Computer-Aided Diagnosis

Automated detection of coarctation of aorta in neonates from two-dimensional echocardiograms

[+] Author Affiliations
Franklin Pereira, Michael Cardinale, Ivan Salgo

Philips Ultrasound Inc., 3000 Minuteman Road, Andover, Massachusetts 02176, United States

Alejandra Bueno, Andrea Rodriguez, Douglas Perrin, Gerald Marx, Pedro del Nido

Boston Children’s Hospital, Department of Cardiovascular Surgery, 300 Longwood Avenue, Boston, Massachusetts 02115, United States

J. Med. Imag. 4(1), 014502 (Jan 24, 2017). doi:10.1117/1.JMI.4.1.014502
History: Received August 29, 2016; Accepted December 20, 2016
Text Size: A A A

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).

Figures in this Article
© 2017 Society of Photo-Optical Instrumentation Engineers

Citation

Franklin Pereira ; Alejandra Bueno ; Andrea Rodriguez ; Douglas Perrin ; Gerald Marx, et al.
"Automated detection of coarctation of aorta in neonates from two-dimensional echocardiograms", J. Med. Imag. 4(1), 014502 (Jan 24, 2017). ; http://dx.doi.org/10.1117/1.JMI.4.1.014502


Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging & repositioning the boxes below.

Related Book Chapters

Topic Collections

PubMed Articles
Advertisement
  • Don't have an account?
  • Subscribe to the SPIE Digital Library
  • Create a FREE account to sign up for Digital Library content alerts and gain access to institutional subscriptions remotely.
Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).
Access This Proceeding
Sign in or Create a personal account to Buy this article ($15 for members, $18 for non-members).
Access This Chapter

Access to SPIE eBooks is limited to subscribing institutions and is not available as part of a personal subscription. Print or electronic versions of individual SPIE books may be purchased via SPIE.org.