{"id":24273,"date":"2022-03-04T09:11:41","date_gmt":"2022-03-04T15:11:41","guid":{"rendered":"https:\/\/saluddigital.com\/?p=24273"},"modified":"2025-10-21T10:14:55","modified_gmt":"2025-10-21T16:14:55","slug":"estudio-realiza-muestra-el-desarrollo-de-pruebas-de-covid-19-a-traves-de-audio","status":"publish","type":"post","link":"https:\/\/saluddigital.com\/en\/big-data\/estudio-realiza-muestra-el-desarrollo-de-pruebas-de-covid-19-a-traves-de-audio\/","title":{"rendered":"Study performs shows the development of COVID-19 tests through audio"},"content":{"rendered":"
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The study "Sounds of COVID-19: Exploring the realistic performance of audio-based digital tests," was published in the scientific journal npj Digital Medicine by Nature.<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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It is possible to use audio-based approaches for efficient, affordable and at-scale detection of COVID-19. The study published in Nature recently uses audio cues such as coughing, breathing and voice, and the application of machine learning and deep learning models, to test for this disease.<\/p>

However, the authors of the study acknowledge that it is necessary to explore how biases and methodological decisions affect the practical performance of this type of tool. To do this, they managed to collect a wide set of respiratory audio data through a mobile app, this data also included information on symptoms and results of COVID-19 tests.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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The data set included 5,240 audio samples from 2,478 English-speaking participants, the importance of choosing only one language was to avoid acoustic bias, the study explains. The study also considered population subgroups such as gender, age and also showed a consistent performance on the prevalence of COVID-19.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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\u201cWe use these audio samples to build an audio-based COVID-19 prediction model. The unbiased model took features extracted from respiration, cough, and voice cues as predictors and yielded an AUC-ROC of 0.71 (95% CI %: 0.65\u20130.77)," the study explains.<\/p>

In this sense, the creation and validation of a deep learning method for the detection of COVID-19, analyzing human sounds, was possible. \u201cWe analyze the predictive performance of the presented model in the detection of COVID-19 infection, which can provide information on the adoption of digital health technologies in the COVID-19 era\u201d, the authors conclude in the discussion section. in the study.<\/p>

Check the full study at the following link: https:\/\/www.nature.com\/articles\/s41746-021-00553-x<\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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NATURE<\/strong><\/p>

https:\/\/www.nature.com\/articles\/s41746-021-00553-x<\/a><\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>","protected":false},"excerpt":{"rendered":"

El estudio “Sonidos de COVID-19: exploraci\u00f3n del rendimiento realista de las pruebas digitales basadas en audio”, fue publicado en la revista cient\u00edfica npj Digital Medicine de Nature. Es posible utilizar enfoques basados en audio para la detecci\u00f3n eficiente, asequible y adem\u00e1s a escala de COVID-19. El estudio publicado en Nature recientemente, utiliza pistas de audio como tos, respiraci\u00f3n y voz, y la aplicaci\u00f3n de modelos de aprendizaje autom\u00e1tico y aprendizaje profundo, para realizar pruebas de esta enfermedad. No obstante, los autores del estudio reconocen que es necesario explorar c\u00f3mo los sesgos y las decisiones metodol\u00f3gicas llega a afectar el desempe\u00f1o pr\u00e1ctico de este tipo de herramientas. Para ello lograron recopilar un amplio conjunto de datos de audio respiratorio a trav\u00e9s de una app m\u00f3vil, estos datos adem\u00e1s incluyeron informaci\u00f3n sobre s\u00edntomas y sobre resultados de pruebas COVID-19. El conjunto de datos incluy\u00f3 5240 muestras de audio de 2478 participantes de habla inglesa, la importancia de elegir solamente un idioma fue para evitar sesgos ac\u00fasticos, seg\u00fan explica el estudio. El estudio tambi\u00e9n contempl\u00f3 subgrupos de poblaci\u00f3n como sexo, edad y adem\u00e1s mostr\u00f3 un rendimiento consistente sobre la prevalencia de COVID-19. \u201cUsamos estas muestras de audio para construir un modelo de predicci\u00f3n de COVID-19 basado en audio. El modelo imparcial tom\u00f3 las caracter\u00edsticas extra\u00eddas de la respiraci\u00f3n, la tos y las se\u00f1ales de voz como predictores y arroj\u00f3 un AUC-ROC de 0,71 (IC del 95 %: 0,65\u20130,77)\u201d, explica el estudio. En este sentido fue posible la creaci\u00f3n y validaci\u00f3n de un m\u00e9todo de aprendizaje profundo para la detecci\u00f3n de COVID-19, analizando sonidos humanos. \u201cAnalizamos el rendimiento predictivo del modelo presentado en la detecci\u00f3n de la infecci\u00f3n por COVID-19, lo que puede aportar informaci\u00f3n sobre la adopci\u00f3n de tecnolog\u00edas de salud digital en la era de la COVID-19\u201d, concluyen los autores en el apartado de discusi\u00f3n en el estudio. Consulta el estudio completo en el siguiente enlace: https:\/\/www.nature.com\/articles\/s41746-021-00553-x BIBLIOGRAF\u00cdA NATURE https:\/\/www.nature.com\/articles\/s41746-021-00553-x<\/p>","protected":false},"author":1,"featured_media":24274,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3399,156,3393,160,1418],"tags":[145],"class_list":["post-24273","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-analitica","category-big-data","category-inteligencia-artificial-y-ciencia","category-noticias","category-resena-de-publicaciones-cientificas","tag-noticias"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/24273","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/comments?post=24273"}],"version-history":[{"count":0,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/24273\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media\/24274"}],"wp:attachment":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media?parent=24273"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/categories?post=24273"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/tags?post=24273"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}