AIMC Topic: Stroke
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Development of a deep learning model to identify hyperdense MCA sign in patients with acute ischemic stroke.
Japanese journal of radiology
Oct 31, 2019
PURPOSE: The aim of this study was to develop an interactive deep learning-assisted identification of the hyperdense middle cerebral artery (MCA) sign (HMCAS) on non-contrast computed tomography (CT) among patients with acute ischemic stroke.
Temporally downsampled cerebral CT perfusion image restoration using deep residual learning.
International journal of computer assisted radiology and surgery
Oct 31, 2019
PURPOSE: Acute ischemic stroke is one of the most causes of death all over the world. Onset to treatment time is critical in stroke diagnosis and treatment. Considering the time consumption and high price of MR imaging, CT perfusion (CTP) imaging is ...
Clinical non-superiority of technology-assisted gait training with body weight support in patients with subacute stroke: A meta-analysis.
Annals of physical and rehabilitation medicine
Oct 30, 2019
BACKGROUND: Technology-assisted gait training (TAGT) with body weight support (BWS) has been designed to provide high numbers of repetitions during stepping practice, but its benefits have been inconclusive.
Automated label-free detection of injured neuron with deep learning by two-photon microscopy.
Journal of biophotonics
Oct 30, 2019
Stroke is a significant cause of morbidity and long-term disability globally. Detection of injured neuron is a prerequisite for defining the degree of focal ischemic brain injury, which can be used to guide further therapy. Here, we demonstrate the c...
A hybrid machine learning approach to cerebral stroke prediction based on imbalanced medical dataset.
Artificial intelligence in medicine
Oct 23, 2019
BACKGROUND AND OBJECTIVE: Cerebral stroke has become a significant global public health issue in recent years. The ideal solution to this concern is to prevent in advance by controlling related metabolic factors. However, it is difficult for medical ...
Data-efficient deep learning of radiological image data for outcome prediction after endovascular treatment of patients with acute ischemic stroke.
Computers in biology and medicine
Oct 22, 2019
Treatment selection is becoming increasingly more important in acute ischemic stroke patient care. Clinical variables and radiological image biomarkers (old age, pre-stroke mRS, NIHSS, occlusion location, ASPECTS, among others) have an important role...
Training for Walking Efficiency With a Wearable Hip-Assist Robot in Patients With Stroke: A Pilot Randomized Controlled Trial.
Stroke
Oct 18, 2019
Background and Purpose- The purpose of this study was to investigate the effects of gait training with a newly developed wearable hip-assist robot on locomotor function and efficiency in patients with chronic stroke. Methods- Twenty-eight patients wi...
Extreme Gradient Boosting Model Has a Better Performance in Predicting the Risk of 90-Day Readmissions in Patients with Ischaemic Stroke.
Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
Oct 16, 2019
OBJECT: Ischemic stroke readmission within 90 days of hospital discharge is an important quality of care metric. The readmission rates of ischemic stroke patients are usually higher than those of patients with other chronic diseases. Our aim was to i...
Atrial Fibrillation Burden Signature and Near-Term Prediction of Stroke: A Machine Learning Analysis.
Circulation. Cardiovascular quality and outcomes
Oct 15, 2019
BACKGROUND: Atrial fibrillation (AF) increases the risk of stroke 5-fold and there is rising interest to determine if AF severity or burden can further risk stratify these patients, particularly for near-term events. Using continuous remote monitorin...
Stroke
Atrial Fibrillation
Proof of Concept Study
Administrative Claims, Healthcare
Logistic Models
United States
Predictive Value of Tests
Signal Processing, Computer-Assisted
Telemetry
Female
Veterans Health Services
Risk Factors
Aged
Aged, 80 and over
Risk Assessment
Male
Humans
Diagnosis, Computer-Assisted
Middle Aged
Machine Learning
Neural Networks, Computer
Time Factors
Prognosis
Retrospective Studies
Electronic Health Records
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