Strokes

Machine Learning Approaches to Identify Influential Factors of the Comorbid Psychiatric Symptoms and Hypertension Among Rural Adults in Bangladesh: A Cross-Sectional Study.

BACKGROUND AND AIM: The burden of both psychiatric symptoms (anxiety and/or depression) and hypertension poses significant public health challenges in Bangladesh, especially in rural areas with limited healthcare access. Thus, this study aimed to identify the influential factors associated with comorbid psychiatric symptoms and hypertension in rural Bangladesh using machine learning (ML) algorithm...

External Validation of a Machine Learning Model for the Early Prediction of Upper-Limb Recovery After Stroke.

BACKGROUND: Reliable prediction of upper-limb recovery after stroke can support rehabilitation planning, yet few prognostic models have been tested beyond their development cohorts. External validation is essential to establish the clinical reliability of these methods. OBJECTIVE: To externally validate a previously developed machine learning model for predicting 6-month upper-limb capacity after ...

Clinical and Echocardiographic Factors Associated With AI-Estimated Atrial Fibrillation Likelihood During Sinus Rhythm in Patients With Documented Paroxysmal Atrial Fibrillation.

BACKGROUND: Artificial intelligence-enabled electrocardiography (AI-ECG) has emerged as a promising tool for identifying patients with atrial fibrilla...

[Artificial intelligence in hypertension: where do we stand?].

Artificial intelligence (AI) is entering the study of hypertension, serving both primarily clinical purposes - to assist practicing physicians - and r...

Bulk and single-cell RNA-seq analyses characterize a DBP-related circadian signature in ischemic stroke.

Circadian rhythm disruption is associated with ischemic stroke outcomes, yet the roles of circadian genes in stroke pathology remain unclear. This stu...

Machine-Learning-Based Evaluation of the Prognostic Significance of the Non-High-Density Lipoprotein to High-Density Lipoprotein Cholesterol Ratio in Critical Ischemic Stroke.

BACKGROUND This study examined associations of the non-high-density lipoprotein to high-density lipoprotein cholesterol ratio (NHHR) with short-term (...

Predicting stroke risk using retinal imaging with artificial intelligence: a scoping review of current evidence.

BACKGROUND: Stroke remains a leading cause of mortality and long-term disability worldwide, while currently used clinical risk prediction tools demons...

Mental simulation practices in stroke rehabilitation reorganize brain connectivity: a systematic review.

BACKGROUND AND PURPOSE: Stroke is a leading cause of disability, associated with impaired motor function and brain connectivity. Mental Simulation Pra...

Artificial intelligence shows comparable or improved performance to traditional risk models in predicting atrial fibrillation after cryptogenic stroke.

BACKGROUND AND AIMS: Detecting subclinical atrial fibrillation (AF) and initiating anticoagulation therapy are critical for secondary stroke preventio...

Identifying the Most Important Influencing Factors Based on Demographic Information, Nutrition, Physical Activity, Leisure Time, and Underlying Diseases in the Severity of Disease in COVID-19 Patients Using Data Mining Techniques.

BACKGROUND AND AIMS: The COVID-19 pandemic has affected millions of individuals worldwide and resulted in substantial mortality. Data mining and machi...

AI-Driven Atrial Fibrillation Management: From Signal to Strategy.

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia worldwide and is associated with substantial morbidity, including ischemic st...

Cardiovascular risks of plastic-associated chemicals: Integrating mixture modeling and SHAP-explained machine learning to unravel linear and non-linear associations.

INTRODUCTION: Plastic-associated chemicals (PACs) are widely detected environmental contaminants, yet their cardiovascular relevance in the context of...

Integrating Multimodal Data and Genomics for a Comprehensive Assessment of Cardiovascular Aging and Its Impact.

BACKGROUND: Age-related structural and functional remodeling of the heart and vessels increases cardiovascular disease (CVD) risk, yet comprehensive a...

A multi-paradigm and longitudinal EEG dataset including the "sixth-finger" and "affected-hand" motor imagery of stroke patients.

Motor imagery-based brain-computer interface (MI-BCI) applications in stroke rehabilitation aim to match brain activity with real-time feedback, there...

Effect of pilates exercises plus breastfeeding positioning adjustment on artificial intelligence-assessed craniovertebral angle in breastfeeding women with forward head posture: a randomized controlled trial.

Forward head posture (FHP), considered a neuromusculoskeletal disorder, includes those associated with breastfeeding. Pilates is recognized for improv...

Predicting medication non-adherence using machine learning: Incorporating Complementary and Alternative Medicine (CAM) beliefs in Malaysian chronic disease patients.

Medication non-adherence among chronic disease patients remains a major contributor to poor health outcomes and medication wastage, particularly in mu...

Intracranial pressure physiology, monitoring and individualized management in the acute brain injured patient.

Acute brain injury (ABI), including traumatic brain injury, ischemic and hemorrhagic stroke, is associated with high morbidity and mortality, which is...

Robot-assisted gait training using a robotic exoskeleton during inpatient rehabilitation in acute Guillain-Barré Syndrome.

A woman in her early 60s with acute Guillain-Barré syndrome (GBS) developed rapidly progressive tetraparesis and respiratory failure requiring non-inv...

Neuroimaging in Distal & Medium Vessel Occlusions.

Distal medium vessel occlusions (DMVOs), as a cause of acute ischemic stroke, typically present with unique challenges in diagnosis and treatment owin...

Accelerated brain aging and anterior white matter hyperintensity burden in chronic post-stroke aphasia: a lesion-aware MRI analysis.

BACKGROUND AND PURPOSE: Chronic post-stroke aphasia is heterogeneous, and focal lesion models do not fully explain persistent language impairment. We ...