Respiratory Diseases Diagnosis Using Audio Analysis and Artificial Intelligence: A Systematic Review.

ABSTRACT: Respiratory diseases represent a significant global burden, necessitating efficient diagnostic methods for timely intervention. Digital biomarkers based on audio, acoustics, and sound from the upper and lower respiratory system, as well as the voice, have emerged as valuable indicators of respiratory functionality. Recent advancements in machine learning (ML) algorithms offer promising avenues for the identification and diagnosis of respiratory diseases through the analysis and processing of such audio-based biomarkers. An ever-increasing number of studies employ ML techniques to extract meaningful information from audio biomarkers. Beyond disease identification, these studies explore diverse aspects such as the recognition of cough sounds amidst environmental noise, the analysis of respir

SUBMITTER: Kapetanidis P 

PROVIDER: S-EPMC10893010 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature