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Heart Defects, Congenital

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Denoising and artefact removal for transthoracic echocardiographic imaging in congenital heart disease: utility of diagnosis specific deep learning algorithms.

The international journal of cardiovascular imaging
Deep learning (DL) algorithms are increasingly used in cardiac imaging. We aimed to investigate the utility of DL algorithms in de-noising transthoracic echocardiographic images and removing acoustic shadowing artefacts specifically in patients with ...

Artificial Intelligence-Assisted Auscultation of Heart Murmurs: Validation by Virtual Clinical Trial.

Pediatric cardiology
Artificial intelligence (AI) has potential to improve the accuracy of screening for valvular and congenital heart disease by auscultation. However, despite recent advances in signal processing and classification algorithms focused on heart sounds, cl...

Real-time cardiovascular MR with spatio-temporal artifact suppression using deep learning-proof of concept in congenital heart disease.

Magnetic resonance in medicine
PURPOSE: Real-time assessment of ventricular volumes requires high acceleration factors. Residual convolutional neural networks (CNN) have shown potential for removing artifacts caused by data undersampling. In this study, we investigated the ability...

A study of time-frequency features for CNN-based automatic heart sound classification for pathology detection.

Computers in biology and medicine
This study concerns the task of automatic structural heart abnormality risk detection from digital phonocardiogram (PCG) signals aiming at pediatric heart disease screening applications. Recently, various systems based on convolutional neural network...

A computational framework for the detection of subcortical brain dysmaturation in neonatal MRI using 3D Convolutional Neural Networks.

NeuroImage
Deep neural networks are increasingly being used in both supervised learning for classification tasks and unsupervised learning to derive complex patterns from the input data. However, the successful implementation of deep neural networks using neuro...

Maternal exposure to ambient PM during pregnancy increases the risk of congenital heart defects: Evidence from machine learning models.

The Science of the total environment
Previous research suggested an association between maternal exposure to ambient air pollutants and risk of congenital heart defects (CHDs), though the effects of particulate matter ≤10μm in aerodynamic diameter (PM) on CHDs are inconsistent. We used ...

Biomarker and shear stress in secondary pediatric pulmonary hypertension.

Turkish journal of medical sciences
Background/aim: Endothelial dysfunction, tissue damage, inflammation, and microthrombosis are involved in the pathogenesis of pulmonary hypertension (PH), which may be present as a complication of congenital heart diseases. This study aims to identif...

Knowledge-based reconstruction for measurement of right ventricular volumes on cardiovascular magnetic resonance images in a mixed population.

Congenital heart disease
OBJECTIVE: Follow-up of right ventricular performance is important for patients with congenital heart disease. Cardiac magnetic resonance imaging is optimal for this purpose. However, observer-dependency of manual analysis of right ventricular volume...

Cardiac Biomarkers of Low Cardiac Output Syndrome in the Postoperative Period After Congenital Heart Disease Surgery in Children.

Revista espanola de cardiologia (English ed.)
INTRODUCTION AND OBJECTIVES: To assess the predictive value of atrial natriuretic peptide, β-type natriuretic peptide, copeptin, mid-regional pro-adrenomedullin (MR-proADM) and cardiac troponin I (cTn-I) as indicators of low cardiac output syndrome i...