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

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 intellige...

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Vydáno v:PeerJ. Computer science Ročník 8; s. e958
Hlavní autoři: Santosh, KC, Rasmussen, Nicholas, Mamun, Muntasir, Aryal, Sunil
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States PeerJ. Ltd 25.04.2022
PeerJ, Inc
PeerJ Inc
Témata:
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
ISSN:2376-5992, 2376-5992
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Shrnutí: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also included articles outside of PubMed and Web of Science, but pre-print articles were strictly avoided as they are not peer-reviewed.
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ISSN:2376-5992
2376-5992
DOI:10.7717/peerj-cs.958