AI-Integrated Digital Auscultation System for Early Detection and Monitoring of Chronic Obstructive Pulmonary Disease in Resource-Limited Settings

Auscultation remains a fundamental component of respiratory examination in clinical practice; however, its effectiveness is constrained by inter-observer variability and diagnostic subjectivity, particularly in the early detection of disease. This study presents a portable digital auscultation system, embedded within a smartphone-based platform, designed to enhance the diagnosis and monitoring of Chronic Obstructive Pulmonary Disease (COPD) through artificial intelligence (AI). The system employs a custom-built digital stethoscope equipped with an electret microphone, interfaced with an Android device, to acquire respiratory sounds. Signal processing techniques, including Mel-Frequency Cepstral Coefficients (MFCC), are applied to extract discriminative features from auscultated audio. Multiple deep learning classifiers—ANN, CNN, GRU, and LSTM—were evaluated for respiratory sound classification, with the ANN model achieving the highest diagnostic accuracy of 96.36%, along with precision, recall, and F1‑score of 96.23%, 96.10%, and 96.16%, respectively, outperforming CNN (93.45%), GRU (90.8%), and LSTM (88.25%). The diagnostic interface, developed using Streamlit, offers real-time feedback and supports remote respiratory health assessment. This AI-enhanced diagnostic tool has the potential to support biomedical practitioners in the early detection of COPD, monitoring disease progression, and assessing treatment responses, particularly in pharmacologically managed patients within low-resource healthcare environments.