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Reperfusion Therapy in Acute Ischemic Stroke with Active Cancer: A Meta-Analysis Aided by Machine Learning
While the prevalence of active cancer patients experiencing acute stroke is increasing, the effects of active cancer on reperfusion therapy outcomes are inconclusive. Thus, we aimed to compare the safety and outcomes of reperfusion therapy in acute stroke patients with and without active cancer.
Source: Journal of Stroke and Cerebrovascular Diseases - March 26, 2021 Category: Neurology Authors: Mi-Yeon Eun, Eun-Tae Jeon, Kwon-Duk Seo, Dongwhane Lee, Jin-Man Jung Source Type: research

Alberta Stroke Program Early CT Score Calculation Using the Deep Learning-Based Brain Hemisphere Comparison Algorithm
The Alberta Stroke Program Early Computed Tomography Score (ASPECTS) is a promising tool for the evaluation of stroke expansion to determine suitability for reperfusion therapy. The aim of this study was to validate deep learning-based ASPECTS calculation software that utilizes a three-dimensional fully convolutional network-based brain hemisphere comparison algorithm (3D-BHCA).
Source: Journal of Stroke and Cerebrovascular Diseases - April 17, 2021 Category: Neurology Authors: Masaki Naganuma, Atsushi Tachibana, Takuya Fuchigami, Sadato Akahori, Shuichiro Okumura, Kenichiro Yi, Yoshimasa Matsuo, Koichi Ikeno, Toshiro Yonehara Source Type: research

Machine Learning Algorithm Identifies the Importance of Environmental Factors for Hospital Discharge to Home of Stroke Patients using Wheelchair after Discharge
This study aimed to identify the influential factors affecting home discharge in the stroke patients who use a wheelchair after discharge by using machine learning technology.
Source: Journal of Stroke and Cerebrovascular Diseases - May 21, 2021 Category: Neurology Authors: Takeshi Imura, Yuji Iwamoto, Yuki Azuma, Tetsuji Inagawa, Naoki Imada, Ryo Tanaka, Hayato Araki, Osamu Araki Source Type: research

Machine-Learning-Derived Model for the Stratification of Cardiovascular risk in Patients with Ischemic Stroke
Background Stratification of cardiovascular risk in patients with ischemic stroke is important as it may inform management strategies. We aimed to develop a machine-learning-derived prognostic model for the prediction of cardiovascular risk in ischemic stroke patients.
Source: Journal of Stroke and Cerebrovascular Diseases - August 2, 2021 Category: Neurology Authors: George Ntaios, Dimitrios Sagris, Athanasios Kallipolitis, Efstathia Karagkiozi, Eleni Korompoki, Efstathios Manios, Vasileios Plagianakos, Konstantinos Vemmos, Ilias Maglogiannis Source Type: research

Comparison of Supervised Machine Learning Algorithms for Classifying of Home Discharge Possibility in Convalescent Stroke Patients: A Secondary Analysis
Classifying the possibility of home discharge is important during stroke rehabilitation to support decision-making. There have been several studies on supervised machine learning algorithms, but only a few have compared the performance of different algorithms based on the same dataset for the classification of home discharge possibility. Therefore, we aimed to evaluate five supervised machine learning algorithms for the classification of home discharge possibility in stroke patients.
Source: Journal of Stroke and Cerebrovascular Diseases - July 26, 2021 Category: Neurology Authors: Takeshi Imura, Haruki Toda, Yuji Iwamoto, Tetsuji Inagawa, Naoki Imada, Ryo Tanaka, Yu Inoue, Hayato Araki, Osamu Araki Source Type: research

Clinical Prediction Rule for Identifying the Stroke Patients who will Obtain Clinically Important Improvement of Upper Limb Motor Function by Robot-Assisted Upper Limb
The number of studies on the characteristics of patients with stroke who would benefit from robot-assisted upper limb rehabilitation is limited, and there are no clear criteria for determining which individuals should receive such treatment. The current study aimed to develop a clinical prediction rule using machine learning to identify the characteristics of patients with stroke who can the achieve minimal clinically important difference of the Fugl-Meyer Upper Extremity Evaluation (FMA-UE) after single-joint hybrid assistive limb (HAL-SJ) rehabilitation.
Source: Journal of Stroke and Cerebrovascular Diseases - April 29, 2022 Category: Neurology Authors: Yuji Iwamoto, Takeshi Imura, Ryo Tanaka, Tsubasa Mitsutake, Hungu Jung, Takahiro Suzukawa, Shingo Taki, Naoki Imada, Tetsuji Inagawa, Hayato Araki, Osamu Araki 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

Machine Learning Analysis of MicroRNA Expression Data Reveals Novel Diagnostic Biomarker for Ischemic Stroke
In this study, we sought to use different machine learning algorithms to identify an optimal model of microRNA by integrating the expression data of pre-selected microRNAs for discriminating patients with IS from controls.
Source: Journal of Stroke and Cerebrovascular Diseases - May 19, 2021 Category: Neurology Authors: Xinyi Zhao, Xingmei Chen, Xulong Wu, Lulu Zhu, Jianxiong Long, Li Su, Lian Gu Source Type: research

The mucormycosis and Stroke: the learning curve during the second COVID-19 pandemic
Background The Angio-invasive Rhino-orbito-cerebral mucormycosis (ROCM) producing strokes is a less explored entity. Our hospital, a stroke-ready one, had an opportunity to manage mucormycosis when it was identified as the nodal center for mucormycosis management. We are sharing our experiences and mistakes in managing the cerebrovascular manifestations of ROCM.Methods We conducted a prospective observational study during the second wave of the COVID-19 pandemic from 1st May 2021 to 30th September 2021, where consecutive patients aged more than 18 years with microbiologically confirmed cases of ROCM were included.
Source: Journal of Stroke and Cerebrovascular Diseases - October 12, 2022 Category: Neurology Authors: Dileep Ramachandran, Aravind R, Praveen Panicker, Jayaprabha S, MC Sathyabhama, Abhilash Nair, Raj S. Chandran, Simon George, Chintha S, Thomas Iype Source Type: research

Impact of Gender and Blood Pressure on Poststroke Cognitive Decline among Older Latinos
Conclusions: Among this population of older Mexican Americans, PSCD did not differ by gender. We found no evidence that systolic BP influenced PSCD in women or men.
Source: Journal of Stroke and Cerebrovascular Diseases - June 29, 2012 Category: Neurology Authors: Deborah A. Levine, Mary N. Haan, Kenneth M. Langa, Lewis B. Morgenstern, John Neuhaus, Anne Lee, Lynda D. Lisabeth Tags: Original Articles 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

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

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

Detecting the Early Infarct Core on Non-Contrast CT Images with a Deep Learning Residual Network
To explore a new approach mainly based on deep learning residual network (ResNet) to detect infarct cores on non-contrast CT images and improve the accuracy of acute ischemic stroke diagnosis.
Source: Journal of Stroke and Cerebrovascular Diseases - March 27, 2021 Category: Neurology Authors: Jiawei Pan, Guoqing Wu, Jinhua Yu, Daoying Geng, Jun Zhang, Yuanyuan Wang Source Type: research

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