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. 2024 Feb 10;24(4):1173.
doi: 10.3390/s24041173.

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

Panagiotis Kapetanidis  1 , Fotios Kalioras  1 , Constantinos Tsakonas  1 , Pantelis Tzamalis  1 , George Kontogiannis  1 , Theodora Karamanidou  2 , Thanos G Stavropoulos  2 , Sotiris Nikoletseas  1
Affiliations

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

Panagiotis Kapetanidis et al. Sensors (Basel). .

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 respiratory sounds to detect respiratory symptoms like wheezes and crackles, as well as the analysis of the voice/speech for the evaluation of human voice abnormalities. To provide a more in-depth analysis, this review examines 75 relevant audio analysis studies across three distinct areas of concern based on respiratory diseases' symptoms: (a) cough detection, (b) lower respiratory symptoms identification, and (c) diagnostics from the voice and speech. Furthermore, publicly available datasets commonly utilized in this domain are presented. It is observed that research trends are influenced by the pandemic, with a surge in studies on COVID-19 diagnosis, mobile data acquisition, and remote diagnosis systems.

Keywords: audio analysis; audio-based biomarkers; digital biomarkers; machine learning; respiratory disease; respiratory symptoms; signal processing; systematic review.

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Conflict of interest statement

This research was conducted as a collaboration between the University of Patras in Greece and Pfizer. Pfizer is the research sponsor.

Figures

Figure 1
Figure 1
Review process flowchart.
Figure 2
Figure 2
Domain distribution of the included studies.
Figure 3
Figure 3
Number of neurodegenerative studies vs. number of respiratory studies.
Figure 4
Figure 4
Distribution of cough-related studies across publication years.
Figure 5
Figure 5
Distribution of respiratory sounds related studies across publication years.
Figure 6
Figure 6
Distribution of voice/speech analysis related studies across publication years.
Figure 7
Figure 7
Distribution of cough-related studies with respect to the device used for data acquisition.
Figure 8
Figure 8
Distribution of studies with respect to their research topic in the cough area.
Figure 9
Figure 9
Distribution of respiratory sounds related studies with respect to the device used for data acquisition.
Figure 10
Figure 10
Distribution of studies with respect to their research topic in the area of respiratory sounds.
Figure 11
Figure 11
Distribution of studies with respect to the device used for data acquisition.
Figure 12
Figure 12
Distribution of studies with respect to their research topic in the area of voice analysis for disease identification.
See this image and copyright information in PMC

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