Key result
Machine learning algorithms, primarily deep learning and support vector machines, achieved an overall diagnostic accuracy > 0.80 for the diagnosis and assessment of congenital heart defects.
Why the study?
Machine learning research holds promise for congenital heart defect care where timely and accurate diagnosis is essential, motivating a summary of its clinical utility in paediatric cardiology.
Do machine learning applications optimize the diagnosis and assessment of congenital heart defects in pediatric cardiology?
Do machine learning applications optimize the diagnosis and assessment of congenital heart defects in pediatric cardiology?
Machine learning algorithms, particularly deep learning and support vector machines, show high diagnostic accuracy (>0.80) and promise for optimizing the diagnosis and assessment of congenital heart defects using clinical data like heart sounds and imaging.
Answers are grounded in this paper and cite their sources.
Hypothesis-generating for machine learning in congenital heart defect diagnosis; prospective validation required before clinical use.
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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Helman et al. (2021) conducted a review in Congenital Heart Defect (CHD) (n=50). Machine learning applications was evaluated on Diagnostic accuracy. Machine learning algorithms, primarily deep learning and support vector machines, achieved an overall diagnostic accuracy > 0.80 for the diagnosis and assessment of congenital heart defects.
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