Respiratory auscultation is crucial for early detection of pediatric
pneumonia, a condition that can quickly worsen without timely intervention. In
areas with limited physician access, effective auscultation is challenging. We
present a smartphone-based system that leverages built-in microphones and
advanced deep learning algorithms to detect abnormal respiratory sounds
indicative of pneumonia risk. Our end-to-end deep learning framework employs
domain generalization to integrate a large electronic stethoscope dataset with
a smaller smartphone-derived dataset, enabling robust feature learning for
accurate respiratory assessments without expensive equipment. The accompanying
mobile application guides caregivers in collecting high-quality lung sound
samples and provides immediate feedback on potential pneumonia risks. User
studies show strong classification performance and high acceptance,
demonstrating the system’s ability to facilitate proactive interventions and
reduce preventable childhood pneumonia deaths. By seamlessly integrating into
ubiquitous smartphones, this approach offers a promising avenue for more
equitable and comprehensive remote pediatric care.

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