Cardiovascular

Congestive Heart Failure

Latest AI and machine learning research in congestive heart failure for healthcare professionals.

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Showing 526-546 of 3,377 articles
Noninvasive estimation of aortic hemodynamics and cardiac contractility using machine learning.

Cardiac and aortic characteristics are crucial for cardiovascular disease detection. However, noninv...

Rapid reconstruction of highly undersampled, non-Cartesian real-time cine k-space data using a perceptual complex neural network (PCNN).

Highly accelerated real-time cine MRI using compressed sensing (CS) is a promising approach to achie...

Automated quantification of myocardial tissue characteristics from native T mapping using neural networks with uncertainty-based quality-control.

BACKGROUND: Tissue characterisation with cardiovascular magnetic resonance (CMR) parametric mapping ...

Generalizable fully automated multi-label segmentation of four-chamber view echocardiograms based on deep convolutional adversarial networks.

A major issue in translation of the artificial intelligence platforms for automatic segmentation of ...

Identifying Phenogroups in patients with subclinical diastolic dysfunction using unsupervised statistical learning.

BACKGROUND: Subclinical diastolic dysfunction is a precursor for developing heart failure with prese...

CINENet: deep learning-based 3D cardiac CINE MRI reconstruction with multi-coil complex-valued 4D spatio-temporal convolutions.

Cardiac CINE magnetic resonance imaging is the gold-standard for the assessment of cardiac function....

A Proxy for Detecting IUGR Based on Gestational Age Estimation in a Guatemalan Rural Population.

progress of fetal development is normally assessed through manual measurements taken from ultrasoun...

Machine learning-based classification and diagnosis of clinical cardiomyopathies.

Dilated cardiomyopathy (DCM) and ischemic cardiomyopathy (ICM) are two common types of cardiomyopath...

A Comparison of Three-Dimensional Speckle Tracking Echocardiography Parameters in Predicting Left Ventricular Remodeling.

Three-dimensional speckle tracking echocardiography (3D STE) is an emerging noninvasive method for p...

Genes, the brain, and artificial intelligence in evolution.

Three important systems, genes, the brain, and artificial intelligence (especially deep learning) ha...

Fully automated quantification of left ventricular volumes and function in cardiac MRI: clinical evaluation of a deep learning-based algorithm.

To investigate the performance of a deep learning-based algorithm for fully automated quantification...

Development of an artificial intelligence diagnostic model based on dynamic uncertain causality graph for the differential diagnosis of dyspnea.

Dyspnea is one of the most common manifestations of patients with pulmonary disease, myocardial dysf...

Continuous blood pressure measurement from one-channel electrocardiogram signal using deep-learning techniques.

Continuous blood pressure (BP) measurement is crucial for reliable and timely hypertension detection...

Positron emission tomography imaging in cardiovascular disease.

Positron emission tomography (PET) imaging is useful in cardiovascular disease across several areas,...

An innovative method for screening and evaluating the degree of diabetic retinopathy and drug treatment based on artificial intelligence algorithms.

Current methods of evaluating the degree of diabetic retinopathy are highly subjective and have no q...

Estimating Blood Pressure from the Photoplethysmogram Signal and Demographic Features Using Machine Learning Techniques.

Hypertension is a potentially unsafe health ailment, which can be indicated directly from the blood ...

Detecting cardiac pathologies via machine learning on heart-rate variability time series and related markers.

In this paper we develop statistical algorithms to infer possible cardiac pathologies, based on data...

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