Strokes

Mapping Cognitive Engagement: EEG and Graph Theory Analysis of Brain Region Involvement in Supernumerary Robotic Finger Utilization.

As the worldwide incidence of stroke increases, supernumerary robotic limbs (SRLs), more specifically supernumerary robotic fingers (SRFs), present a potentially effective solution for enhancing the task related functionality of the upper-limbs of stroke survivors. This study investigated the impact of an SRF use on cognitive function, employing both electroencephalography (EEG) and graph theory a...

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 before testing post-stroke robot assisted motor training (RAMT). Pretraining variations, especially sensory-motor differences may also affect the gains achieved from the RAMT. Moreover, one assessment test may not effectively characterize the baseline sensory-motor status or the RAMT gains. Pre-the...

Artificial Intelligence Based Hierarchical Classification of Frontotemporal Dementia.

Frontotemporal dementia (FTD) is a typical kind of presenile dementia with three main subtypes: behavioral-variant FTD (bvFTD), non-fluent variant pri...

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 and higher dose of therapy. Unsupervised gait asse...

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 behavioral language tests. However, these tests are time-c...

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 activities of daily living (ADL) difficult. Robotics is...

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 symptoms, including personality changes, behavioral ...

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 machine learning (ML) models, few have delved into...

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 right ventricular (RV) afterload, impairing systolic ...

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-term rehabilitation to stroke survivors poses a he...

Ultrafast Brain MRI with Deep Learning Reconstruction for Suspected Acute Ischemic Stroke.

Background Deep learning (DL)-accelerated MRI can substantially reduce examination times. However, studies prospectively evaluating the diagnostic per...

Relation Detection to Identify Stroke Assertions from Clinical Notes Using Natural Language Processing.

According to the World Stroke Organization, 12.2 million people world-wide will have their first stroke this year almost half of which will die as a r...

Identification of candidate biomarkers and molecular networks associated with Pulmonary Arterial Hypertension using machine learning and plasma multi-Omics analysis

Pulmonary arterial hypertension (PAH) is a rare but severe and life- threatening condition that primarily affects the pulmonary blood vessels and the ...

Better Blood Pressure Control for Stroke Patients in the ICU: A Deep Reinforcement Learning with Supervised Guidance Approach for Adaptive Infusion Rate Tuning.

Blood pressure variability (BPV) plays a critical role in vascular diseases, particularly in acute ischemic stroke patients in intensive care units (I...

HTNpedia: A Knowledge Base for Hypertension Research.

BACKGROUND: Hypertension is notably a serious public health concern due to its high prevalence and strong association with cardiovascular disease and ...

A deep learning and radiomics based Alberta stroke program early CT score method on CTA to evaluate acute ischemic stroke.

BACKGROUND: Alberta stroke program early CT score (ASPECTS) is a semi-quantitative evaluation method used to evaluate early ischemic changes in patien...

FDA Review of Radiologic AI Algorithms: Process and Challenges.

A Food and Drug Administration (FDA)-cleared artificial intelligence (AI) algorithm misdiagnosed a finding as an intracranial hemorrhage in a patient,...

Examining different cost ratio frameworks for decision rule machine learning algorithms in diagnostic application.

BACKGROUND: Artificial Intelligence (AI) plays a pivotal role in the diagnosis of health conditions ranging from general well-being to critical health...

A Video-based Automated Tracking and Analysis System of Plaque Burden in Carotid Artery Using Deep Learning: A Comparison with Senior Sonographers.

BACKGROUND AND OBJECTIVE: The incidence of stroke is rising, and it is the second major cause of mortality and the third leading cause of disability a...

Clinical machine learning predicting best stroke rehabilitation responders to exoskeletal robotic gait rehabilitation.

BACKGROUND: Although clinical machine learning (ML) algorithms offer promising potential in forecasting optimal stroke rehabilitation outcomes, their ...