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Public Health Genomics and Precision Health Knowledge Base (v9.0)
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Precision Health Database|Search|Public Health Genomics and Precision Health Knowledge Base (PHGKB)
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Last data update: Apr 25, 2024
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Role of artificial intelligence in early detection of congenital heart diseases in neonates.
Haris Ejaz et al. Front Digit Health 2024 51345814
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Incremental value of machine learning for risk prediction in tetralogy of Fallot.
Ayako Ishikita et al. Heart 2023
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Prenatal diagnosis of hypoplastic left heart syndrome on ultrasound using artificial intelligence: How does performance compare to a current screening programme?
Thomas G Day et al. Prenat Diagn 2023
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A machine learning model for predicting congenital heart defects from administrative data.
Haoming Shi et al. Birth Defects Res 2023
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Fetal electrocardiography and artificial intelligence for prenatal detection of congenital heart disease.
Ivar R de Vries et al. Acta Obstet Gynecol Scand 2023
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Artificial intelligence in cardiovascular diseases: diagnostic and therapeutic perspectives.
Xiaoyu Sun et al. Eur J Med Res 2023 28(1) 242
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A Cardiac Deep Learning Model (CDLM) to Predict and Identify the Risk Factor of Congenital Heart Disease.
Prabu Pachiyannan et al. Diagnostics (Basel) 2023 13(13)
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Predictors of Human Milk Feeding and Direct Breastfeeding for Infants with Single Ventricle Congenital Heart Disease: Machine Learning Analysis of the National Pediatric Cardiology Quality Improvement Collaborative Registry.
Kristin M Elgersma et al. J Pediatr 2023 113562
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Classification of normal and abnormal fetal heart ultrasound images and identification of ventricular septal defects based on deep learning.
Yiru Yang et al. J Perinat Med 2023
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Preterm birth and maternal heart disease: A machine learning analysis using the Korean national health insurance database.
Jue Seong Lee et al. PloS one 2023 18(3) e0283959
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Unsupervised Machine Learning for Assessment of Left Ventricular Diastolic Function and Risk Stratification.
Chao Chieh-Ju et al. Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography 2022
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Machine learning and artificial intelligence in cardiac transplantation: A systematic review.
Naruka Vinci et al. Artificial organs 2022
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Development of a Semi-Automated Database for Adult Congenital Heart Disease Patients.
Verma Shourya et al. The Canadian journal of cardiology 2022
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Patient monitoring and education over a tailored digital application platform for congenital heart disease: A feasibility pilot study.
Nashat Heba et al. International journal of cardiology 2022
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Artificial intelligence in perinatal diagnosis and management of congenital heart disease.
Reddy Charitha D et al. Seminars in perinatology 2022 151588
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From Heart Murmur to Echocardiography - Congenital Heart Defects Diagnostics Using Machine-Learning Algorithms.
Begic Edin et al. Psychiatria Danubina 2022 33(Suppl 13) 236-246
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Explainable machine learning model for predicting the occurrence of postoperative malnutrition in children with congenital heart disease.
Shi Hui et al. Clinical nutrition (Edinburgh, Scotland) 2021 41(1) 202-210
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Artificial Intelligence Supports Decision Making during Open-Chest Surgery of Rare Congenital Heart Defects.
Lo Muzio Francesco Paolo et al. Journal of clinical medicine 2021 10(22)
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Application of Artificial Intelligence to Cardiovascular Computed Tomography.
Yang Dong Hyun et al. Korean journal of radiology 2021
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Machine learning-based risk profile classification of patients undergoing elective heart valve surgery.
Bodenhofer Ulrich et al. European journal of cardio-thoracic surgery : official journal of the European Association for Cardio-thoracic Surgery 2021
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An ensemble of neural networks provides expert-level prenatal detection of complex congenital heart disease.
Arnaout Rima et al. Nature medicine 2021 27(5) 882-891
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Deep learning for detecting congenital heart disease in the fetus
SA Morris et al, Nature Medicine, May 14, 2021
Detecting neurodevelopmental trajectories in congenital heart diseases with a machine-learning approach.
Cainelli Elisa et al. Scientific reports 2021 Jan 11(1) 2574
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Automated interpretation of congenital heart disease from multi-view echocardiograms.
Wang Jing et al. Medical image analysis 2020 Dec 69101942
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[Artificial intelligence technology in cardiac auscultation screening for congenital heart disease: present and future].
Xu Weize et al. Zhejiang da xue xue bao. Yi xue ban = Journal of Zhejiang University. Medical sciences 2020 Oct 49(5) 548-555
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Artificial Intelligence, Machine Learning, and Cardiovascular Disease.
Mathur Pankaj et al. Clinical Medicine Insights. Cardiology 2020 141179546820927404
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Improving preoperative risk-of-death prediction in surgery congenital heart defects using artificial intelligence model: A pilot study.
Chang Junior João et al. PloS one 2020 15(9) e0238199
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Prediction of Pulmonary to Systemic Flow Ratio in Patients With Congenital Heart Disease Using Deep Learning-Based Analysis of Chest Radiographs.
Toba Shuhei et al. JAMA cardiology 2020 Jan
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Artificial intelligence in pediatric and adult congenital cardiac MRI: an unmet clinical need.
Arafati Arghavan et al. Cardiovascular diagnosis and therapy 2019 Oct 9(Suppl 2) S310-S325
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Implementing Artificial Intelligence and Digital Health in Resource-Limited Settings? Top 10 Lessons We Learned in Congenital Heart Defects and Cardiology.
Thomford Nicholas Ekow et al. Omics : a journal of integrative biology 2019 Oct
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Page last reviewed:
Feb 1, 2024
Page last updated:
Apr 25, 2024
Content source:
Public Health Genomics Branch in the Division of Blood Disorders and Public Health Genomics
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National Center on Birth Defects and Developmental Disabilities
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