Congestive Heart Failure

Classification of normal sinus rhythm, abnormal arrhythmia and congestive heart failure ECG signals using LSTM and hybrid CNN-SVM deep neural networks.

Effective monitoring of heart patients according to heart signals can save a huge amount of life. In the last decade, the classification and prediction of heart diseases according to ECG signals has gained great importance for patients and doctors. In this paper, the deep learning architecture with high accuracy and popularity has been proposed in recent years for the classification of Normal Sinu...

Automatic detection of non-perfusion areas in diabetic macular edema from fundus fluorescein angiography for decision making using deep learning.

Vision loss caused by diabetic macular edema (DME) can be prevented by early detection and laser photocoagulation. As there is no comprehensive detection technique to recognize NPA, we proposed an automatic detection method of NPA on fundus fluorescein angiography (FFA) in DME. The study included 3,014 FFA images of 221 patients with DME. We use 3 convolutional neural networks (CNNs), including De...

Noninvasive estimation of aortic hemodynamics and cardiac contractility using machine learning.

Cardiac and aortic characteristics are crucial for cardiovascular disease detection. However, noninvasive estimation of aortic hemodynamics and cardia...

Computer versus cardiologist: Is a machine learning algorithm able to outperform an expert in diagnosing a phospholamban p.Arg14del mutation on the electrocardiogram?

BACKGROUND: Phospholamban (PLN) p.Arg14del mutation carriers are known to develop dilated and/or arrhythmogenic cardiomyopathy, and typical electrocar...

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 achieve high spatio-temporal resolution and clinically ...

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 has the potential to detect and quantify both foca...

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 echocardiograms to clinics is their generalizabili...

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

BACKGROUND: Subclinical diastolic dysfunction is a precursor for developing heart failure with preserved ejection fraction (HFpEF); yet not all patien...

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. Imaging accelerations have shown to enable 3D CIN...

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 ultrasound images, requiring relatively expensive equipment...

Artificial Intelligence-Enabled ECG Algorithm to Identify Patients With Left Ventricular Systolic Dysfunction Presenting to the Emergency Department With Dyspnea.

BACKGROUND: Identification of systolic heart failure among patients presenting to the emergency department (ED) with acute dyspnea is challenging. The...

Machine learning-based classification and diagnosis of clinical cardiomyopathies.

Dilated cardiomyopathy (DCM) and ischemic cardiomyopathy (ICM) are two common types of cardiomyopathies leading to heart failure. Accurate diagnostic ...

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 predicting left ventricular remodeling (LVR) after ...

Genes, the brain, and artificial intelligence in evolution.

Three important systems, genes, the brain, and artificial intelligence (especially deep learning) have similar goals, namely, the maximization of like...

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 of left ventricular (LV) volumes and function in ...

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 dysfunction, and neuromuscular disorder, among other c...

Machine Learning Clustering for Blood Pressure Variability Applied to Systolic Blood Pressure Intervention Trial (SPRINT) and the Hong Kong Community Cohort.

Visit-to-visit blood pressure variability (BPV) has been shown to be a predictor of cardiovascular disease. We aimed to classify the BPV levels using ...

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. State-of-the-art continuous BP measurement metho...

Positron emission tomography imaging in cardiovascular disease.

Positron emission tomography (PET) imaging is useful in cardiovascular disease across several areas, from assessment of myocardial perfusion and viabi...

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 quantitative standard. To objectively evaluate the ...