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Exploring machine learning for audio-based respiratory condition screening: A concise review of databases, methods, and open issues
Auscultation plays an important role in the clinic, and the research community has been exploring machine learning (ML) to enable remote and automatic auscultation for respiratory condition screening via sounds. To give the big picture of what is going on in this field, in this narrative review, we...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9791302/ https://www.ncbi.nlm.nih.gov/pubmed/35974706 http://dx.doi.org/10.1177/15353702221115428 |
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author | Xia, Tong Han, Jing Mascolo, Cecilia |
author_facet | Xia, Tong Han, Jing Mascolo, Cecilia |
author_sort | Xia, Tong |
collection | PubMed |
description | Auscultation plays an important role in the clinic, and the research community has been exploring machine learning (ML) to enable remote and automatic auscultation for respiratory condition screening via sounds. To give the big picture of what is going on in this field, in this narrative review, we describe publicly available audio databases that can be used for experiments, illustrate the developed ML methods proposed to date, and flag some under-considered issues which still need attention. Compared to existing surveys on the topic, we cover the latest literature, especially those audio-based COVID-19 detection studies which have gained extensive attention in the last two years. This work can help to facilitate the application of artificial intelligence in the respiratory auscultation field. |
format | Online Article Text |
id | pubmed-9791302 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | SAGE Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-97913022022-12-27 Exploring machine learning for audio-based respiratory condition screening: A concise review of databases, methods, and open issues Xia, Tong Han, Jing Mascolo, Cecilia Exp Biol Med (Maywood) Minireview Auscultation plays an important role in the clinic, and the research community has been exploring machine learning (ML) to enable remote and automatic auscultation for respiratory condition screening via sounds. To give the big picture of what is going on in this field, in this narrative review, we describe publicly available audio databases that can be used for experiments, illustrate the developed ML methods proposed to date, and flag some under-considered issues which still need attention. Compared to existing surveys on the topic, we cover the latest literature, especially those audio-based COVID-19 detection studies which have gained extensive attention in the last two years. This work can help to facilitate the application of artificial intelligence in the respiratory auscultation field. SAGE Publications 2022-08-16 2022-11 /pmc/articles/PMC9791302/ /pubmed/35974706 http://dx.doi.org/10.1177/15353702221115428 Text en © Experimental Biology and Medicine 2022 https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
spellingShingle | Minireview Xia, Tong Han, Jing Mascolo, Cecilia Exploring machine learning for audio-based respiratory condition screening: A concise review of databases, methods, and open issues |
title | Exploring machine learning for audio-based respiratory condition screening: A concise review of databases, methods, and open issues |
title_full | Exploring machine learning for audio-based respiratory condition screening: A concise review of databases, methods, and open issues |
title_fullStr | Exploring machine learning for audio-based respiratory condition screening: A concise review of databases, methods, and open issues |
title_full_unstemmed | Exploring machine learning for audio-based respiratory condition screening: A concise review of databases, methods, and open issues |
title_short | Exploring machine learning for audio-based respiratory condition screening: A concise review of databases, methods, and open issues |
title_sort | exploring machine learning for audio-based respiratory condition screening: a concise review of databases, methods, and open issues |
topic | Minireview |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9791302/ https://www.ncbi.nlm.nih.gov/pubmed/35974706 http://dx.doi.org/10.1177/15353702221115428 |
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