AIMC Topic: Heart Defects, Congenital

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Learned iterative segmentation of highly variable anatomy from limited data: Applications to whole heart segmentation for congenital heart disease.

Medical image analysis May 13, 2022
Training deep learning models that segment an image in one step typically requires a large collection of manually annotated images that captures the anatomical variability in a cohort. This poses challenges when anatomical variability is extreme but ...
Heart Thorax Heart Defects, Congenital Humans Neural Networks, Computer Image Processing, Computer-Assisted
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Artificial intelligence in perinatal diagnosis and management of congenital heart disease.

Seminars in perinatology Mar 13, 2022
Prenatal diagnosis and management of congenital heart disease (CHD) has progressed substantially in the past few decades. Fetal echocardiography can accurately detect and diagnose approximately 85% of cardiac anomalies. The prenatal diagnosis of CHD ...
Echocardiography Heart Defects, Congenital Prenatal Diagnosis Pregnancy Female Humans Artificial Intelligence Algorithms
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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.
Humans Computers Ventricular Function, Left Ductus Arteriosus, Patent Child Heart Septal Defects, Atrial Stroke Volume Heart Defects, Congenital Deep Learning Sound Recordings 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...
Fetal Heart Female Echocardiography Deep Learning Image Processing, Computer-Assisted Heart Defects, Congenital Humans Computers Pregnancy
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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 (...
Humans Heart Defects, Congenital Clinical Decision Rules Postoperative Complications Infant ROC Curve Predictive Value of Tests Malnutrition Postoperative Period Prospective Studies Female Male Machine Learning Algorithms
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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...
Machine Learning Algorithms Child Humans Support Vector Machine Magnetic Resonance Imaging Heart Defects, Congenital
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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...
Cardiac Surgical Procedures Algorithms Outcome Assessment, Health Care Prognosis Heart Defects, Congenital Female Humans Models, Cardiovascular Machine Learning Infant Child, Preschool ROC Curve Male Postoperative Complications
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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 Electrocardiography Heart Defects, Congenital
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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 Assessment Heart Defects, Congenital Humans Artificial Intelligence Machine Learning Risk Factors
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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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