AIMC Topic: Heart Defects, Congenital

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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...
Humans Machine Learning Prospective Studies Heart Defects, Congenital Surveys and Questionnaires Cognition Cardiac Surgical Procedures
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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...
Mutation, Missense Reproducibility of Results Sensitivity and Specificity Neoplasms Computational Biology Heart Defects, Congenital Deep Learning Genetic Predisposition to Disease Humans Algorithms Autism Spectrum Disorder
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A new approach to modelling in adult congenital heart disease: artificial intelligence.

Revista espanola de cardiologia (English ed.) Jan 18, 2021
Algorithms Adult Heart Defects, Congenital Humans Artificial Intelligence
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Recurrent disease progression networks for modelling risk trajectory of heart failure.

PloS one Jan 6, 2021
MOTIVATION: Recurrent neural networks (RNN) are powerful frameworks to model medical time series records. Recent studies showed improved accuracy of predicting future medical events (e.g., readmission, mortality) by leveraging large amount of high-di...
Humans Disease Progression Heart Failure Heart Defects, Congenital Recurrence Neural Networks, Computer Databases, Factual Risk Factors
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Automatic recognition of murmurs of ventricular septal defect using convolutional recurrent neural networks with temporal attentive pooling.

Scientific reports Dec 11, 2020
Recognizing specific heart sound patterns is important for the diagnosis of structural heart diseases. However, the correct recognition of heart murmur depends largely on clinical experience. Accurately identifying abnormal heart sound patterns is ch...
Adolescent Child, Preschool Child Signal Processing, Computer-Assisted Female Adult Male Heart Murmurs Heart Defects, Congenital Models, Cardiovascular Heart Auscultation Neural Networks, Computer Software Humans
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Fully‑automated deep‑learning segmentation of pediatric cardiovascular magnetic resonance of patients with complex congenital heart diseases.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance Nov 30, 2020
BACKGROUND: For the growing patient population with congenital heart disease (CHD), improving clinical workflow, accuracy of diagnosis, and efficiency of analyses are considered unmet clinical needs. Cardiovascular magnetic resonance (CMR) imaging of...
Workflow Reproducibility of Results Image Interpretation, Computer-Assisted Male Automation Humans Female Heart Defects, Congenital Child Age Factors Magnetic Resonance Imaging Predictive Value of Tests Deep Learning Adolescent Child, Preschool
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Improving preoperative risk-of-death prediction in surgery congenital heart defects using artificial intelligence model: A pilot study.

PloS one Sep 4, 2020
BACKGROUND: Congenital heart disease accounts for almost a third of all major congenital anomalies. Congenital heart defects have a significant impact on morbidity, mortality and health costs for children and adults. Research regarding the risk of pr...
Pilot Projects Female Male Risk Assessment Humans Artificial Intelligence Retrospective Studies Heart Defects, Congenital Registries Preoperative Period
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Rapid whole-heart CMR with single volume super-resolution.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance Aug 3, 2020
BACKGROUND: Three-dimensional, whole heart, balanced steady state free precession (WH-bSSFP) sequences provide delineation of intra-cardiac and vascular anatomy. However, they have long acquisition times. Here, we propose significant speed-ups using ...
Aged Aged, 80 and over Young Adult Middle Aged Deep Learning Adult Male Humans Prospective Studies Female Reproducibility of Results Workflow Image Interpretation, Computer-Assisted Heart Defects, Congenital Child Magnetic Resonance Imaging Predictive Value of Tests Heart Adolescent Time Factors Child, Preschool
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Predicting the Risk of Adverse Events in Pregnant Women With Congenital Heart Disease.

Journal of the American Heart Association Jul 14, 2020
Background Women with congenital heart disease are considered at high risk for adverse events. Therefore, we aim to establish 2 prediction models for mothers and their offspring, which can predict the risk of adverse events occurred in pregnant women...
Humans Logistic Models Machine Learning Pregnancy Complications Pregnancy Heart Defects, Congenital Female Middle Aged Risk Assessment China Infant, Newborn Adolescent Young Adult Adult
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Deep learning in congenital heart disease imaging: hope but not haste.

Heart (British Cardiac Society) Apr 27, 2020
Image Processing, Computer-Assisted Tetralogy of Fallot Prognosis Heart Defects, Congenital Deep Learning Humans
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