Artificial Intelligence for Detecting COVID-19 With the Aid of Human Cough, Breathing and Speech Signals: Scoping Review
<italic>Goal:</italic> Official tests for COVID-19 are time consuming, costly, can produce high false negatives, use up vital chemicals and may violate social distancing laws. Therefore, a fast and reliable additional solution using recordings of cough, breathing and speech data for prel...
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IEEE
2022-01-01
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| Series: | IEEE Open Journal of Engineering in Medicine and Biology |
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| Online Access: | https://ieeexplore.ieee.org/document/9713955/ |
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| author | Mouzzam Husain Andrew Simpkin Claire Gibbons Tanya Talkar Daniel Low Paolo Bonato Satrajit S. Ghosh Thomas Quatieri Derek T. O'Keeffe |
| author_facet | Mouzzam Husain Andrew Simpkin Claire Gibbons Tanya Talkar Daniel Low Paolo Bonato Satrajit S. Ghosh Thomas Quatieri Derek T. O'Keeffe |
| author_sort | Mouzzam Husain |
| collection | DOAJ |
| description | <italic>Goal:</italic> Official tests for COVID-19 are time consuming, costly, can produce high false negatives, use up vital chemicals and may violate social distancing laws. Therefore, a fast and reliable additional solution using recordings of cough, breathing and speech data for preliminary screening may help alleviate these issues. <italic>Objective:</italic> This scoping review explores how Artificial Intelligence (AI) technology aims to detect COVID-19 disease by using cough, breathing and speech recordings, as reported in the literature. Here, we describe and summarize attributes of the identified AI techniques and datasets used for their implementation. <italic>Methods:</italic> A scoping review was conducted following the guidelines of PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews). Electronic databases (Google Scholar, Science Direct, and IEEE Xplore) were searched between 1st April 2020 and 15th August 2021. Terms were selected based on the target intervention (i.e., AI), the target disease (i.e., COVID-19) and acoustic correlates of the disease (i.e., speech, breathing and cough). A narrative approach was used to summarize the extracted data. <italic>Results:</italic> 24 studies and 8 Apps out of the 86 retrieved studies met the inclusion criteria. Half of the publications and Apps were from the USA. The most prominent AI architecture used was a convolutional neural network, followed by a recurrent neural network. AI models were mainly trained, tested and run-on websites and personal computers, rather than on phone apps. More than half of the included studies reported area-under-the-curve performance of greater than 0.90 on symptomatic and negative datasets while one study achieved 100% sensitivity in predicting asymptomatic COVID-19 from cough-, breathing- or speech-based acoustic features. <italic>Conclusions:</italic> The included studies show that AI has the potential to help detect COVID-19 using cough, breathing and speech samples. The proposed methods (with some time and appropriate clinical testing) could prove to be an effective method in detecting various diseases related to respiratory and neurophysiological changes in the human body. |
| format | Article |
| id | doaj-art-5cb863c5e2984ff49d9462d8e5da743a |
| institution | OA Journals |
| issn | 2644-1276 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | IEEE |
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| series | IEEE Open Journal of Engineering in Medicine and Biology |
| spelling | doaj-art-5cb863c5e2984ff49d9462d8e5da743a2025-08-20T02:38:13ZengIEEEIEEE Open Journal of Engineering in Medicine and Biology2644-12762022-01-01323524110.1109/OJEMB.2022.31436889713955Artificial Intelligence for Detecting COVID-19 With the Aid of Human Cough, Breathing and Speech Signals: Scoping ReviewMouzzam Husain0https://orcid.org/0000-0001-7636-2583Andrew Simpkin1Claire Gibbons2Tanya Talkar3https://orcid.org/0000-0002-1999-6843Daniel Low4Paolo Bonato5https://orcid.org/0000-0002-1818-1714Satrajit S. Ghosh6Thomas Quatieri7https://orcid.org/0000-0003-1925-6340Derek T. O'Keeffe8https://orcid.org/0000-0001-8501-2382Health Innovation Via Engineering (HIVE) Lab, Curam, Lero, School of Medicine, Lambe Institute for Translational Research, National University of Ireland Galway, Galway, IrelandSchool of Mathematics, Statistics and Applied Mathematics, National University of