Hypertension

Interaction Between Air Pollution and Genetic Predisposition to Blood Pressure and LDL-C on Cardiovascular Events.

AIMS: Particulate matter ≤2.5μm (PM2.5) air pollution is a leading global environmental risk factor. We investigated the impact of PM2.5 on cardiovascular risk with lifetime genetic predisposition to low-density lipoprotein cholesterol (LDL-C) and systolic blood pressure (SBP). METHODS: We conducted a Mendelian Randomization (MR) study of participants from the UK Biobank study (n = 412,446) over 1...

Determinants of childhood and adolescent obesity: An explainable AI approach using the ICAD database.

OBJECTIVES: To identify the biological, behavioural, and socio-demographic determinants of childhood and adolescent obesity and evaluate how their relative importance shifts across age strata using an Explainable Artificial Intelligence (XAI) approach. STUDY DESIGN: A cross-sectional analysis of a large-scale, multi-country pooled database. METHODS: Data from 9660 participants (aged 5-17 years) fr...

Validation of latent health behaviour subtypes in young and middle-aged adults with hypertension: a machine learning-augmented LCA approach with external cohort replication.

AIMS: To validate behavioural subtypes among young and middle-aged hypertensive patients using latent class analysis (LCA) and assess their generaliza...

Prediction of Alzheimer's disease risk factors from retinal images via deep learning: Development and validation of biologically relevant morphological associations in the UK Biobank.

BackgroundThe systemic, metabolic, lifestyle factors have established associations with Alzheimer's disease (AD) through epidemiologic and AD-specific...

nnU-Net-based CT segmentation of perigastric varices in sinistral portal hypertension: a multicenter study.

PURPOSE: To develop a deep learning model based on nnU-Net for automated segmentation of all perigastric veins on contrast-enhanced CT images in patie...

Machine learning versus deep learning for screening ischemic stroke among asymptomatic population.

Ischemic stroke puts great health burden in public. However, the diagnosis is based on head CT or MRI scanning. We aim to develop classifier models to...

Explainable artificial intelligence models in predicting major cardiovascular events: insights from the PolyIran and PolyPars prospective studies.

Cardiovascular diseases are among the leading causes of global mortality and morbidity, underscoring the need for accurate and explainable predictive ...

Predicting major adverse cardiovascular and cerebrovascular events in chronic heart failure: a machine learning study.

BACKGROUND: Heart failure (HF) is a clinical syndrome characterized by impaired cardiac diastolic and systolic function due to structural or functiona...

Early intensive antihypertensive treatment in high-risk population of intracerebral haemorrhage expansion identified by artificial intelligence (ARCHES): study protocol for a multicentre randomised controlled trial.

BACKGROUNDS: Haematoma expansion (HE) is a significant factor in poor outcomes following intracerebral haemorrhage (ICH). Studies have suggested that ...

Comparison of ChatGPT and dietitians in formulating diet plans and recommendations for patients with cardiometabolic diseases.

BACKGROUND: Artificial Intelligence (AI) is transforming personalized medicine, yet its efficacy constitutes a dynamic factor in the field of health a...

An artificial intelligence model to detect abnormal ejection fraction from non-contrast chest computed tomography: the CT-LVEF study.

AIMS: Heart failure (HF), a major global health challenge, affects millions worldwide and poses substantial healthcare and economic burdens. It is est...

AI-assisted diagnosis of cardiac amyloidosis using electrocardiograms and echocardiography: a multicenter retrospective study in China.

BACKGROUND: Cardiac amyloidosis (CA) is an under-recognized cause of left-ventricular hypertrophy (LVH) that is often misclassified as hypertrophic ca...

Transcriptomic analysis identifies novel ferroptosis-related biomarkers and therapeutic targets in pulmonary arterial hypertension.

BACKGROUND: Ferroptosis plays a significant role in pulmonary arterial hypertension (PAH), although its underlying mechanisms and key pathogenic genes...

Smartwatch-derived exercise metrics as predictors of early hypertension: a prospective observational study.

BACKGROUND: Early identification of individuals at risk for hypertension is essential for effective cardiovascular disease. Physiological and activity...

Can AI chatbots effectively respond inquiries for patients with Pulmonary Arterial Hypertension about exercise and physical activity: A comparative study of three chatbots.

OBJECTIVE: The utilization of artificial intelligence-based chatbots in the healthcare field is increasingly prevalent. Nonetheless, the quality of re...

An explainable machine learning method for identifying key atherogenic lipid biomarkers in abdominal obesity among the Southern Chinese population.

BACKGROUND: Abdominal obesity (AO) significantly contributes to cardiometabolic diseases and poses an increasing public health challenge. Composite li...

An interpretable machine learning model for preoperative risk stratification in paediatric congenital heart disease surgery: a retrospective cohort study.

BACKGROUND: Congenital heart disease (CHD), one of the most common birth defects, poses challenges to preoperative risk stratification due to its anat...

PMB-NN: Physiology-centered hybrid AI for personalized hemodynamic monitoring from photoplethysmography.

BACKGROUND AND OBJECTIVE: Continuous monitoring of blood pressure (BP) and hemodynamic parameters such as peripheral resistance (R) and arterial compl...

Deep learning-based arterial waveform analysis for predicting postoperative cerebrovascular events in pediatric patients with Moyamoya disease.

BACKGROUND: Postoperative cerebrovascular events, including transient ischemic attacks, infarctions, and hemorrhages, remain a significant concern in ...

Artificial intelligence for right ventricular assessment: current evidence and future directions.

PURPOSE OF REVIEW: Right ventricular size and function are vital to risk stratification in pulmonary hypertension, valvular disease, and congenital he...