{"id":15123,"date":"2021-05-13T10:03:33","date_gmt":"2021-05-13T15:03:33","guid":{"rendered":"https:\/\/saluddigital.com\/?p=15123"},"modified":"2025-10-21T12:45:11","modified_gmt":"2025-10-21T18:45:11","slug":"pronostico-de-pacientes-con-covid-19-a-traves-de-inteligencia-artificial-y-radiografias-de-torax","status":"publish","type":"post","link":"https:\/\/saluddigital.com\/en\/big-data\/pronostico-de-pacientes-con-covid-19-a-traves-de-inteligencia-artificial-y-radiografias-de-torax\/","title":{"rendered":"Prognostication of patients with COVID-19 through Artificial Intelligence and chest radiographs."},"content":{"rendered":"
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t

The Lancet published in its most recent issue the article \u201cPrognostication of patients with COVID-19 using artificial intelligence based on chest x-rays and clinical data: a retrospective study.\u201d<\/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
\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

Chest X-rays are an important modality for medical imaging, and it is also cheaper and more accessible than other options. \u201cWe aimed to develop and evaluate an artificial intelligence system using chest x-rays and clinical data to predict disease severity and progression in patients with COVID-19,\u201d the authors explain.<\/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

\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"\"\t\t\t\t\t\t\t\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
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

This study published in The Lancet presents a retrospective study at several hospitals in the University of Pennsylvania Health System in Philadelphia, Pennsylvania and explain at hospitals affiliated with Brown University in Providence, Rhode Island. Participating patients were confirmed to be COVID-19 positive through PCR testing and chest x-ray.<\/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

\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

The study involved 1835 patients who presented between March 9 and July 20, 2020. Three groups were formed: 1285 for model training, 183 for validation and 366 for testing.<\/p>

\u201cUsing the chest x-rays as input to an EfficientNet deep neural network and clinical data, models were trained to predict the binary outcome of disease severity (ie, critical or non-critical),\u201d the study explains. Deep learning features extracted from the models and patients' clinical data were used to build timing models to predict the risk of disease progression.<\/p>

The full study and its results can be found at the following link: https:\/\/www.thelancet.com\/journals\/landig\/article\/PIIS2589-7500(21)00039-X\/fulltext#%20<\/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

\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/i><\/span>\n\t\t\t\t\t\t\t\t<\/i><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t BIBLIOGRAPHY<\/a>\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t

THE LANCET<\/strong><\/p>

https:\/\/www.thelancet.com\/journals\/landig\/article\/PIIS2589-7500(21)00039-X\/fulltext<\/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":"

The Lancet public\u00f3 en su m\u00e1s reciente n\u00famero el art\u00edculo \u201cPron\u00f3stico de pacientes con COVID-19 mediante inteligencia artificial basada en radiograf\u00edas de t\u00f3rax y datos cl\u00ednicos: un estudio retrospectivo\u201d. Las radiograf\u00edas de t\u00f3rax son una modalidad importante para la obtenci\u00f3n de im\u00e1genes m\u00e9dicas, adem\u00e1s resulta m\u00e1s econ\u00f3mica y accesible que otras opciones. \u201cNuestro objetivo fue desarrollar y evaluar un sistema de inteligencia artificial utilizando radiograf\u00edas de t\u00f3rax y datos cl\u00ednicos para predecir la gravedad y la progresi\u00f3n de la enfermedad en pacientes con COVID-19\u201d, explican los autores. Este estudio publicado en The Lancet presenta un estudio retrospectivo en varios hospitales del Sistema de Salud de la Universidad de Pensilvania en Filadelfia, Pensilvania y explican en hospitales afiliados a la Universidad de Brown en Providence, Rhode Island. Los pacientes participantes confirmaron ser positivos a COVID-19 a trav\u00e9s de una prueba PCR y una radiograf\u00eda de t\u00f3rax. El estudio cont\u00f3 con la participaci\u00f3n de 1835 pacientes que se presentaron entre el 9 de marzo y 20 de julio de 2020. Se formaron tres grupos: 1285 para el entrenamiento del modelo, 183 para validaci\u00f3n y 366 para pruebas. \u201cUtilizando las radiograf\u00edas de t\u00f3rax como entrada para una red neuronal profunda EfficientNet y datos cl\u00ednicos, se entrenaron modelos para predecir el resultado binario de la gravedad de la enfermedad (es decir, cr\u00edtica o no cr\u00edtica)\u201d, explica el estudio. Las caracter\u00edsticas del aprendizaje profundo extra\u00eddas de los modelos y los datos cl\u00ednicos de los pacientes, fueron utilizadas para construir modelos de tiempo, para predecir el riesgo de la progresi\u00f3n de la enfermedad. Consulta el estudio completo y sus resultados en el siguiente enlace: https:\/\/www.thelancet.com\/journals\/landig\/article\/PIIS2589-7500(21)00039-X\/fulltext#%20 BIBLIOGRAF\u00cdA THE LANCET https:\/\/www.thelancet.com\/journals\/landig\/article\/PIIS2589-7500(21)00039-X\/fulltext<\/p>","protected":false},"author":1,"featured_media":15124,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3399,156,3393,1418],"tags":[145],"class_list":["post-15123","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-analitica","category-big-data","category-inteligencia-artificial-y-ciencia","category-resena-de-publicaciones-cientificas","tag-noticias"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/15123","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=15123"}],"version-history":[{"count":0,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/15123\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media\/15124"}],"wp:attachment":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media?parent=15123"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/categories?post=15123"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/tags?post=15123"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}