Ireland, Galway, IrelandHealth Innovation Via Engineering (HIVE) Lab, Curam, Lero, School of Medicine, Lambe Institute for Translational Research, National University of Ireland Galway, Galway, IrelandMIT Lincoln Laboratory, Lexington, MA, USAProgram in Speech and Hearing Bioscience and Technology, Harvard Medical School, Boston, MA, USADepartment of Physical Medicine and Rehabilitation, Harvard Medical School, Spaulding Rehabilitation Hospital, Boston, MA, USAProgram in Speech and Hearing Bioscience and Technology, Harvard Medical School, Boston, MA, USAMIT Lincoln Laboratory, Lexington, MA, USAHealth Innovation Via Engineering (HIVE) Lab, Curam, Lero, School of Medicine, Lambe Institute for Translational Research, National University of Ireland Galway, Galway, Ireland<italic>Goal:</italic> Official tests for COVID-19 are time consuming, costly, can produce high false negatives, use up vital chemicals and may violate social distancing laws. Therefore, a fast and reliable additional solution using recordings of cough, breathing and speech data for preliminary screening may help alleviate these issues. <italic>Objective:</italic> This scoping review explores how Artificial Intelligence (AI) technology aims to detect COVID-19 disease by using cough, breathing and speech recordings, as reported in the literature. Here, we describe and summarize attributes of the identified AI techniques and datasets used for their implementation. <italic>Methods:</italic> A scoping review was conducted following the guidelines of PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews). Electronic databases (Google Scholar, Science Direct, and IEEE Xplore) were searched between 1st April 2020 and 15th August 2021. Terms were selected based on the target intervention (i.e., AI), the target disease (i.e., COVID-19) and acoustic correlates of the disease (i.e., speech, breathing and cough). A narrative approach was used to summarize the extracted data. <italic>Results:</italic> 24 studies and 8 Apps out of the 86 retrieved studies met the inclusion criteria. Half of the publications and Apps were from the USA. The most prominent AI architecture used was a convolutional neural network, followed by a recurrent neural network. AI models were mainly trained, tested and run-on websites and personal computers, rather than on phone apps. More than half of the included studies reported area-under-the-curve performance of greater than 0.90 on symptomatic and negative datasets while one study achieved 100% sensitivity in predicting asymptomatic COVID-19 from cough-, breathing- or speech-based acoustic features. <italic>Conclusions:</italic> The included studies show that AI has the potential to help detect COVID-19 using cough, breathing and speech samples. The proposed methods (with some time and appropriate clinical testing) could prove to be an effective method in detecting various diseases related to respiratory and neurophysiological changes in the human body.https://ieeexplore.ieee.org/document/9713955/COVID-19artificial intelligencemachine learningcoughspeech signalsacoustics |
| spellingShingle | Mouzzam Husain Andrew Simpkin Claire Gibbons Tanya Talkar Daniel Low Paolo Bonato Satrajit S. Ghosh Thomas Quatieri Derek T. O'Keeffe Artificial Intelligence for Detecting COVID-19 With the Aid of Human Cough, Breathing and Speech Signals: Scoping Review IEEE Open Journal of Engineering in Medicine and Biology COVID-19 artificial intelligence machine learning cough speech signals acoustics |
| title | Artificial Intelligence for Detecting COVID-19 With the Aid of Human Cough, Breathing and Speech Signals: Scoping Review |
| title_full | Artificial Intelligence for Detecting COVID-19 With the Aid of Human Cough, Breathing and Speech Signals: Scoping Review |
| title_fullStr | Artificial Intelligence for Detecting COVID-19 With the Aid of Human Cough, Breathing and Speech Signals: Scoping Review |
| title_full_unstemmed | Artificial Intelligence for Detecting COVID-19 With the Aid of Human Cough, Breathing and Speech Signals: Scoping Review |
| title_short | Artificial Intelligence for Detecting COVID-19 With the Aid of Human Cough, Breathing and Speech Signals: Scoping Review |
| title_sort | artificial intelligence for detecting covid 19 with the aid of human cough breathing and speech signals scoping review |
| topic | COVID-19 artificial intelligence machine learning cough speech signals acoustics |
| url | https://ieeexplore.ieee.org/document/9713955/ |
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