DOI
10.5703/1288284318527
Description
Respiratory monitoring in neonates remains a clinical challenge due to the limitations of existing pulmonary function assessment tools and the infant’s delicate physiology. This work-in-progress presents an artificial intelligence (AI)-driven framework for intraoral respiratory analysis using a smart pacifier platform that integrates acoustic sensing, embedded electronics, and deep learning. The proposed AI model is based upon a deep convolutional neural network that processes intraoral breathing sound reflections passively, or in presence of active sound stimuli. By learning complex temporal-spectral features from the breathing sound signals, the AI model will estimate key cardiorespiratory measures (e.g., respiratory rate, lung capacity) and the pulmonary condition (e.g., existence of underlying obstructive respiratory condition) in real time. Preliminary results highlight the correlation between breathing sound patterns and respiratory mechanics, demonstrating the potential feasibility of continuous, noninvasive pulmonary monitoring in neonatal settings. This AI-enabled intraoral sound sensing approach establishes a foundation for intelligent neonatal respiratory assessment, enhancing early detection and personalized management of pulmonary disorders.
AI-Powered Pacifier with Intraoral Sound Analysis for Neonatal Pulmonary Monitoring
Respiratory monitoring in neonates remains a clinical challenge due to the limitations of existing pulmonary function assessment tools and the infant’s delicate physiology. This work-in-progress presents an artificial intelligence (AI)-driven framework for intraoral respiratory analysis using a smart pacifier platform that integrates acoustic sensing, embedded electronics, and deep learning. The proposed AI model is based upon a deep convolutional neural network that processes intraoral breathing sound reflections passively, or in presence of active sound stimuli. By learning complex temporal-spectral features from the breathing sound signals, the AI model will estimate key cardiorespiratory measures (e.g., respiratory rate, lung capacity) and the pulmonary condition (e.g., existence of underlying obstructive respiratory condition) in real time. Preliminary results highlight the correlation between breathing sound patterns and respiratory mechanics, demonstrating the potential feasibility of continuous, noninvasive pulmonary monitoring in neonatal settings. This AI-enabled intraoral sound sensing approach establishes a foundation for intelligent neonatal respiratory assessment, enhancing early detection and personalized management of pulmonary disorders.