Strokes

Machine learning-based analysis of the relationship between brain lesion sites and swallowing and cognitive functions in stroke patients.

Stroke-related dysphagia is influenced by brain damage location and cognitive impairment, but its mechanisms remain unclear. In this study, we aimed to clarify the mechanisms by which brain damage causes dysphagia and cognitive function in 246 patients with stroke using a probabilistic neural network model. Dysphagia was classified as mild (oral intake with liquid and diet modifications) or severe...

ECG Signatures and Long-Term Ischemic Stroke Risk: A Deep Learning Analysis of 200,000 Patients.

BACKGROUND: Scalable risk stratification for ischemic stroke remains an unmet need. OBJECTIVES: In this study, the authors sought to assess whether deep learning of 12-lead electrocardiograms (ECGs) can estimate longitudinal ischemic stroke risk and quantify the extent to which risk signals reflect plausible mechanisms (eg, atrial cardiopathy). METHODS: We trained a convolutional neural network to...

Hand Gesture Intention Detection Using sEMG and Transfer Learning in Stroke Survivors.

Surface electromyography (sEMG) has shown potential for intuitive control of wearable assistive technologies in individuals with motor impairments. Ho...

Canadian radiology: 2026 update.

Canadian radiology continues to produce scholarship that is technically sophisticated, clinically relevant, and increasingly attentive to the wider sy...

Machine learning models for predicting readmission after stroke: A systematic review and meta-analysis.

BACKGROUND: Hospital readmission following stroke poses a significant challenge for healthcare systems. Machine learning (ML) offers the potential to ...

Machine learning-based study of breath sounds in children with asthma in remission and cough variant asthma.

BACKGROUND: Traditional auscultation, heavily dependent on the subjective judgment of physicians, can lead to variability in diagnoses. This study aim...

Do predictors of motor recovery differ between robotic and conventional post-stroke rehabilitation?

BACKGROUND: While research on robotic rehabilitation has largely focused on evaluating device effectiveness, there remains a clear need to investigate...

Blood Pressure Assessment Across the Lifespan: Improving Clinical Research and Clinical Practice: A National Heart, Lung, and Blood Institute Workshop Report.

Hypertension is a modifiable risk factor for cardiovascular disease (CVD) and its prevalence is high in the United States and worldwide. Adequate char...

Development and Validation of a SHAP-Interpretable Machine Learning Model for Stroke Risk Prediction Using Circulating MicroRNA Biomarkers.

BACKGROUND/OBJECTIVE: Stroke remains a leading cause of morbidity and mortality worldwide. Circulating microRNAs (miRNAs) have emerged as promising no...

Associations of Red Blood Cell Distribution Width-Derived Indicators and Their Longitudinal Dynamic Trajectories With Mortality Risk in Critically Ill Patients With Pulmonary Hypertension.

Red blood cell distribution width (RDW)-derived indicators have increasingly been recognized as biomarkers reflecting systemic inflammation and hemato...

Alterations in resting-state neural networks in subacute non-fluent aphasia after stroke: a fNIRS study.

BACKGROUND: Post-stroke aphasia is a prevalent and often disabling language impairment. Despite its high incidence, the neuropathological mechanisms u...

Cardiovascular Risk Stratification in Youth-Onset Type 2 Diabetes Using Machine Learning.

BACKGROUND: The rising incidence of youth-onset type 2 diabetes mellitus (T2DM), along with the risk of early cardiovascular complications, is concern...

EJHF expert consensus statement on the diagnosis and management of hypertrophic cardiomyopathy.

Hypertrophic cardiomyopathy (HCM) is the most prevalent genetic cardiac disease and a leading cause of heart failure, arrhythmia, and sudden cardiac d...

Predicting 28-day all-cause mortality in critically ill ischemic stroke patients using white blood cell-to-hemoglobin ratio and dual-feature selection machine learning models.

BACKGROUND: The white blood cell-to-hemoglobin ratio (WHR) is a composite biomarker of inflammation and nutrition, but its prognostic role in critical...

Brain stroke detection and classification using deep learning-based transCBAMSegNet and hybrid transformer models.

Stroke is a life-threatening neurological condition that requires rapid assessment to reduce mortality and long-term disability. Accurate lesion segme...

Myocardial relaxation, left ventricular filling, and exercise capacity in older subjects with dyspnea.

With aging, left ventricular (LV) early diastolic lengthening declines. Delayed or dyssynchronous untwisting and relaxation may slow and reduce fillin...

Multi-dimensional MRI representation and privileged learning approaches to functional outcome prediction for ischemic stroke patients.

Accurate prediction of long-term functional outcomes for stroke patients remains a clinical challenge, despite advances in diagnostics and treatments....

Superlearner can predict in-hospital mortality risk in critically ill patients with ischemic stroke: development and international validation.

BACKGROUND: Critically ill patients with ischemic stroke face substantial in-hospital mortality. Early and accurate prediction of mortality risk may f...

Longitudinal Degeneration of Microstructural and Structural Connectivity Patterns Following Stroke.

BACKGROUND: Stroke causes neurological impairment through local plasticity mechanisms, such as synaptic sprouting, and large-scale network reorganizat...

Deep learning prediction of nocturnal hypertension for patients intolerant to ambulatory blood pressure monitoring.

BACKGROUND: Ambulatory blood pressure monitoring (ABPM) plays an irreplaceable role in the diagnosis and management of hypertension. However, more tha...