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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 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

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

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

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

Discrepancies in Stroke Distribution and Dataset Origin in Machine Learning for Stroke
Machine learning algorithms depend on accurate and representative datasets for training in order to become valuable clinical tools that are widely generalizable to a varied population. We aim to conduct a review of machine learning uses in stroke literature to assess the geographic distribution of datasets and patient cohorts used to train these models and compare them to stroke distribution to evaluate for disparities.
Source: Journal of Stroke and Cerebrovascular Diseases - April 30, 2021 Category: Neurology Authors: Lohit Velagapudi, Nikolaos Mouchtouris, Michael P. Baldassari, David Nauheim, Omaditya Khanna, Fadi Al Saiegh, Nabeel Herial, M. Reid Gooch, Stavropoula Tjoumakaris, Robert H. Rosenwasser, Pascal Jabbour 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

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

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

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

Decision-Making on Referral to Primary Care Physiotherapy After Inpatient Stroke Rehabilitation
Worldwide, stroke is a leading cause of death and disability.1 Although incidence rates are expected to increase over the next few decades, survival rates are expected to improve. Consequently, more stroke survivors will have to learn to live with the consequences. After acute stroke care or rehabilitation, returning home is one of the primary goals for stroke survivors.2 In the Netherlands, 65 % of stroke survivors return home immediately after acute hospital care.3 The remaining 35% continue inpatient rehabilitation in a rehabilitation center (RC) or geriatric rehabilitation center (GRC) before returning home.
Source: Journal of Stroke and Cerebrovascular Diseases - February 23, 2021 Category: Neurology Authors: Marieke Geerars, Roderick Wondergem, Martijn F. Pisters Source Type: research

Decision Tree Algorithm Identifies Stroke Patients Likely Discharge Home After Rehabilitation Using Functional and Environmental Predictors
The importance of environmental factors for stroke patients to achieve home discharge was not scientifically proven. There are limited studies on the application of the decision tree algorithm with various functional and environmental variables to identify stroke patients with a high possibility of home discharge. The present study aimed to identify the factors, including functional and environmental factors, affecting home discharge after stroke inpatient rehabilitation using the machine learning method.
Source: Journal of Stroke and Cerebrovascular Diseases - February 3, 2021 Category: Neurology Authors: Takeshi Imura, Yuji Iwamoto, Tetsuji Inagawa, Naoki Imada, Ryo Tanaka, Haruki Toda, Yu Inoue, Hayato Araki, Osamu Araki Source Type: research

Clinical Features for Identifying the Possibility of Toileting Independence after Convalescent Inpatient Rehabilitation in Severe Stroke Patients: A Decision Tree Analysis Based on a Nationwide Japan Rehabilitation Database
This study aimed to identify the factors affecting toileting independence in severe stroke patients using ML.
Source: Journal of Stroke and Cerebrovascular Diseases - November 27, 2020 Category: Neurology Authors: Takeshi Imura, Yu Inoue, Ryo Tanaka, Junji Matsuba, Yasutaka Umayahara 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

Development and Validation of Machine Learning-Based Prediction for Dependence in the Activities of Daily Living after Stroke Inpatient Rehabilitation: A Decision-Tree Analysis
This study aimed to develop and assess the CPRs using machine learning-based methods to identify ADL dependence in stroke patients.
Source: Journal of Stroke and Cerebrovascular Diseases - September 26, 2020 Category: Neurology Authors: Yuji Iwamoto, Takeshi Imura, Ryo Tanaka, Naoki Imada, Tetsuji Inagawa, Hayato Araki, Osamu Araki Source Type: research

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