AIMC Topic: Heart Defects, Congenital

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Deep learning-based computer-aided heart sound analysis in children with left-to-right shunt congenital heart disease.

International journal of cardiology Dec 10, 2021
OBJECTIVE: The purpose of this study was to explore a new algorithm model capable of leverage deep learning to screen and diagnose specific types of left-to-right shunt congenital heart disease (CHD) in children.
Ventricular Function, Left Stroke Volume Deep Learning Humans Ductus Arteriosus, Patent Computers Heart Septal Defects, Atrial Sound Recordings Child Heart Defects, Congenital Heart Sounds
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Deep Learning-Based Computer-Aided Fetal Echocardiography: Application to Heart Standard View Segmentation for Congenital Heart Defects Detection.

Sensors (Basel, Switzerland) Nov 30, 2021
Accurate segmentation of fetal heart in echocardiography images is essential for detecting the structural abnormalities such as congenital heart defects (CHDs). Due to the wide variations attributed to different factors, such as maternal obesity, abd...
Computers Pregnancy Female Humans Image Processing, Computer-Assisted Echocardiography Heart Defects, Congenital Deep Learning Fetal Heart
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Explainable machine learning model for predicting the occurrence of postoperative malnutrition in children with congenital heart disease.

Clinical nutrition (Edinburgh, Scotland) Nov 10, 2021
BACKGROUND & AIMS: Malnutrition is persistent in 50%-75% of children with congenital heart disease (CHD) after surgery, and early prediction is crucial for nutritional intervention. The aim of this study was to develop and validate machine learning (...
Predictive Value of Tests Malnutrition Postoperative Period Infant ROC Curve Heart Defects, Congenital Clinical Decision Rules Machine Learning Algorithms Prospective Studies Female Male Postoperative Complications Humans
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The role of machine learning applications in diagnosing and assessing critical and non-critical CHD: a scoping review.

Cardiology in the young Nov 2, 2021
Machine learning uses historical data to make predictions about new data. It has been frequently applied in healthcare to optimise diagnostic classification through discovery of hidden patterns in data that may not be obvious to clinicians. Congenita...
Heart Defects, Congenital Humans Machine Learning Algorithms Child Support Vector Machine Magnetic Resonance Imaging
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Explainable machine-learning predictions for complications after pediatric congenital heart surgery.

Scientific reports Aug 26, 2021
The quality of treatment and prognosis after pediatric congenital heart surgery remains unsatisfactory. A reliable prediction model for postoperative complications of congenital heart surgery patients is essential to enable prompt initiation of thera...
Heart Defects, Congenital Female Models, Cardiovascular Male Cardiac Surgical Procedures Machine Learning Outcome Assessment, Health Care Postoperative Complications Humans Algorithms Infant Prognosis Child, Preschool ROC Curve
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ECG for Screening Cardiac Abnormalities: The Premise and Promise of Machine Learning.

Circulation. Cardiovascular imaging Jun 15, 2021
Humans Machine Learning Heart Defects, Congenital Electrocardiography
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Adverse Outcomes Prediction for Congenital Heart Surgery: A Machine Learning Approach.

World journal for pediatric & congenital heart surgery Apr 28, 2021
OBJECTIVE: Risk assessment tools typically used in congenital heart surgery (CHS) assume that various possible risk factors interact in a linear and additive fashion, an assumption that may not reflect reality. Using artificial intelligence technique...
Risk Factors Risk Assessment Heart Defects, Congenital Humans Artificial Intelligence Machine Learning
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A Myocardial Segmentation Method Based on Adversarial Learning.

BioMed research international Feb 26, 2021
Congenital heart defects (CHD) are structural imperfections of the heart or large blood vessels that are detected around birth and their symptoms vary wildly, with mild case patients having no obvious symptoms and serious cases being potentially life...
Heart Defects, Congenital Imaging, Three-Dimensional Humans Magnetic Resonance Imaging Machine Learning Myocardium
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Detecting neurodevelopmental trajectories in congenital heart diseases with a machine-learning approach.

Scientific reports Jan 28, 2021
We aimed to delineate the neuropsychological and psychopathological profiles of children with congenital heart disease (CHD) and look for associations with clinical parameters. We conducted a prospective observational study in children with CHD who u...
Cardiac Surgical Procedures Heart Defects, Congenital Humans Prospective Studies Machine Learning Surveys and Questionnaires Cognition
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MVP predicts the pathogenicity of missense variants by deep learning.

Nature communications Jan 21, 2021
Accurate pathogenicity prediction of missense variants is critically important in genetic studies and clinical diagnosis. Previously published prediction methods have facilitated the interpretation of missense variants but have limited performance. H...
Humans Algorithms Autism Spectrum Disorder Deep Learning Reproducibility of Results Heart Defects, Congenital Genetic Predisposition to Disease Sensitivity and Specificity Mutation, Missense Computational Biology Neoplasms
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