Cardiovascular

Strokes

Latest AI and machine learning research in strokes for healthcare professionals.

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Bridging Gaps with Generative AI: Enhancing Hypertension Monitoring Through Patient and Provider Insights.

This study introduces a Generative Artificial Intelligence (GenAI) assistant designed to address key...

Innovative approaches to atrial fibrillation prediction: should polygenic scores and machine learning be implemented in clinical practice?

Atrial fibrillation (AF) prediction and screening are of important clinical interest because of the ...

Segmentation of stroke lesions using transformers-augmented MRI analysis.

Accurate segmentation of chronic stroke lesions from mono-spectral magnetic resonance imaging scans ...

Estimating Upper-extremity Function with Raw Kinematic Trajectory Data after Stroke using End-to-end Machine Learning Approach.

Although there are some studies on the automatic evaluation of impairment levels after stroke using ...

A Regression Framework for Predicting Cognitive Decline in Frontotemporal Dementia using Recurrent Neural Networks.

Frontotemporal dementia (FTD) is a progressive neurodegenerative disorder with a diverse range of sy...

Therapy for Abnormal Muscle Synergies in Stroke Using the ULIX Low-Impedance Robot.

Patients who suffer from stroke often experience synergistic movements that make completion of activ...

Detecting Post-Stroke Aphasia Via Brain Responses to Speech in a Deep Learning Framework.

Aphasia, a language disorder primarily caused by a stroke, is traditionally diagnosed using behavior...

Unsupervised Gait Assessments of Stroke Patients Using a Smartphone and Machine Learning.

Home-based rehabilitation is a trend of post-stroke lower limb rehabilitation, aimed at a long-term ...

Artificial Intelligence Based Hierarchical Classification of Frontotemporal Dementia.

Frontotemporal dementia (FTD) is a typical kind of presenile dementia with three main subtypes: beha...

Multidimensional feature analysis shows stratification in robotic-motor-training gains based on the level of pre-training motor impairment in stroke.

Stroke involves heterogeneity in injury and ongoing endogenous recovery, which are seldom stratified...

An Efficient Deep Transfer Learning Network for Characterization of Stroke Patients' Motor Execution from Multi-Channel EEG-Recordings.

Recent advances in stroke rehabilitation technology have been focused on developing Intelligent Reha...

Implementation of a machine learning model in acute coronary syndrome and stroke risk assessment for patients with lower urinary tract symptoms.

OBJECTIVE: The global population is aging and the burden of lower urinary tract symptoms (LUTS) is e...

Leveraging multivariate analysis and adjusted mutual information to improve stroke prediction and interpretability.

OBJECTIVES: To develop a machine learning model to accurately predict stroke risk based on demograph...

Effects of training with a rehabilitation device (ReblessĀ®) on upper limb function in patients with chronic stroke: A randomized controlled trial.

BACKGROUND: Upper limb dysfunction is one of the most common sequelae of stroke and robotic therapy ...

Developing an Accumulative Assessment System of Upper Extremity Motor Function in Patients With Stroke Using Deep Learning.

OBJECTIVE: The Fugl-Meyer assessment for upper extremity (FMA-UE) is a measure for assessing upper e...

Efficacy of Daoyin combined with lower limb robot as a comprehensive rehabilitation intervention for stroke patients: a randomized controlled trial.

OBJECTIVE: To assess the effectiveness of a comprehensive rehabilitation approach combining Traditio...

Acceptance of Telepresence Robotics, Telecare and Teletherapy Among Stroke Patients, Relatives and Therapy Staff.

BACKGROUND: Stroke as a cause of disability in adulthood causes an increasing demand for therapy and...

Improvements of mid-thigh circumferences following robotic rehabilitation in hemiparetic stroke patients.

INTRODUCTION: Stroke has emerged as the leading cause of disability globally. The provision of long-...

Deep learning to assess right ventricular ejection fraction from two-dimensional echocardiograms in precapillary pulmonary hypertension.

BACKGROUND: Precapillary pulmonary hypertension (PH) is characterized by a sustained increase in rig...

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