Digital Stethoscope with Companion Mobile App (AIRIS)

Abstract

Respiratory illness remains a significant cause of morbidity and mortality throughout the world, particularly in low-resource environments where direct access to diagnostic tools and healthcare professionals is not always feasible. Traditional diagnosis by auscultation and pulmonary function tests usually relies on the presence of specialized equipment and trained healthcare professionals, so early diagnosis and continuous monitoring become unattainable for most. Such absence of readily accessible respiratory care significantly prolongs diagnosis and treatment and leads to more serious health consequences. To overcome this challenge, we introduce AIRIS (Advanced Insight for Respiratory Intelligence System)—a software and hardware combined solution whose aim is to support early detection and monitoring of respiratory diseases. The system is equipped with a digital stethoscope specially tailored for the recording of high-fidelity lung sounds, driven by a Raspberry Pi Zero 2W. The audio signals are sent to a mobile app, built using Flutter, where real-time noise reduction, visualization, and analysis are performed. At its heart is a machine learning model based on Convolutional Neural Networks (CNN), trained on the ICBHI dataset for respiratory sound classification and detection of potential anomalies. The AIRIS app also supports an end-to-end healthcare process with secure login by users, doctor-patient messaging, appointment scheduling, and patient monitoring of progress—backed by Firebase. There is also an AI-driven chatbot with LangChain and OpenAI APIs to support users in navigating symptom-related questions. With smart diagnostics, remote access, and ease of use, AIRIS is an affordable and scalable solution to respiratory healthcare, particularly valuable for underserved and remote populations.