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

Helman, Stephanie and Herrup, Elizabeth and Christopher, Adam and Al-Zaiti, Salah (2021) The role of machine learning applications in diagnosing and assessing critical and non-critical CHD: a scoping review. Cardiology in the Young, 31 (11). ISSN 1467-1107

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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.


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Item Type: Article
Status: Published
CreatorsEmailPitt UsernameORCID
Helman, Stephaniesmh178@pitt.edusm178
Al-Zaiti, Salahssa33@pitt.edussa33
Date: 2 November 2021
Date Type: Publication
Journal or Publication Title: Cardiology in the Young
Volume: 31
Number: 11
Publisher: Cambridge University Press
Schools and Programs: School of Nursing > Nursing
Refereed: Yes
ISSN: 1467-1107
Official URL:
Article Type: Research Article
Date Deposited: 20 Dec 2023 19:09
Last Modified: 20 Dec 2023 19:09


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