A systematic review on cough sound analysis for Covid-19 diagnosis and screening: is my cough sound COVID-19?

• Published in: PeerJ. Computer science Vol. 8; p. e958
• Main Authors: Santosh, KC, Rasmussen, Nicholas, Mamun, Muntasir, Aryal, Sunil
• Format: Journal Article
• Language: English
• Published: United States PeerJ. Ltd 25.04.2022; PeerJ, Inc; PeerJ Inc
• Subjects: Acoustics; Algorithms; Analysis; Artificial Intelligence; Bioinformatics; Coronaviruses; Cough; Cough sound; Covid-19; COVID-19 vaccines; Data mining; Data Mining and Machine Learning; Deep learning; Diagnosis; Disease; Health aspects; Human subjects; Illnesses; Infections; Machine learning; Pandemics; Pneumonia; Public healthcare; Screening; Sociodemographics; Sound; State-of-the-art reviews; Systematic review; AI; Diagnosis; Covid-19; Public healthcare; Cough sound; Machine learning
• DOI: 10.7717/peerj-cs.958
• ISSN: 2376-5992

For COVID-19, the need for robust, inexpensive, and accessible screening becomes critical. Even though symptoms present differently, cough is still taken as one of the primary symptoms in severe and non-severe infections alike. For mass screening in resource-constrained regions, artificial intelligence (AI)-guided tools have progressively contributed to detect/screen COVID-19 infections using cough sounds. Therefore, in this article, we review state-of-the-art works in both years 2020 and 2021 by considering AI-guided tools to analyze cough sound for COVID-19 screening primarily based on machine learning algorithms. In our study, we used PubMed central repository and Web of Science with key words: (Cough OR Cough Sounds OR Speech) AND (Machine learning OR Deep learning OR Artificial intelligence) AND (COVID-19 OR Coronavirus). For better meta-analysis, we screened for appropriate dataset (size and source), algorithmic factors (both shallow learning and deep learning models) and corresponding performance scores. Further, in order not to miss up-to-date experimental research-based articles, we also included articles outside of PubMed and Web of Science, but pre-print articles were strictly avoided as they are not peer-reviewed.