AIMC Topic: Heart Defects, Congenital

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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...

Steroids Improve Hemodynamics in Infants With Adrenal Insufficiency After Cardiac Surgery.

Journal of cardiothoracic and vascular anesthesia
OBJECTIVE: To investigate whether steroid replacement therapy improved hemodynamics in infants after surgery for congenital heart disease only when they develop adrenal insufficiency. The authors retrospectively investigated adrenal function and eval...

Totally robotic repair of atrioventricular septal defect in the adult.

Journal of cardiothoracic surgery
BACKGROUND: Atrioventricular septal defect (AVSD) accounts for up to 3 % of congenital cardiac defects, which is routinely repaired via median sternotomy. Minimally invasive approach such as endoscopic or robotic assisted repair for AVSD has not been...

Identification of Mitral Annulus Hinge Point Based on Local Context Feature and Additive SVM Classifier.

Computational and mathematical methods in medicine
The position of the hinge point of mitral annulus (MA) is important for segmentation, modeling and multimodalities registration of cardiac structures. The main difficulties in identifying the hinge point of MA are the inherent noisy, low resolution o...

Machine Learning for Predicting Waitlist Mortality in Pediatric Heart Transplantation.

Pediatric transplantation
BACKGROUND: Waitlist mortality remains a critical issue for pediatric heart transplant (HTx) candidates, particularly for candidates with congenital heart disease. Listing center organ offer acceptance practices have been identified as a factor influ...

Development and validation of an integrated residual-recurrent neural network model for automated heart murmur detection in pediatric populations.

Scientific reports
Congenital heart disease affects approximately 1% of children worldwide, with a number of cases in resource-limited settings remaining undiagnosed through school age. While cardiac auscultation is a key screening method, its effectiveness varies wide...

Electrocardiogram-based deep learning to predict left ventricular systolic dysfunction in paediatric and adult congenital heart disease in the USA: a multicentre modelling study.

The Lancet. Digital health
BACKGROUND: Left ventricular systolic dysfunction (LVSD) is independently associated with cardiovascular events in patients with congenital heart disease. Although artificial intelligence-enhanced electrocardiogram (AI-ECG) analysis is predictive of ...

Electrocardiogram-based deep learning to predict mortality in paediatric and adult congenital heart disease.

European heart journal
BACKGROUND AND AIMS: Robust and convenient risk stratification of patients with paediatric and adult congenital heart disease (CHD) is lacking. This study aims to address this gap with an artificial intelligence-enhanced electrocardiogram (ECG) tool ...