Abstract 12262: The Role of Machine Learning-Based Decision Support Tools for Diagnosing and Assessing Congenital Heart Disease

Abstract

Introduction: Machine learning (ML) is frequently applied in healthcare to optimize diagnostic classification through discovery of hidden patterns in data, not obvious to the human brain. ML research in CHD entails one of the most promising clinical applications, where timely and accurate diagnosis is essential. The objective of this scoping review was to summarize the accuracy and role of ML techniques used in pediatric cardiology research, specifically focusing on approaches that optimize diagnosis and assessment of underlying CHD. Methods: All original, peer-reviewed studies published in PubMed between January 2015 and February 2021 describing “(machine learning) AND ((congenital heart disease) OR (cardiovascular disease in children))” for predicting diagnostic outcomes in children with critical and non-critical CHD were included. An independent reviewer extracted and summarized primary data elements using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guidelines. Results: This search yielded 217 journal articles of which 48 full-text articles (22%) met inclusion criteria. The most common application included optimizing the diagnosis and assessment of critical and non-critical CHD (38%). Deep learning (48%) and support vector machine (19%) were the most common ML algorithms used, with more interpretable algorithms being less common including decision trees (5%). Studies focusing on optimizing diagnostic classification reported a pooled overall diagnostic accuracy ranging from of 0.864 to 0.955, but only two of these models were externally validated. Clinical applications primarily focused on the classification of auscultatory heart sounds, transthoracic echocardiograms, and cardiac magnetic resonance images. Conclusions: These findings indicate that ML is a very promising tool for diagnosing and assessing critical and non-critical CHD, yet extensive research is still needed to build interpretable, robust, and generalizable models for clinical use, especially considering the extreme heterogeneity of complex CHD.