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The role of machine learning applications in diagnosing and assessing critical and non-critical CHD: a scoping review

Part of: Advanced Imaging

Published online by Cambridge University Press:  02 November 2021

Stephanie M. Helman Open the ORCID record for Stephanie M. Helman [Opens in a new window] ,
Elizabeth A. Herrup ,
Adam B. Christopher  and
Salah S. Al-Zaiti
Stephanie M. Helman*
Affiliation:
Department of Acute and Tertiary Care Nursing, University of Pittsburgh, Pittsburgh, PA, USA
Elizabeth A. Herrup
Affiliation:
Division of Pediatric Critical Care Medicine, UPMC Children’s Hospital of Pittsburgh, Pittsburgh, PA, USA
Adam B. Christopher
Affiliation:
Division of Pediatric Cardiology, UPMC Children’s Hospital of Pittsburgh, Pittsburgh, PA, USA
Salah S. Al-Zaiti
Affiliation:
Department of Acute and Tertiary Care Nursing, University of Pittsburgh, Pittsburgh, PA, USA Department of Emergency Medicine, University of Pittsburgh, Pittsburgh, PA, USA Division of Cardiology, University of Pittsburgh, Pittsburgh, PA, USA
*
Author for correspondence: S. Helman, PhD(c), RN, CCRN-K, CCNS, Department of Acute and Tertiary Care Nursing, University of Pittsburgh, 3500 Victoria Street, Pittsburgh, PA 15213, USA. Tel: +1 215-760-9725. E-mail: [email protected]
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Abstract

Machine learning uses historical data to make predictions about new data. It has been frequently applied in healthcare to optimise diagnostic classification through discovery of hidden patterns in data that may not be obvious to clinicians. Congenital Heart Defect (CHD) machine learning research entails one of the most promising clinical applications, in which timely and accurate diagnosis is essential. The objective of this scoping review is to summarise the application and clinical utility of machine learning techniques used in paediatric cardiology research, specifically focusing on approaches aiming to optimise diagnosis and assessment of underlying CHD. Out of 50 full-text articles identified between 2015 and 2021, 40% focused on optimising the diagnosis and assessment of CHD. Deep learning and support vector machine were the most commonly used algorithms, accounting for an overall diagnostic accuracy > 0.80. Clinical applications primarily focused on the classification of auscultatory heart sounds, transthoracic echocardiograms, and cardiac MRIs. The range of these applications and directions of future research are discussed in this scoping review.

Keywords

CHDMachine learning
Type
Review
Information
Cardiology in the Young , Volume 31 , Issue 11 , November 2021 , pp. 1770 - 1780
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Copyright
© The Author(s), 2021. Published by Cambridge University Press

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