asians, with an incidence of 51.8 per 100,000 person-years.1 SICH is a major cause of mortality and morbidity, with a case fatality of 40.4% at one month, one-year mortality rate of 54.0% and only 12 to 39% of patients leading an independent life on follow up.1,2 Current management for SICH remains supportive, and includes outcome prediction and rehabilitation.
Source: Journal of Stroke and Cerebrovascular Diseases - December 10, 2021 Category: Neurology Authors: Mervyn Jun Rui Lim, Raphael Hao Chong Quek, Kai Jie Ng, Ne-Hooi Will Loh, Sein Lwin, Kejia Teo, Vincent Diong Weng Nga, Tseng Tsai Yeo, Mehul Motani Source Type: research

Machine Learning-Based Perihematomal Tissue Features to Predict Clinical Outcome after Spontaneous Intracerebral Hemorrhage
To explore whether radiomic features of perihematomal tissue can improve the forecasting accuracy for the prognosis of patients with an intracerebral hemorrhage (ICH).
Source: Journal of Stroke and Cerebrovascular Diseases - April 11, 2022 Category: Neurology Authors: Xin Qi, Guorui Hu, Haiyan Sun, Zhigeng Chen, Chao Yang Source Type: research

End-to-end artificial intelligence platform for the management of large vessel occlusions: A preliminary study
In this study, we developed a deep learning pipeline that detects large vessel occlusion (LVO) and predicts functional outcome based on computed tomography angiography (CTA) images to improve the management of the LVO patients.
Source: Journal of Stroke and Cerebrovascular Diseases - September 15, 2022 Category: Neurology Authors: Shujuan Meng, Thi My Linh Tran, Mingzhe Hu, PanPan Wang, Thomas Yi, Zhusi Zhong, Luoyun Wang, Braden Vogt, Zhicheng Jiao, Arko Barman, Ugur Cetintemel, Ken Chang, Dat-Thanh Nguyen, Ferdinand K. Hui, Ian Pan, Bo Xiao, Li Yang, Hao Zhou, Harrison X. Bai Source Type: research

Pages: 1  2  3  4