Congenital Heart Disease Classification Using Phonocardiograms: A Scalable Screening Tool for Diverse Environments
Journal:
arXiv
Published Date:
Mar 28, 2025
Abstract
Congenital heart disease (CHD) is a critical condition that demands early
detection, particularly in infancy and childhood. This study presents a deep
learning model designed to detect CHD using phonocardiogram (PCG) signals, with
a focus on its application in global health. We evaluated our model on several
datasets, including the primary dataset from Bangladesh, achieving a high
accuracy of 94.1%, sensitivity of 92.7%, specificity of 96.3%. The model also
demonstrated robust performance on the public PhysioNet Challenge 2022 and 2016
datasets, underscoring its generalizability to diverse populations and data
sources. We assessed the performance of the algorithm for single and multiple
auscultation sites on the chest, demonstrating that the model maintains over
85% accuracy even when using a single location. Furthermore, our algorithm was
able to achieve an accuracy of 80% on low-quality recordings, which
cardiologists deemed non-diagnostic. This research suggests that an AI- driven
digital stethoscope could serve as a cost-effective screening tool for CHD in
resource-limited settings, enhancing clinical decision support and ultimately
improving patient outcomes.
