Santosh K C, Rasmussen Nicholas, Mamun Muntasir, Aryal Sunil
2AI: Applied Artificial Intelligence Lab, Computer Science, University of South Dakota, Vermiillion, South Dakota, United States.
School of Information Technology, Deakin University, Victoria, Australia.
PeerJ Comput Sci. 2022 Apr 25;8:e958. doi: 10.7717/peerj-cs.958. eCollection 2022.
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also included articles outside of PubMed and Web of Science, but pre-print articles were strictly avoided as they are not peer-reviewed.
对于新冠肺炎而言，强大、廉价且可及的筛查变得至关重要。尽管症状表现各异，但咳嗽仍是重症和非重症感染的主要症状之一。在资源有限的地区进行大规模筛查时，人工智能（AI）引导的工具已逐渐助力利用咳嗽声音检测/筛查新冠病毒感染。因此，在本文中，我们主要基于机器学习算法，通过考虑用于分析咳嗽声音以进行新冠病毒筛查的人工智能引导工具，回顾了2020年和2021年的前沿研究成果。在我们的研究中，我们使用了PubMed中央存储库和科学网，关键词为：（咳嗽或咳嗽声音或语音）且（机器学习或深度学习或人工智能）且（新冠肺炎或冠状病毒）。为了进行更好的荟萃分析，我们筛选了合适的数据集（规模和来源）、算法因素（浅层学习和深度学习模型）以及相应的性能得分。此外，为了不错过基于最新实验研究的文章，我们还纳入了PubMed和科学网之外的文章，但严格避免预印本文章，因为它们未经同行评审。
