Hypertension

Prediction model of in-hospital cardiac arrest using machine learning in the early phase of hospitalization.

In hospitals, the deterioration of a patient's condition leading to death is often preceded by physi...

A review of machine learning methods for non-invasive blood pressure estimation.

Blood pressure is a very important clinical measurement, offering valuable insights into the hemodyn...

Factors associated with 90-day mortality in Vietnamese stroke patients: Prospective findings compared with explainable machine learning, multicenter study.

The prevalence and predictors of mortality following an ischemic stroke or intracerebral hemorrhage ...

Deep Learning Model of Diastolic Dysfunction Risk Stratifies the Progression of Early-Stage Aortic Stenosis.

BACKGROUND: The development and progression of aortic stenosis (AS) from aortic valve (AV) sclerosis...

Unsupervised Machine Learning to Identify Risk Factors of Pyeloplasty Failure in Ureteropelvic Junction Obstruction.

In adult patients with ureteropelvic junction obstruction (UPJO), little data exist on predicting p...

Enhancing reginal wall abnormality detection accuracy: Integrating machine learning, optical flow algorithms, and temporal convolutional networks in multi-view echocardiography.

BACKGROUND: Regional Wall Motion Abnormality (RWMA) serves as an early indicator of myocardial infar...

Mobil Monitoring Doppler Ultrasound (MoMDUS) study: protocol for a prospective, observational study investigating the use of artificial intelligence and low-cost Doppler ultrasound for the automated quantification of hypertension, pre-eclampsia and fetal growth restriction in rural Guatemala.

INTRODUCTION: Undetected high-risk conditions in pregnancy are a leading cause of perinatal mortalit...

Digital therapeutics in hypertension: How to make sustainable lifestyle changes.

Various digital therapeutic products have been validated and approved since 2017. They have demonstr...

Validation of neuron activation patterns for artificial intelligence models in oculomics.

Recent advancements in artificial intelligence (AI) have prompted researchers to expand into the fie...

Deep learning method with integrated invertible wavelet scattering for improving the quality ofcardiac DTI.

Respiratory motion, cardiac motion and inherently low signal-to-noise ratio (SNR) are major limitati...

The promise of machine learning models in cardiovascular risk prediction : Machine learning predictions of the adverse events of different treatments in patients with ischemic left ventricular systolic dysfunction.

Artificial intelligence guided screening for cardiomyopathies in an obstetric population: a pragmatic randomized clinical trial.

Nigeria has the highest reported incidence of peripartum cardiomyopathy worldwide. This open-label, ...

Automated echocardiographic diastolic function grading: A hybrid multi-task deep learning and machine learning approach.

BACKGROUND: Assessing left ventricular diastolic function (LVDF) with echocardiography as per ASE gu...

EFNet: A multitask deep learning network for simultaneous quantification of left ventricle structure and function.

PURPOSE: The purpose of this study is to develop an automated method using deep learning for the rel...

Deep learning-based segmentation in MRI-(immuno)histological examination of myelin and axonal damage in normal-appearing white matter and white matter hyperintensities.

The major vascular cause of dementia is cerebral small vessel disease (SVD). Its diagnosis relies on...

Applying masked autoencoder-based self-supervised learning for high-capability vision transformers of electrocardiographies.

The generalization of deep neural network algorithms to a broader population is an important challen...

A Deep-Learning-Enabled Electrocardiogram and Chest X-Ray for Detecting Pulmonary Arterial Hypertension.

The diagnosis and treatment of pulmonary hypertension have changed dramatically through the re-defin...

Assessment of left ventricular wall thickness and dimension: accuracy of a deep learning model with prediction uncertainty.

Left ventricular (LV) geometric patterns aid clinicians in the diagnosis and prognostication of vari...

Hierarchical Hybrid Networks for Automatic Pulmonary Blood Vessel Segmentation in Computed Tomography Images.

Pulmonary arterial hypertension (PAH) is considered the third most common cardiovascular disease aft...

Sex and population differences in the cardiometabolic continuum: a machine learning study using the UK Biobank and ELSA-Brasil cohorts.

BACKGROUND: The temporal relationships across cardiometabolic diseases (CMDs) were recently conceptu...

Non-Contact Blood Pressure Estimation From Radar Signals by a Stacked Deformable Convolution Network.

This study introduces a contactless blood pressure monitoring approach that combines conventional ra...