Strokes

Deep Neural Networks Can Predict New-Onset Atrial Fibrillation From the 12-Lead ECG and Help Identify Those at Risk of Atrial Fibrillation-Related Stroke.

BACKGROUND: Atrial fibrillation (AF) is associated with substantial morbidity, especially when it goes undetected. If new-onset AF could be predicted, targeted screening could be used to find it early. We hypothesized that a deep neural network could predict new-onset AF from the resting 12-lead ECG and that this prediction may help identify those at risk of AF-related stroke.

Assessing robustness of carotid artery CT angiography radiomics in the identification of culprit lesions in cerebrovascular events.

Radiomics, quantitative feature extraction from radiological images, can improve disease diagnosis and prognostication. However, radiomic features are susceptible to image acquisition and segmentation variability. Ideally, only features robust to these variations would be incorporated into predictive models, for good generalisability. We extracted 93 radiomic features from carotid artery computed ...

Predicting COVID-19 disease progression and patient outcomes based on temporal deep learning.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has caused health concerns worldwide since December 2019. From the beginning of infection...

Effect of assist-as-needed robotic gait training on the gait pattern post stroke: a randomized controlled trial.

BACKGROUND: Regaining gait capacity is an important rehabilitation goal post stroke. Compared to clinically available robotic gait trainers, robots wi...

Decision Tree Algorithm Identifies Stroke Patients Likely Discharge Home After Rehabilitation Using Functional and Environmental Predictors.

BACKGROUND AND PURPOSE: The importance of environmental factors for stroke patients to achieve home discharge was not scientifically proven. There are...

Effects of wearable ankle robotics for stair and over-ground training on sub-acute stroke: a randomized controlled trial.

BACKGROUND: Wearable ankle robotics could potentially facilitate intensive repetitive task-specific gait training on stair environment for stroke reha...

Deep Learning-Based Acute Ischemic Stroke Lesion Segmentation Method on Multimodal MR Images Using a Few Fully Labeled Subjects.

Acute ischemic stroke (AIS) has been a common threat to human health and may lead to severe outcomes without proper and prompt treatment. To precisely...

AI Therapist Realizing Expert Verbal Cues for Effective Robot-Assisted Gait Training.

Repetitive and specific verbal cues by a therapist are essential in aiding a patient's motivation and improving the motor learning process. The verbal...

The Metabolic Cost of Exercising With a Robotic Exoskeleton: A Comparison of Healthy and Neurologically Impaired People.

While neuro-recovery is maximized through active engagement, it has been suggested that the use of robotic exoskeletons in neuro-rehabilitation provid...

Associated factors of white matter hyperintensity volume: a machine-learning approach.

To identify the most important parameters associated with cerebral white matter hyperintensities (WMH), in consideration of potential collinearity, we...

Systematic review of guidelines to identify recommendations for upper limb robotic rehabilitation after stroke.

INTRODUCTION: Upper limb motor impairment is one of the most frequent stroke consequences. Robot therapy may represent a valid option for upper limb s...

Extracting Angina Symptoms from Clinical Notes Using Pre-Trained Transformer Architectures.

Anginal symptoms can connote increased cardiac risk and a need for change in cardiovascular management. In this study, a pre-trained transformer archi...

Na MRI in ischemic stroke: Acquisition time reduction using postprocessing with convolutional neural networks.

Quantitative Na magnetic resonance imaging (MRI) provides tissue sodium concentration (TSC), which is connected to cell viability and vitality. Long ...

Robot enhanced stroke therapy optimizes rehabilitation (RESTORE): a pilot study.

BACKGROUND: Robotic rehabilitation after stroke provides the potential to increase and carefully control dosage of therapy. Only a small number of stu...

Ultrasound-based internal carotid artery plaque characterization using deep learning paradigm on a supercomputer: a cardiovascular disease/stroke risk assessment system.

Visual or manual characterization and classification of atherosclerotic plaque lesions are tedious, error-prone, and time-consuming. The purpose of th...

Prediction of Clinical Outcome in Patients with Large-Vessel Acute Ischemic Stroke: Performance of Machine Learning versus SPAN-100.

BACKGROUND AND PURPOSE: Traditional statistical models and pretreatment scoring systems have been used to predict the outcome for acute ischemic strok...

Robotic Assessment of Upper Limb Function in a Nonhuman Primate Model of Chronic Stroke.

Stroke is a leading cause of death and disability worldwide and survivors are frequently left with long-term disabilities that diminish their autonomy...

Observational Cross-Sectional Study of Inflammatory Markers After Transient Ischemic Attacks, Acute Coronary Syndromes, and Vascular Stroke Events.

We identified the prevalence of elevated high-sensitivity C-reactive protein and interleukin-6 in patients with recent cardiovascular (CV) events with...

Evaluation of Artificial Intelligence-Powered Identification of Large-Vessel Occlusions in a Comprehensive Stroke Center.

BACKGROUND AND PURPOSE: Artificial intelligence algorithms have the potential to become an important diagnostic tool to optimize stroke workflow. Viz ...

Machine learning-based multimodal prediction of language outcomes in chronic aphasia.

Recent studies have combined multiple neuroimaging modalities to gain further understanding of the neurobiological substrates of aphasia. Following th...