Identifying pediatric heart murmurs and distinguishing innocent from pathologic using deep learning.

Area of Science:

• Artificial Intelligence
• Cardiology
• Medical Imaging

Background:

• Accurate classification of pediatric heart sounds is crucial for timely diagnosis and treatment.
• Current methods for heart sound analysis often lack the precision needed for complex pediatric cases.
• Deep learning offers potential for enhanced accuracy in analyzing subtle variations in heart sounds.

Purpose of the Study:

• To develop and evaluate deep learning algorithms for multi-class classification of pediatric heart sounds.
• To differentiate between normal heart sounds, innocent murmurs, and pathologic murmurs using advanced AI techniques.
• To compare the performance of novel Vision Transformer models against traditional Convolutional Neural Network (CNN) approaches.

Main Methods:

• Utilized a dataset of pediatric heart sounds, including normal, innocent, and pathologic murmurs, augmented with public data.
• Developed two novel approaches using a Vision Transformer trained on Gramian Angular Field (GAF) and Markov Transition Field (MTF) image representations.
• Benchmarked Vision Transformer models against a ResNet-50 CNN trained on spectrogram images.

Main Results:

• The Vision Transformer models consistently outperformed the ResNet-50 CNN across all tested image representations.
• The Gramian Angular Field (GAF) representation demonstrated superior performance for pediatric heart sound classification.
• The best model achieved high Area Under the Curve (AUC) values: 0.92 for normal sounds, 0.83 for innocent murmurs, and 0.88 for pathologic murmurs.

Conclusions:

• Novel deep learning methods, particularly Vision Transformers with GAF, significantly improve pediatric heart sound classification accuracy.
• This study presents the first demonstration of multi-class classification for pediatric murmurs using deep learning.
• The developed models offer a more explainable and interpretable approach, potentially increasing clinician trust and adoption in clinical practice.