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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...

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Detalles Bibliográficos
Autores principales: Xia, Tong, Han, Jing, Mascolo, Cecilia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2022
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.
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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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