{"id":13492,"date":"2021-04-13T10:01:53","date_gmt":"2021-04-13T15:01:53","guid":{"rendered":"https:\/\/saluddigital.com\/?p=13492"},"modified":"2025-10-21T12:49:51","modified_gmt":"2025-10-21T18:49:51","slug":"cientificos-crean-modelo-de-inteligencia-artificial-para-apoyar-en-el-diagnostico-de-nodulos-tiroideos","status":"publish","type":"post","link":"https:\/\/saluddigital.com\/en\/inteligencia-artificial-y-ciencia\/cientificos-crean-modelo-de-inteligencia-artificial-para-apoyar-en-el-diagnostico-de-nodulos-tiroideos\/","title":{"rendered":"Scientists create Artificial Intelligence model to support thyroid nodule diagnosis"},"content":{"rendered":"
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Artificial Intelligence (AI) models, provide a new approach to disease diagnosis and treatment. In the April issue of The Lancet, a study about the use of this technology for the management and diagnosis of thyroid nodules was published.<\/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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This AI model was presented in the article entitled: \"Deep learning-based artificial intelligence model to assist thyroid nodule diagnosis and management: a multicentre diagnostic study\". It is a deep learning-based model for differentiation between malignant tumors and benign thyroid nodules.<\/p>

ThyNet is the name of the model and the study focuses on how it \" could help radiologists improve diagnostic performance and avoid unnecessary fine needle aspiration.\" To train the deep learning algorithm the researchers use a training dataset of 14439 images from 6587 patients and the fitting dataset 3610 images from 1752 patients.<\/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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\"All nodules in the training and total test set were pathologically confirmed. The diagnostic performance of ThyNet was first compared with 12 radiologists,\" This corresponded to test set A. Subsequently, for Set B the ThyNey-assisted strategy was developed in conjunction with radiologists.<\/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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Then, \"the ThyNet assisted strategy was then tested in a real-world clinical setting (using images and videos; test set C).\" The researchers explain that \"The ThyNet-assisted strategy significantly improved the diagnostic accuracy of radiologists on thyroid nodule differentiation and could potentially decrease the number of unnecessary fine needle aspirations.\"<\/p>

Find the full study at the following link: https:\/\/www.thelancet.com\/journals\/landig\/article\/PIIS2589-7500(21)00041-8\/fulltext<\/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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\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)00041-8\/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":"

Los modelos de Inteligencia Artificial (IA), brindan una nueva forma de acercamiento al diagn\u00f3stico y tratamiento de enfermedades. En la edici\u00f3n de abril de The Lancet fue publicado un estudio sobre la Utilizaci\u00f3n de esta tecnolog\u00eda para el manejo y diagn\u00f3stico de n\u00f3dulos tiroideos. Este modelo de IA fue presentado en el art\u00edculo titulado: Modelo de inteligencia artificial basado en aprendizaje profundo para ayudar al diagn\u00f3stico y manejo de n\u00f3dulos tiroideos: un estudio de diagn\u00f3stico multic\u00e9ntrico. Se trata de un modelo basado en aprendizaje profundo para la diferenciaci\u00f3n entre tumores malignos y n\u00f3dulos tiroideos benignos. ThyNet es el nombre del modelo y el estudio se centra en c\u00f3mo “podr\u00eda ayudar a los radi\u00f3logos a mejorar el rendimiento diagn\u00f3stico y evitar la aspiraci\u00f3n con aguja fina innecesaria”. Para entrenar al algoritmo de aprendizaje profundo los investigadores utilizaci\u00f3n un conjunto de datos de entrenamiento de 14439 im\u00e1genes de 6587 pacientes y el conjunto de datos de ajuste 3610 im\u00e1genes de 1752 pacientes. “Todos los n\u00f3dulos en el entrenamiento y el conjunto de prueba total se confirmaron patol\u00f3gicamente. El rendimiento diagn\u00f3stico de ThyNet se compar\u00f3 primero con el de 12 radi\u00f3logos”, Este correspondi\u00f3 al conjunto de pruebas A. Posteriormente, para el Conjunto B se desarroll\u00f3 la estrategia asistida por ThyNey en conjunto con radi\u00f3logos. “Luego, la estrategia asistida por ThyNet se prob\u00f3 en un entorno cl\u00ednico del mundo real (utilizando im\u00e1genes y videos; conjunto de prueba C)”. Los investigadores explican que “La estrategia asistida por ThyNet puede mejorar significativamente el rendimiento diagn\u00f3stico de los radi\u00f3logos y ayudar a reducir las aspiraciones con aguja fina innecesarias para los n\u00f3dulos tiroideos”. Encuentra el estudio completo en el siguiente enlace: https:\/\/www.thelancet.com\/journals\/landig\/article\/PIIS2589-7500(21)00041-8\/fulltext BIBLIOGRAF\u00cdA THE LANCET https:\/\/www.thelancet.com\/journals\/landig\/article\/PIIS2589-7500(21)00041-8\/fulltext<\/p>","protected":false},"author":1,"featured_media":13493,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3393,3396],"tags":[145],"class_list":["post-13492","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-inteligencia-artificial-y-ciencia","category-uso-de-plataformas-digitales","tag-noticias"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/13492","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=13492"}],"version-history":[{"count":0,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/13492\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media\/13493"}],"wp:attachment":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media?parent=13492"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/categories?post=13492"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/tags?post=13492"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}