AIMC Topic: Heart Defects, Congenital

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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 Neural Networks, Computer Heart Defects, Congenital Heart Failure Databases, Factual Recurrence 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...
Heart Murmurs Adolescent Child, Preschool Child Models, Cardiovascular Humans Adult Heart Auscultation Neural Networks, Computer Software Signal Processing, Computer-Assisted Heart Defects, Congenital Male Female
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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...
Male Deep Learning Adolescent Child, Preschool Humans Female Reproducibility of Results Predictive Value of Tests Workflow Image Interpretation, Computer-Assisted Automation Child Age Factors Magnetic Resonance Imaging Heart Defects, Congenital
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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...
Registries Preoperative Period Retrospective Studies Heart Defects, Congenital Male Humans Female Risk Assessment Artificial Intelligence Pilot Projects
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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 ...
Child Magnetic Resonance Imaging Predictive Value of Tests Image Interpretation, Computer-Assisted Heart Defects, Congenital Heart Workflow Humans Prospective Studies Deep Learning Female Adolescent Aged, 80 and over Time Factors Young Adult Child, Preschool Reproducibility of Results Adult Aged Male Middle Aged
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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 Machine Learning Pregnancy Young Adult Adult Female Infant, Newborn Adolescent Middle Aged Pregnancy Complications Logistic Models Risk Assessment Heart Defects, Congenital China
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Deep learning in congenital heart disease imaging: hope but not haste.

Heart (British Cardiac Society) Apr 27, 2020
Deep Learning Image Processing, Computer-Assisted Tetralogy of Fallot Heart Defects, Congenital Humans Prognosis
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Machine Learning in Fetal Cardiology: What to Expect.

Fetal diagnosis and therapy Jan 7, 2020
In fetal cardiology, imaging (especially echocardiography) has demonstrated to help in the diagnosis and monitoring of fetuses with a compromised cardiovascular system potentially associated with several fetal conditions. Different ultrasound approac...
Fetal Heart Humans Echocardiography Machine Learning Ultrasonography, Prenatal Heart Defects, Congenital Pregnancy Prenatal Diagnosis Female
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DW-Net: A cascaded convolutional neural network for apical four-chamber view segmentation in fetal echocardiography.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Dec 23, 2019
Fetal echocardiography (FE) is a widely used medical examination for early diagnosis of congenital heart disease (CHD). The apical four-chamber view (A4C) is an important view among early FE images. Accurate segmentation of crucial anatomical structu...
Artifacts Heart Defects, Congenital Fetal Heart Female Humans Echocardiography Neural Networks, Computer Ultrasonography, Prenatal Pregnancy
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