Strokes

Development and validation of a prediction model for long-term cognitive frailty risk in stroke patients based on CHARLS data.

BACKGROUND: This study aimed to develop and validate machine learning (ML) models for predicting the risk of cognitive frailty in community-dwelling elderly adults with stroke. METHODS: This study involved 2,325 stroke survivors from the China Health and Retirement Longitudinal Study (CHARLS), conducted between 2018 and 2020. We examined 22 behavioral variables, encompassing indicators from the so...

Smartphone-derived joint angular velocities in sit-to-stand motion provide a spatiotemporal marker for symptomatic knee osteoarthritis.

BACKGROUND: Knee osteoarthritis (OA) is a debilitating condition that compromises mobility and exacerbates knee pain, necessitating accurate and accessible diagnostic tools. Traditional motion capture technology, while effective, is often cost-prohibitive and limited to laboratory settings. In response, we developed a smartphone-based approach utilizing spatiotemporal analysis of joint angular vel...

Artificial Intelligence in Neurocritical Care : Multimodal Biosignal Analysis for Prognosis, Monitoring, and Future Pediatric Applications.

Neurocritical care relies on continuous assessment of neurological function and physiology under time pressure, yet bedside teams must interpret high-...

Correction to: Creating Virtual Stroke Networks: Current and Future Role of Artificial Intelligence, Mobile Imaging Applications, and Telehealth in Triage and Treatment of Acute Ischemic Stroke: A Scientific Statement From the American Heart Association.

A hybrid pipeline for carotid artery segmentation using YOLOv11n and contour models.

Carotid artery segmentation is critical for determining the degree of vascular disease, and for recommending treatment options. Early detection of car...

Imaging biomarkers of post-COVID dyspnea: insights from machine learning CT patterns and parametric response mapping.

Artificial Intelligence Applications in COVID-19-Associated Coagulopathy: Lessons Learned.

Coronavirus disease 2019 (COVID-19)-associated coagulopathy (CAC) is a thromboinflammatory syndrome marked by endothelial injury, micro- and macrovasc...

Cardiopulmonary exercise testing in contemporary cardiology: physiological insights, novel applications and evolving algorithms.

Cardiopulmonary exercise testing (CPET) allows for the study of the pathophysiology of exercise intolerance through assessment of exercise integrative...

Integrated bioinformatics and machine learning, research on specific biomarkers for large-artery atherosclerosis stroke.

Large-artery atherosclerosis stroke (LAA) is the main subtype of ischemic stroke. Currently, the diagnosis of LAA is confirmed through magnetic resona...

Deep-learning reconstruction for noncontrast head computed tomography: Improved image quality and potential diagnostic impact in acute ischemic stroke.

Predicting Recurrence and Outcomes After Stressor-Associated Atrial Fibrillation Using ECG-Based Deep Learning.

BACKGROUND: Stressor-associated atrial fibrillation (AF) refers to new-onset AF that occurs with a reversible, acute stressor. Identifying individuals...

Population-Level Digital Stroke Surveillance: Building a Fair and Accurate ICD-10 Detection Model.

Background The International Classification of Diseases, 10th Revision (ICD-10), is widely used for clinical care, quality assurance, and stroke resea...

Effect of a clinical decision support system on stroke care quality and outcomes in patients with acute ischaemic stroke (GOLDEN BRIDGE II): cluster randomised clinical trial.

OBJECTIVE: To evaluate the efficacy of a clinical decision support system (CDSS) on stroke care quality and clinical outcomes among patients with acut...

Percutaneous coronary intervention in high bleeding risk patients: a review of contemporary management strategies.

INTRODUCTION: High bleeding risk (HBR) affects over one-third of patients undergoing percutaneous coronary intervention (PCI) and is associated with e...

Machine Learning-Based Risk Prediction for Coronary Heart Disease Complicated by Hyperhomocysteinemia: Retrospective Study.

BACKGROUND: Hyperhomocysteinemia (HHcy) is recognized as an independent risk factor for coronary heart disease (CHD), yet accurately predicting CHD ri...

Development and Validation of a Machine Learning Model to Predict Oral Anticoagulant Use in Stroke From Prothrombin Time-International Normalized Ratio and Activated Partial Thromboplastin Time.

BACKGROUND: Timely recognition of oral anticoagulant use is critical in acute stroke but is often hampered by impaired consciousness and unavailable m...

The Burden, Outcomes, and Management of Patients with Aldosterone Dysregulation: A Targeted Literature Review.

INTRODUCTION: Aldosterone dysregulation (excess aldosterone production at the source within the adrenal glands) in the absence of primary aldosteronis...

Cerebrovascular CTA radiomics for objective collateral grading in acute ischemic stroke.

OBJECTIVE: Collateral circulation is a key determinant of functional outcome after large vessel occlusion (LVO) and informs thrombectomy decisions. Ho...

Deep learning-based identification of chronic pulmonary embolism on CTPA: a regional lung analysis using multiplanar MIP images.

OBJECTIVE: Chronic pulmonary embolism (CPE) and chronic thromboembolic pulmonary hypertension (CTEPH) are challenging to diagnose, with delayed detect...

Protective Effect of the FOXO3 Gene in Cardiometabolic Disease in the CHAMP Cohort.

Recent findings from the Honolulu Heart Program cohort in Hawaii suggest a longevity-associated variant of FOXO3 may provide resilience against cardio...