{"id":22402,"date":"2021-10-29T09:16:31","date_gmt":"2021-10-29T14:16:31","guid":{"rendered":"https:\/\/saluddigital.com\/?p=22402"},"modified":"2025-10-21T11:46:56","modified_gmt":"2025-10-21T17:46:56","slug":"nuevo-algoritmo-de-inteligencia-artificial-seria-capaz-de-agilizar-los-diagnosticos-de-insuficiencia-cardiaca","status":"publish","type":"post","link":"https:\/\/saluddigital.com\/en\/big-data\/nuevo-algoritmo-de-inteligencia-artificial-seria-capaz-de-agilizar-los-diagnosticos-de-insuficiencia-cardiaca\/","title":{"rendered":"New Artificial Intelligence algorithm would be capable of speeding up diagnoses of heart failure"},"content":{"rendered":"
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Mount Sinai researchers have developed an Artificial Intelligence (AI) algorithm capable of learning how to identify certain changes in electrocardiograms (ECG), and predict heart failure.<\/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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A published study in Journal of the American College of Cardiology: Cardiovascular Imaging<\/em>, showed the results of this AI algorithm, which would be able to learn and identify ECG changes to alert doctors about patients with possible heart failure.<\/p>

Lead author of this study, Dr. Benjamin S. Glicksberg, assistant professor of genetics and genomic sciences, member of the Hasso Plattner Institute for Digital Health at Mount Sinai, explained the following about this breakthrough: \u201cWe show that deep learning algorithms can recognize blood pumping problems on both sides of the heart from ECG waveform data. Diagnosing these types of heart conditions typically requires expensive and time-consuming procedures. We hope that this algorithm will enable faster diagnosis of heart failure."<\/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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To conduct the study, the researchers programmed a computer so that it could read ECGs from patients, and other data extracted from written reports containing Echocardiogram (ECHO) results from the same patients. In this way the written reports were taken as a standard set of data for the computer to compare with the ECG data. In this way it was possible to detect weaker hearts that could suffer from heart failure.<\/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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Heart failure is a condition that causes the heart to pump less blood than the body requires. Diagnoses are usually made through ECG and ECO to find out if the patient suffers from this condition. However, the main problem with this type of evaluation or diagnosis is that it requires specialized equipment and medical professionals that are not found in any hospital. Hence the importance of this advance through AI.<\/p>

For the study, the computer analyzed more than 700,000 ECG and ECHO reports and reports from 150,000 patients in the Mount Sinai Health System between 2003 and 2020. The data was obtained from four hospitals as part of the algorithm's learning process. and to test it we used data from a fifth hospital.<\/p>

The results showed 94% accuracy in which patients had a healthy ejection fraction and 87% accuracy in predicting those who had an ejection fraction below 40% (normal ejection fraction is 50% or more and weak hearts equal to or minus 40%). Thus, results indicated that the algorithm was effective in predicting heart failure.<\/p>

"Our results suggest that this algorithm could be a useful tool to help clinicians combat heart failure in a variety of patients," added Dr. Glicksberg.<\/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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AI MEAS<\/strong><\/p>

https:\/\/ai-med.io\/more-news\/ai-algorithm-may-enable-quicker-diagnosis-of-heart-failure\/<\/a> \u00a0<\/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 de Mount Sinai, han desarrollado un algoritmo de Inteligencia Artificial (IA), capaz de aprender como identificar ciertos cambios en electrocardiogramas (ECG), y predecir insuficiencia card\u00edaca. Un estudio publicado en Journal of the American College of Cardiology: Cardiovascular Imaging, mostr\u00f3 los resultados de este algoritmo de IA, que ser\u00eda capaz de aprender e identificar cambios en ECG para alertar a los m\u00e9dicos sobre pacientes con posibilidad de insuficiencia cardiaca. El autor principal de este estudio, Dr. Benjamin S. Glicksberg, profesor asistente de gen\u00e9tica y ciencias gen\u00f3micas, miembro del Hasso Plattner Institute for Digital Health de Mount Sinai, explic\u00f3 lo siguiente sobre este avance: \u201cDemostramos que los algoritmos de aprendizaje profundo pueden reconocer problemas de bombeo de sangre en ambos lados del coraz\u00f3n a partir de los datos de la forma de onda del ECG. Por lo general, el diagn\u00f3stico de estos tipos de afecciones card\u00edacas requiere procedimientos costosos y que requieren mucho tiempo. Esperamos que este algoritmo permita un diagn\u00f3stico m\u00e1s r\u00e1pido de insuficiencia card\u00edaca\u201d. Para realizar el estudio los investigadores programaron una computadora para que pudiera leer ECG de pacientes, y otros datos extra\u00eddos de informes escritos que conten\u00edan resultados de Ecocardiogramas (ECO) de los mismos pacientes. De esta forma los informes escritos fueron tomados como un conjunto est\u00e1ndar de datos para que la computadora hiciera la comparaci\u00f3n con los datos del ECG. De esta forma fue posible detectar corazones m\u00e1s d\u00e9biles que pudieran sufrir de insuficiencia cardiaca. La insuficiencia cardiaca es una condici\u00f3n que produce que el coraz\u00f3n bombee menos sangre de la que el cuerpo requiere. Generalmente se realizan diagn\u00f3sticos a trav\u00e9s de ECG y ECO para conocer si el paciente sufre de esta condici\u00f3n. Sin embargo, el principal problema con este tipo de evaluaciones o diagn\u00f3sticos es que se requiere equipo especializado y profesionales m\u00e9dicos que no se encuentran en cualquier hospital. Por ello la importancia de este avance a trav\u00e9s de IA. Para el estudio la computadora analiz\u00f3 m\u00e1s de 700 mil reportes de ECG y ECO e informes de 150 mil pacientes del sistema de salud de Mount Sinai entre 2003 y 2020. Los datos se obtuvieron de cuatro hospitales, esto como parte del proceso de aprendizaje del algoritmo y para probarlo se utilizaron datos de un quinto hospital. Los resultados mostraron una precisi\u00f3n del 94% sobre qu\u00e9 pacientes ten\u00edan una fracci\u00f3n de eyecci\u00f3n saludable y 87% de precisi\u00f3n para predecir aquellos que ten\u00edan una fracci\u00f3n de eyecci\u00f3n por debajo del 40% (la fracci\u00f3n de eyecci\u00f3n normal es del 50% o m\u00e1s y los corazones d\u00e9biles igual o menos a 40%). De esta forma resultados indicaron que el algoritmo fue eficaz para la predicci\u00f3n de insuficiencia cardiaca. \u201cNuestros resultados sugieren que este algoritmo podr\u00eda ser una herramienta \u00fatil para ayudar a los m\u00e9dicos a combatir la insuficiencia card\u00edaca que sufren una variedad de pacientes\u201d, agreg\u00f3 el Dr. Glicksberg. BIBLIOGRAF\u00cdA AI MED https:\/\/ai-med.io\/more-news\/ai-algorithm-may-enable-quicker-diagnosis-of-heart-failure\/  <\/p>","protected":false},"author":1,"featured_media":22410,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3399,156,3393,160,1418],"tags":[145],"class_list":["post-22402","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\/22402","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=22402"}],"version-history":[{"count":0,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/22402\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media\/22410"}],"wp:attachment":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media?parent=22402"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/categories?post=22402"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/tags?post=22402"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}