<ul><li>Congenital heart disease (CHD) is a critical condition that demands early detection, particularly in infancy and childhood.</li><li>A deep learning model designed to detect CHD using phonocardiogram (PCG) signals achieved high accuracy of 94.1%, sensitivity of 92.7%, specificity of 96.3%.</li><li>The model demonstrated robust performance on diverse datasets from Bangladesh, as well as public datasets, showing its generalizability to different populations and data sources.</li><li>The research suggests that an AI-driven digital stethoscope could be a cost-effective screening tool for CHD in resource-limited settings, improving patient outcomes.</li></ul>

Congenital Heart Disease Classification Using Phonocardiograms: A Scalable Screening Tool for Diverse Environments

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