AIMC Topic: Heart Defects, Congenital

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Prediction of pulmonary pressure after Glenn shunts by computed tomography-based machine learning models.

European radiology Nov 8, 2019
OBJECTIVES: This study aimed to develop non-invasive machine learning classifiers for predicting post-Glenn shunt patients with low and high risks of a mean pulmonary arterial pressure (mPAP) > 15 mmHg based on preoperative cardiac computed tomograph...
Logistic Models Support Vector Machine Blood Pressure Bayes Theorem Discriminant Analysis Lung Adolescent Child, Preschool Child Cardiac Catheterization Transposition of Great Vessels Pulmonary Artery Heart Septal Defects Univentricular Heart Double Outlet Right Ventricle Fontan Procedure Heart Defects, Congenital Pulmonary Atresia Tricuspid Atresia Machine Learning Algorithms Prognosis Retrospective Studies Tomography, X-Ray Computed Humans Female Young Adult Male Infant
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Fetal Congenital Heart Disease Echocardiogram Screening Based on DGACNN: Adversarial One-Class Classification Combined with Video Transfer Learning.

IEEE transactions on medical imaging Oct 7, 2019
Fetal congenital heart disease (FHD) is a common and serious congenital malformation in children. In Asia, FHD birth defect rates have reached as high as 9.3%. For the early detection of birth defects and mortality, echocardiography remains the most ...
Humans Algorithms Deep Learning Video Recording Image Interpretation, Computer-Assisted Heart Defects, Congenital Pregnancy Female Echocardiography Ultrasonography, Prenatal
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Semiautomatic Fetal Intelligent Navigation Echocardiography Has the Potential to Aid Cardiac Evaluations Even in Less Experienced Hands.

Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine Aug 14, 2019
OBJECTIVES: To investigate the interobserver and intraobserver variability and corresponding learning curve in a semiautomatic approach for a standardized assessment of the fetal heart (fetal intelligent navigation echocardiography [FINE]).
Humans Artificial Intelligence Pregnancy Female Heart Defects, Congenital Learning Curve Echocardiography, Four-Dimensional Ultrasonography, Prenatal Clinical Competence Observer Variation Pregnancy Trimester, Second
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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 Jul 19, 2019
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 ...
Heart Defects, Congenital Predictive Value of Tests Image Interpretation, Computer-Assisted Artifacts Signal-To-Noise Ratio Female Reproducibility of Results Young Adult Adult Male Echocardiography Middle Aged Case-Control Studies Deep Learning Humans
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Artificial Intelligence-Assisted Auscultation of Heart Murmurs: Validation by Virtual Clinical Trial.

Pediatric cardiology Dec 12, 2018
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...
Artificial Intelligence Child Algorithms Heart Defects, Congenital Infant Databases, Factual Aged Heart Auscultation Diagnosis, Computer-Assisted Aged, 80 and over Heart Murmurs Adolescent Young Adult Child, Preschool Adult Humans Sensitivity and Specificity Middle Aged
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Real-time cardiovascular MR with spatio-temporal artifact suppression using deep learning-proof of concept in congenital heart disease.

Magnetic resonance in medicine Sep 8, 2018
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...
Retrospective Studies Young Adult Adult Male Middle Aged Deep Learning Adolescent Image Processing, Computer-Assisted Heart Humans Algorithms Fourier Analysis Breath Holding Image Interpretation, Computer-Assisted Magnetic Resonance Imaging, Cine Artifacts Cardiac-Gated Imaging Techniques Respiration Heart Defects, Congenital
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A study of time-frequency features for CNN-based automatic heart sound classification for pathology detection.

Computers in biology and medicine Jun 25, 2018
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...
Signal Processing, Computer-Assisted Humans Neural Networks, Computer Databases, Factual Heart Defects, Congenital Heart Sounds
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A computational framework for the detection of subcortical brain dysmaturation in neonatal MRI using 3D Convolutional Neural Networks.

NeuroImage May 21, 2018
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...
Cerebellar Diseases Humans Magnetic Resonance Imaging Neural Networks, Computer Cerebellum Infant, Newborn Pattern Recognition, Automated Deep Learning Neuroimaging Image Processing, Computer-Assisted Heart Defects, Congenital
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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 Feb 19, 2018
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 ...
Particulate Matter Beijing Female Maternal Exposure Humans Heart Defects, Congenital Adult Machine Learning Air Pollutants Computer Simulation Air Pollution Pregnancy
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Biomarker and shear stress in secondary pediatric pulmonary hypertension.

Turkish journal of medical sciences Dec 19, 2017
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...
Child Echocardiography Humans Heart Defects, Congenital ROC Curve Biomarkers Infant Shear Strength Female Adolescent Hypertension, Pulmonary Male Cohort Studies Child, Preschool
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