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Journal of Medical Imaging

Automated detection of coarctation of aorta in neonates from two-dimensional echocardiograms
Author(s): Franklin Pereira; Alejandra Bueno; Andrea Rodriguez; Douglas Perrin; Gerald Marx; Michael Cardinale; Ivan Salgo; Pedro del Nido
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Paper 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).

Paper Details

Date Published: 24 January 2017
PDF: 13 pages
J. Med. Imag. 4(1) 014502 doi: 10.1117/1.JMI.4.1.014502
Published in: Journal of Medical Imaging Volume 4, Issue 1
Show Author Affiliations
Franklin Pereira, Philips Ultrasound, Inc. (United States)
Alejandra Bueno, Boston Children's Hospital (United States)
Andrea Rodriguez, Boston Children's Hospital (United States)
Douglas Perrin, Boston Children’s Hospital (United States)
Gerald Marx, Boston Children's Hospital (United States)
Michael Cardinale, Philips Ultrasound, Inc. (United States)
Ivan Salgo, Philips Ultrasound, Inc. (United States)
Pedro del Nido, Boston Children's Hospital (United States)

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