Deep learning for detecting congenital heart disease in the fetus
- 14 May 2021
- journal article
- editorial
- Published by Springer Science and Business Media LLC in Nature Medicine
- Vol. 27 (5), 764-765
- https://doi.org/10.1038/s41591-021-01354-1
Abstract
No abstract availableKeywords
This publication has 13 references indexed in Scilit:
- Association of Maternal Psychological Distress With In Utero Brain Development in Fetuses With Congenital Heart DiseaseJAMA Pediatrics, 2020
- Deep learning interpretation of echocardiogramsnpj Digital Medicine, 2020
- Why are congenital heart defects being missed?Ultrasound in Obstetrics & Gynecology, 2019
- Current applications of big data and machine learning in cardiology2019
- Prenatal Diagnosis Influences Preoperative Status in Neonates with Congenital Heart Disease: An Analysis of the Society of Thoracic Surgeons Congenital Heart Surgery DatabasePediatric Cardiology, 2018
- A randomised trial of early palliative care for maternal stress in infants prenatally diagnosed with single-ventricle heart diseaseCardiology in the Young, 2018
- Association of Prenatal Diagnosis of Critical Congenital Heart Disease With Postnatal Brain Development and the Risk of Brain InjuryJAMA Pediatrics, 2016
- Variation in Prenatal Diagnosis of Congenital Heart Disease in InfantsPEDIATRICS, 2015
- Barriers to prenatal detection of congenital heart disease: a population‐based studyUltrasound in Obstetrics & Gynecology, 2012
- Congenital Heart Disease in the General PopulationJournal of the American College of Cardiology, 2007