ArticleJournal of medical imaging (Bellingham, Wash.)2017
Automated detection of coarctation of aorta in neonates from two-dimensional echocardiograms.
Article in Journal of medical imaging (Bellingham, Wash.), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Real-time deep learning interpretation of echocardiographic video for automated detection of anatomical features associated with tetralogy of fallot in pediatric patients : a feasibility study.Scientific reports · 2026Article
- Artificial Intelligence in Adult Congenital Heart Disease: Diagnostic and Therapeutic Applications and Future Directions.Reviews in cardiovascular medicine · 2025Review
- Role of Artificial Intelligence in Congenital Heart Disease and Interventions.Journal of the Society for Cardiovascular Angiography & Interventions · 2025Review
- An Automated View Classification Model for Pediatric Echocardiography Using Artificial Intelligence.Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography · 2022Article
- Artificial Intelligence in Congenital Heart Disease: Current State and Prospects.JACC. Advances · 2022Review
- Analysis of facial ultrasonography images based on deep learning.Scientific reports · 2022Article
- The role of machine learning applications in diagnosing and assessing critical and non-critical CHD: a scoping review.Cardiology in the young · 2021Article
- Diagnostic Accuracy of Machine Learning Models to Identify Congenital Heart Disease: A Meta-Analysis.Frontiers in artificial intelligence · 2021Review
- Deep Learning Assisted Detection of Abdominal Free Fluid in Morison's Pouch During Focused Assessment With Sonography in Trauma.Frontiers in medicine · 2021Article
- Classification of Lung Disease in Children by Using Lung Ultrasound Images and Deep Convolutional Neural Network.Frontiers in physiology · 2021Article
- Routine Echocardiography and Artificial Intelligence Solutions.Frontiers in cardiovascular medicine · 2021Review
- Image Segmentation of the Ventricular Septum in Fetal Cardiac Ultrasound Videos Based on Deep Learning Using Time-Series Information.Biomolecules · 2020Article
- Suprasternal notch echocardiography: a potential alternative for the measurement of respiratory variation in aortic blood flow peak velocity in mechanically ventilated children.Journal of clinical monitoring and computing · 2018Observational
- ELHnet: a convolutional neural network for classifying cochlear endolymphatic hydrops imaged with optical coherence tomography.Biomedical optics express · 2017Article
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Authors and funding
8 authors.
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Abstract
Coarctation of aorta (CoA) is a critical congenital heart defect (CCHD) that requires accurate and immediate diagnosis and treatment. Current newborn screening methods to detect CoA lack both in sensitivity and specificity, and when suspected in a newborn, it must be confirmed using specialized imaging and expert diagnosis, both of which are usually unavailable at tertiary birthing centers. We explore the feasibility of applying machine learning methods to reliably determine the presence of this difficult-to-diagnose cardiac abnormality from ultrasound image data. We propose a framework that uses deep learning-based machine learning methods for fully automated detection of CoA from two-dimensional ultrasound clinical data acquired in the parasternal long axis view, the apical four chamber view, and the suprasternal notch view. On a validation set consisting of 26 CoA and 64 normal patients our algorithm achieved a total error rate of 12.9% (11.5% false-negative error and 13.6% false-positive error) when combining decisions of classifiers over three standard echocardiographic view planes. This compares favorably with published results that combine clinical assessments with pulse oximetry to detect CoA (71% sensitivity).
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