Hypertension

Clinical Phenotypes in Hypertension: A Data-Driven Approach to Risk Stratification.

BACKGROUND: Hypertension is a major contributor to cardiovascular morbidity and mortality. Its heterogeneity complicates risk stratification. Unsupervised machine learning can uncover risk profiles and refine preventative strategies. This study applied a data-driven approach to identify clinical phenotypes of hypertension, examine their associations with cardiovascular imaging characteristics and ...

Reproducibility of echocardiographic measurements of left ventricular systolic function: a systematic review and meta-analysis comparing artificial intelligence and clinician estimates.

Echocardiography underpins the diagnosis and management of cardiovascular disease, yet measurement variability can influence treatment decisions. Artificial intelligence (AI) may standardize interpretation, but its reproducibility and clinical impact require systematic evaluation. To compare the reproducibility of AI-derived and clinician-derived measurements of left ventricular (LV) systolic func...

Value of Multiomics Over Clinical Risk Factors in Hypertension Prediction.

BACKGROUND: Several omics methods have been successfully used in hypertension prediction. However, the predictive ability of various multiomics data h...

Explainable machine learning-driven predictive modelling of incident cognitive impairment in individuals with early-stage cardiovascular-kidney-metabolic syndrome: Insights from a longitudinal CHARLS cohort study.

AIMS: While cardiovascular-kidney-metabolic (CKM) syndrome has been recognised as a continuum of interconnected metabolic, renal, and cardiovascular d...

Predictors of glycemic control with imeglimin for type 2 diabetes: Results of machine learning analyses using clinical trial data.

INTRODUCTION: Identifying patient characteristics predictive of treatment response is crucial for optimizing type 2 diabetes outcomes. Using data from...

Enlarged Perivascular Spaces and Modifiable Vascular Risk Factors: Cross-Sectional and Longitudinal Analysis in the UK Biobank Cohort.

OBJECTIVE: To examine cross-sectional and longitudinal associations between vascular risk factors, APOE genotype, and perivascular spaces (PVS), with ...

Deep learning model for identifying significant tricuspid regurgitation using standard 12-lead electrocardiogram.

INTRODUCTION: Transthoracic echocardiography (TTE) is the current standard for detecting tricuspid regurgitation (TR); however, it incurs additional c...

Fully Automated Plane Prescription in Cardiac MRI: A Prospective Cohort Study.

BACKGROUND: Accurate plane positioning is important for high-quality cardiac MRI images but requires specialized training, limiting accessibility. PUR...

Personalized adrenal gland volume reference ranges and development of a fully automated deep learning screening tool.

OBJECTIVE: This study aims to develop a low-dose CT-based, fully automated deep learning tool for screening adrenal gland volume abnormalities and est...

Machine Learning-Based Prediction of Postoperative Vasoplegia After Cardiac Surgery.

OBJECTIVE: To develop and internally validate a machine-learning model for the early prediction of postoperative vasoplegia after cardiac surgery. DES...

Peripapillary Choroidal Vascularity Index for Differentiating Papilledema from Pseudopapilledema: A Deep Learning-Based Approach.

PURPOSE: To compare the peripapillary choroidal vascularity index (PPCVI) in eyes with papilledema secondary to idiopathic intracranial hypertension (...

Development and validation of a machine learning model to predict 30-day mortality in ischemic stroke patients with consciousness impairment: Insights from MIMIC-IV database and multicenter ICU data in China.

BACKGROUND: Patients with ischemic stroke complicated by consciousness disorders remain associated with high mortality risks. This study aims to devel...

Deep learning-based obstructive coronary artery disease prediction from myocardial perfusion SPECT.

PURPOSE: We aim to apply the deep learning (DL) technique to predict the gold-standard invasive coronary angiography (ICA) for coronary artery disease...

Using Artificial Intelligence to Adjudicate Major Adverse Cardiovascular Events in Clinical Trials.

BACKGROUND: Major adverse cardiovascular events (MACE)-cardiovascular (CV) death, nonfatal myocardial infarction (MI), and nonfatal stroke-are highly ...

Artificial intelligence in nephrology: predicting CKD progression and personalizing treatment.

Chronic kidney disease (CKD) represents a major and expanding global health challenge, with prevalence rising due to aging populations, diabetes, hype...

Predicting Heart Failure From 12-Lead ECGs Using AI: A HeartShare/AMP-HF Pooled Cohort Analysis.

BACKGROUND: Artificial intelligence applied to electrocardiograms (ECG-AI) offers a scalable approach to identify individuals at risk for heart failur...

From Cuffs to Code: Machine Learning in Non-Invasive Blood Pressure Monitoring.

Blood pressure (BP) measurement in both acute care and outpatient settings is essential, as conditions like hypertension and hypotension are common an...

Development and Deployment of a Machine Learning Model to Triage the Use of Prostate MRI (ProMT-ML) in Patients With Suspected Prostate Cancer.

BACKGROUND: Access to prostate MRI remains limited due to resource constraints and the need for expert interpretation. PURPOSE: To develop machine lea...

Artificial intelligence for arterial blood gas interpretation.

Arterial blood gas (ABG) analysis is a fundamental diagnostic tool in clinical medicine, offering critical insights into a patient's respiratory and m...

Association between triglyceride-glucose index and depression and the mediating role of hypertension: a cross-sectional study of obese adults.

BACKGROUND: Recent findings indicate a positive correlation between the TyG (triglyceride-glucose) index and the incidence of depression. However, the...