{"id":12412,"date":"2021-02-09T09:28:23","date_gmt":"2021-02-09T15:28:23","guid":{"rendered":"https:\/\/saluddigital.com\/?p=12412"},"modified":"2025-10-21T13:04:17","modified_gmt":"2025-10-21T19:04:17","slug":"en-el-brigham-and-womens-hospital-logran-predecir-riesgo-cardiovascular-a-traves-de-aprendizaje-profundo","status":"publish","type":"post","link":"https:\/\/saluddigital.com\/en\/plataformas-digitales\/en-el-brigham-and-womens-hospital-logran-predecir-riesgo-cardiovascular-a-traves-de-aprendizaje-profundo\/","title":{"rendered":"Brigham and Women's Hospital Predicts Cardiovascular Risk Through Deep Learning"},"content":{"rendered":"
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Researchers at Harvard University's Brigham and Women's Hospital have developed a deep learning system capable of automatically measuring coronary artery calcium from CT scans.<\/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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Some indicators or predictors of cardiovascular problems such as heart attacks are coronary artery calcification or the accumulation of calcified plaque in the walls of the heart arteries. These risk factors can be detected through computed tomography (CT); however, measuring and quantifying the amount of plaque requires specialized equipment and personnel, in addition to being a time-consuming process.\u00a0<\/p>

Faced with this problem, researchers from the Artificial Intelligence in Medicine (AIM) Program at Brigham and Women's Hospital, Massachusetts General Hospital's Cardiovascular Imaging Research Center (CIRC), developed an algorithm based on deep learning to detect the amount of coronary calcium with greater agility.<\/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 machine learning system was validated thanks to data from more than 20,000 patients. They were able to automatically measure coronary artery calcium from CT scans, and thus help physicians make decisions to prevent cardiovascular problems.<\/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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Co-author of the research, Michael Lu, director of artificial intelligence at the CIRC, explained, \u201cThe coronary artery calcium score can help patients and physicians make informed, personalized decisions about whether to take a statin. From a clinical perspective, our long-term goal is to implement this deep learning system in electronic health records, to automatically identify the patients at high risk.\u201d<\/p>

The results of the research and the process of developing the deep learning system were published in the journal Nature<\/em> under the title:\"Deep convolutional neural networks to predict cardiovascular risk from computed tomography\u201d. <\/strong>It can be consulted at the following link: https:\/\/www.nature.com\/articles\/s41467-021-20966-2<\/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

NATURE <\/strong><\/p>

https:\/\/www.nature.com\/articles\/s41467-021-20966-2<\/a><\/p>

\u00a0<\/strong><\/p>

AI MEAS<\/strong><\/p>

https:\/\/ai-med.io\/more-news\/new-ai-algorithm-uses-routine-imaging-to-predict-cardiovascular-risk\/<\/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":"

Investigadores del Brigham and Women\u2019s Hospital, de la Universidad de Harvard desarrollaron un sistema de aprendizaje profundo capaz de medir autom\u00e1ticamente el calcio de las arterias coronarias a partir de tomograf\u00edas computarizadas. Algunos indicadores o predictores de problemas cardiovasculares como ataques cardiacos son la calcificaci\u00f3n de la arteria coronaria o la acumulaci\u00f3n de placa calcificada en las paredes de las arterias del coraz\u00f3n. Estos factores de riesgo pueden detectarse a trav\u00e9s de tomograf\u00eda computarizada (TC), sin embargo, medir y cuantificar las cantidades de placa requieren equipo y personal especializado, adem\u00e1s de ser un proceso que lleva tiempo.\u00a0 Ante esta problem\u00e1tica, Investigadores del Programa de Inteligencia Artificial en Medicina (AIM, por sus siglas en ingl\u00e9s), del Brigham and Women’s Hospital del Centro de Investigaci\u00f3n de Im\u00e1genes Cardiovasculares (CIRC, por sus siglas en ingl\u00e9s), perteneciente al Hospital General de Massachusetts, desarrollaron un algoritmo basado en aprendizaje profundo, para detectar la cantidad de calcio coronario con mayor agilidad. El sistema de aprendizaje autom\u00e1tico fue validado gracias a datos de m\u00e1s de 20 mil pacientes. Logrando medir de forma autom\u00e1tica el calcio de las arterias coronarias desde las TC, y as\u00ed ayudar a los m\u00e9dicos a tomar decisiones para prevenir problemas cardiovasculares. El coautor de la investigaci\u00f3n, Michael Lu, director de inteligencia artificial del CIRC, explic\u00f3 lo siguiente:\u00a0\u201cLa puntuaci\u00f3n de calcio de las arterias coronarias puede ayudar a los pacientes y m\u00e9dicos a tomar decisiones informadas y personalizadas sobre si deben tomar una estatina.\u00a0Desde una perspectiva cl\u00ednica, nuestro objetivo a largo plazo es implementar este sistema de aprendizaje profundo en registros m\u00e9dicos electr\u00f3nicos, para identificar autom\u00e1ticamente a los pacientes con alto riesgo\u201d. Los resultados de la investigaci\u00f3n y el proceso del desarrollo del sistema de aprendizaje profundo fueron publicados en la revista Nature bajo el t\u00edtulo: \u201cRedes neuronales convolucionales profundas para predecir el riesgo cardiovascular de la tomograf\u00eda computarizada\u201d. El cual es posible consultar en el siguiente enlace: https:\/\/www.nature.com\/articles\/s41467-021-20966-2 BIBLIOGRAF\u00cdA NATURE https:\/\/www.nature.com\/articles\/s41467-021-20966-2 \u00a0 AI MED https:\/\/ai-med.io\/more-news\/new-ai-algorithm-uses-routine-imaging-to-predict-cardiovascular-risk\/<\/p>","protected":false},"author":1,"featured_media":12413,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[154,153],"tags":[145],"class_list":["post-12412","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-comunidades-conectadas","category-plataformas-digitales","tag-noticias"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/12412","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=12412"}],"version-history":[{"count":0,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/12412\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media\/12413"}],"wp:attachment":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media?parent=12412"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/categories?post=12412"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/tags?post=12412"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}