AI Driven Acoustic Insights for Enhanced Lung Health Diagnostics

Abstract

Lung conditions such as asthma, pneumonia, and Chronic Obstructive Pulmonary Disease (COPD) tend to go undetected in early stages, resulting in late treatment and recovery. This project aims to create an AI-based digital stethoscope system for early and accurate identification of abnormal lung conditions. A high-sensitive microphone within the digital stethoscope records lung sounds, which are subsequently processed using Advanced Digital Signal Processing (ADSP) techniques to eliminate noise and enhance prominent acoustic features. Mel-Frequency Cepstral Coefficients (MFCCs) are extracted from processed audio signals to train an end-to-end machine learning model that can identify various pulmonary conditions based on unique respiratory sound patterns. The AI-advanced stethoscope offers medical expert’s real-time insights into lung health, enabling faster and more accurate diagnoses. The system successfully differentiates between normal respiratory sounds and abnormal patterns such as wheezing, crackles, and stridor, supporting early detection of pulmonary diseases. Portability and ease of use allow it to be an invaluable diagnostic asset, especially in remote or resource-poor settings where access to high-end medical equipment is limited. By combining artificial intelligence with traditional stethoscope technology, this novel approach improves lung disease identification, allowing timely intervention and enhanced patient outcomes. The synergy of AI-based analysis and real-time respiratory sound classification marks a substantial breakthrough in pulmonary diagnostics, assisting medical professionals in providing efficient and accurate assessments for effective disease management and treatment planning.