Strokes

Effect of Brain-Computer Interface-Controlled Ankle Robot Training on Post-Stroke Motor Rehabilitation and Resting QEEG Neuroplasticity: A Randomized Controlled Trial.

BACKGROUND: Persistent post-stroke ankle impairment hinders functional recovery. Brain-computer interface (BCI)-controlled ankle robot show rehabilitation potential, but their efficacy and underlying neuroplasticity remain unclear. OBJECTIVE: To assess BCI-controlled ankle robot training on post-stroke lower-limb motor recovery and neuroplasticity using quantitative EEG (qEEG). METHODS: Thirty-two...

An integrative genomic framework to identify remote ischemic preconditioning-responsive genes associated with stroke risk.

BACKGROUND: Remote ischemic preconditioning (RIPC) is a promising, non-invasive strategy for reduction in ischemic injury after stroke, yet its molecular mechanisms and translatability from animal models to humans remain insufficiently defined. In particular, whether RIPC-induced gene programs causally contribute to reduced stroke risk in humans is unclear. Here, we aimed to establish a translatio...

Deep Learning and Machine Learning for Differentiation Between Contrast Extravasation and Hemorrhagic Transformation in Post-Thrombectomy Stroke CT.

BACKGROUND: Mechanical thrombectomy (MT) improves outcomes in acute ischemic stroke (AIS) but often results in hyperdensities on non-contrast CT (NCCT...

Diagnosis of Cardiac Amyloidosis on Echocardiography Using Artificial Intelligence.

BACKGROUND: Diagnosing cardiac amyloidosis (CA) on echocardiography can be challenging due to the imaging overlap between CA and more prevalent causes...

Diagnostic model and identification of IGFBP2 as an aging-related biomarker and therapeutic target for systemic sclerosis-associated interstitial lung disease through integrated bioinformatics and machine learning approaches.

BACKGROUND: Cellular senescence is a critical contributor to the pathogenesis of systemic sclerosis-associated interstitial lung disease (SSc-ILD). Ho...

Explainable machine learning model based on clinical and radiological features for predicting hematoma expansion or rebleeding after decompressive craniectomy in traumatic brain injury: a bicentric cohort study.

BACKGROUND: Hematoma expansion or rebleeding after decompressive craniectomy (DC) is a critical determinant of poor prognosis in traumatic brain injur...

A Strengths, Weaknesses, Opportunities, and Threats Analysis of Generative Artificial Intelligence in Aphasia Management With Proposed Ethical Guidelines.

PURPOSE: Generative artificial intelligence (GenAI) is rapidly emerging as a transformative technology in aphasia management, necessitating judicious ...

Leveraging natural language processing and machine learning to identify chronic conditions from primary care electronic medical records.

Primary care electronic medical records (EMRs) contain rich data that can support proactive identification of chronic health conditions. However, leve...

Robot-assisted therapy following stroke: what effects on quality of life, cognitive and psychosocial outcomes? A systematic review.

OBJECTIVE: Robot-assisted therapy (RAT) has shown promise in post-stroke motor recovery. However, its effects on non-motor outcomes remain unclear. Th...

[Cardiology : what's new in 2025].

In 2025, significant progress has been made in the management of heart failure and cardiovascular diseases, driven by the emergence of new treatments ...

DNA Methylation Markers for Pregnancy Hypertension via Machine Learning Methods.

BACKGROUND: This study aims to develop a prediction model to identify individuals at risk of hypertensive disorders of pregnancy (HDPs), including ges...

Polysomnography Dataset for Sleep Analysis in Ischemic Stroke Patients.

Sleep architecture and integrity significantly influence neural recovery and cognitive restoration. These are particularly relevant in ischemic stroke...

Data-driven subgroups for 3-year risk stratification of incident diabetes and complications in diabetes-free Chinese adults.

BACKGROUND: Substantial metabolic heterogeneity exists prior to the development of diabetes, creating opportunities for earlier and more precise inter...

Health-oriented sleep states: making sleep states reflect health conditions.

STUDY OBJECTIVES: The rich information in sleep offers insights into brain function and overall health. The current guidelines for sleep staging by th...

Machine-learning-derived phenotypes of hypertensive patients using multidimensional clinical and echocardiographic data including strain imaging.

AIMS: We applied unsupervised machine learning clustering to a large cohort of hypertensive patients undergoing echocardiography with strain imaging t...

Coronary revascularization: a long-term perspective.

This paper will forecast advancements in coronary revascularization by 2040, drawing on historical trends and recent breakthroughs. Having forecasted ...

Early Prediction of Adverse Stroke Outcomes using Non-clinical Factors and Missing Data: A Machine Learning Study.

Introduction Early prediction of stroke outcomes using prognostic tools may help clinical decision making and inform resource allocation. However, cli...

Dynamic prediction of paroxysmal atrial fibrillation onset using longitudinal sample entropy in joint models.

BACKGROUND: Atrial fibrillation (AF) is the most common cardiac arrhythmia and is associated with a five-fold increased risk of stroke. Early predicti...

Sex differences in hemodynamics and remodeling patterns uncovered by automated Machine-Learning 3D echocardiography in aortic stenosis with preserved ejection fraction.

Women with aortic stenosis (AS) are underdiagnosed and undertreated compared to men and face a higher mortality risk despite similar symptoms and fewe...

Exploring the future of ischemic stroke diagnosis and underrecognized etiologies.

INTRODUCTION: Despite traditional workups, accurate diagnosis of patients who are classified under cryptogenic stroke with underrecognized etiologies ...