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Texture Features of Magnetic Resonance Images: an Early Marker of Post-stroke Cognitive Impairment
AbstractStroke is frequently associated with delayed, long-term cognitive impairment (CI) and dementia. Recent research has focused on identifying early predictive markers of CI occurrence. We carried out a texture analysis of magnetic resonance (MR) images to identify predictive markers of CI occurrence based on a combination of preclinical and clinical data. Seventy-two-hour post-stroke T1W MR images of 160 consecutive patients were examined, including 75 patients with confirmed CI at the 6-month post-stroke neuropsychological examination. Texture features were measured in the hippocampus and entorhinal cortex and compar...
Source: Translational Stroke Research - October 31, 2019 Category: Neurology Source Type: research

Uric Acid and Gluconic Acid as Predictors of Hyperglycemia and Cytotoxic Injury after Stroke
AbstractHyperglycemia is a feature of worse brain injury after acute ischemic stroke, but the underlying metabolic changes and the link to cytotoxic brain injury are not fully understood. In this observational study, we applied regression and machine learning classification analyses to identify metabolites associated with hyperglycemia and a neuroimaging proxy for cytotoxic brain injury. Metabolomics and lipidomics were carried out using liquid chromatography-tandem mass spectrometry in admission plasma samples from 381 patients presenting with an acute stroke. Glucose was measured by a central clinical laboratory, and a s...
Source: Translational Stroke Research - October 17, 2020 Category: Neurology 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

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

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

Development of Machine Learning Models to Predict Probabilities and Types of Stroke at Prehospital Stage: the Japan Urgent Stroke Triage Score Using Machine Learning (JUST-ML)
AbstractIn conjunction with recent advancements in machine learning (ML), such technologies have been applied in various fields owing to their high predictive performance. We tried to develop prehospital stroke scale with ML. We conducted multi-center retrospective and prospective cohort study. The training cohort had eight centers in Japan from June 2015 to March 2018, and the test cohort had 13 centers from April 2019 to March 2020. We use the three different ML algorithms (logistic regression, random forests, XGBoost) to develop models. Main outcomes were large vessel occlusion (LVO), intracranial hemorrhage (ICH), suba...
Source: Translational Stroke Research - August 14, 2021 Category: Neurology Source Type: research

Individualized stroke care offered by bedside optical monitoring of cerebral blood flow
Using a University of Pennsylvania-designed device to noninvasively and continuously monitor cerebral blood flow (CBF) in acute stroke patients, researchers from Penn Medicine and the Department of Physics & Astronomy in Penn Arts and Sciences are now learning how head of bed (HOB) positioning affects blood flow reaching the brain. Most patients admitted to the hospital with an acute stroke are kept flat for at least 24 hours in an effort to increase CBF in vulnerable brain regions surrounding the damaged tissue.
Source: Health News from Medical News Today - March 24, 2014 Category: Consumer Health News Tags: Stroke Source Type: news

Determining the barriers and facilitators to adopting best practices in the management of poststroke unilateral spatial neglect: results of a qualitative study.
Conclusion: It is estimated that upwards of 40% of patients experience poststroke USN in the acute phase, and we have evidence of poor early management. This study identified several modifiable factors that prepare the ground for the creation and testing of a multimodal knowledge translation intervention aimed at improving clinicians' best practice management of poststroke USN. PMID: 24985390 [PubMed - in process]
Source: Topics in Stroke Rehabilitation - May 1, 2014 Category: Neurology Authors: Petzold A, Korner-Bitensky N, Salbach NM, Ahmed S, Menon A, Ogourtsova T Tags: Top Stroke Rehabil 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

Medical News Today: Eye stroke: Symptoms, risks, and treatment
In this article, learn about what an eye stroke is. How is an eye stroke diagnosed, how can it be prevented, and what treatment is available?
Source: Health News from Medical News Today - June 12, 2017 Category: Consumer Health News Tags: Stroke Source Type: news

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

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

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

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