{"id":24714,"date":"2022-04-08T09:57:31","date_gmt":"2022-04-08T14:57:31","guid":{"rendered":"https:\/\/saluddigital.com\/?p=24714"},"modified":"2025-10-21T09:54:26","modified_gmt":"2025-10-21T15:54:26","slug":"investigadores-de-la-universidad-de-yale-desarrollaron-modelo-de-ia-para-diagnostico-de-enfermedades-del-corazon","status":"publish","type":"post","link":"https:\/\/saluddigital.com\/en\/noticias\/investigadores-de-la-universidad-de-yale-desarrollaron-modelo-de-ia-para-diagnostico-de-enfermedades-del-corazon\/","title":{"rendered":"Yale University researchers developed AI model for diagnosing heart disease"},"content":{"rendered":"
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The Artificial Intelligence (AI) model uses electrocardiogram images to diagnose different heart diseases.<\/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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Researchers at the Yale Cardiovascular Data Science (CarDS) Lab at Yale School of Medicine have developed an AI-based model that helps improve the diagnosis of cardiac arrhythmia through the analysis of electrocardiogram (ECG) images.<\/p>

The team led by specialist Rohan Khera, assistant professor of cardiovascular medicine, explained that most AI-based tools are designed for individual clinical disorders, so they have limited utility.<\/p>

In this case, the solution designed by CarDS Lab aims to improve ECG interpretation even remotely. The results of the research were published in Nature:\u00a0 https:\/\/www.nature.com\/articles\/s41467-022-29153-3<\/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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The research details that it is possible to improve care and diagnosis based on ECG in remote environments, for this reason they developed a multi-brand automated diagnosis model for electrocardiographic images, more suitable for a broader use\u201d.<\/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 study shows that, through 2,228,236 12-lead ECG signals in 811 municipalities in Brazil, they are transformed into ECG images in different lead conformations to train a convolutional neural network (CNN).<\/p>

The data correspond to patients in Brazil who were treated between 2010 and 2017, of which one in six patients was diagnosed with heart rhythm disturbances.<\/p>

\u201cCurrent AI tools rely on raw EKG signals rather than stored images, which are much more common since ECGs are often printed and scanned as images. Furthermore, many AI-based diagnostic tools are designed for individual clinical disorders and thus may have limited utility in a clinical setting where multiple ECG abnormalities coexist,\u201d Khera explained.<\/p>

Another point that Khera highlighted was that it is an intelligent model that does not depend on specific ECG designs, since it has the ability to adapt to new designs. This way you can enhance and support the work of expert human readers.<\/p>

In this way, the model has the potential to expand the application of AI in clinical care, specifically in ECG-based techniques, according to Veer Sangha, lead author of the study.<\/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

YALE<\/strong><\/p>

https:\/\/medicine.yale.edu\/news-article\/picture-perfect-can-an-image-based-electrocardiographic-algorithm-improve-access-to-care-in-remote-settings\/<\/a><\/p>

NATURE<\/strong><\/p>

https:\/\/www.nature.com\/articles\/s41467-022-29153-3<\/a><\/p>

HEALTH IT ANALYSIS<\/strong><\/p>

https:\/\/healthitanalytics.com\/news\/yale-researchers-develop-ai-model-for-heart-condition-diagnosis<\/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 modelo de Inteligencia Artificial (IA), utiliza im\u00e1genes de electrocardiogramas para diagnosticar diferentes enfermedades cardiacas. Investigadores del Yale Cardiovascular Data Science (CarDS) Lab, de la escuela de Medicina de Yale, lograron desarrollaron un modelo basado en IA, que ayuda a mejorar el diagn\u00f3stico de arritmia card\u00edaca a trav\u00e9s del an\u00e1lisis de im\u00e1genes de electrocardiogramas (ECG). El equipo liderado por el especialista Rohan Khera, profesor asistente de medicina cardiovascular, explic\u00f3 que la mayor\u00eda de las herramientas basadas en IA est\u00e1n dise\u00f1adas para desordenes cl\u00ednicos individuales, por lo que tienen una utilidad limitada. En este caso, la soluci\u00f3n dise\u00f1ada por CarDS Lab, tiene como objetivo mejorar la interpretaci\u00f3n de ECG incluso de manera remota. Los resultados de la investigaci\u00f3n fueron publicados en Nature:  https:\/\/www.nature.com\/articles\/s41467-022-29153-3. La investigaci\u00f3n detalla que es posible mejorar la atenci\u00f3n y diagn\u00f3stico basado en ECG en entornos remotos, por ello desarrollaron un modelo de diagn\u00f3stico automatizado multimarca para im\u00e1genes electrocardiogr\u00e1ficas, m\u00e1s adecuado para un uso m\u00e1s amplio\u201d. El estudio muestra que, a trav\u00e9s de 2,228,236 se\u00f1ales de ECG de 12 derivaciones en 811 municipios de Brasil se transforman en im\u00e1genes de ECG en diferentes conformaciones de derivaciones para entrenar una red neuronal convolucional (CNN). Los datos corresponden a pacientes en Brasil que fueron atendidos entre 2010 y 2017. De los cuales uno de cada seis pacientes fue diagnosticado con alteraciones del ritmo cardiaco. \u201cLas herramientas de IA actuales se basan en se\u00f1ales electrocardiogr\u00e1ficas sin procesar en lugar de im\u00e1genes almacenadas, que son mucho m\u00e1s comunes ya que los ECG a menudo se imprimen y escanean como im\u00e1genes. Adem\u00e1s, muchas herramientas de diagn\u00f3stico basadas en IA est\u00e1n dise\u00f1adas para trastornos cl\u00ednicos individuales y, por lo tanto, pueden tener una utilidad limitada en un entorno cl\u00ednico donde coexisten m\u00faltiples anomal\u00edas en el ECG\u201d, explic\u00f3 Khera. Otro punto que destac\u00f3 Khera, fue que se trata de un modelo inteligente que no depende de dise\u00f1os espec\u00edficos de ECG, ya que tiene la capacidad de adaptarse a nuevos dise\u00f1os. De esta forma puede mejorar y apoyar el trabajo de lectores humanos expertos. De esta forma el modelo tiene el potencial de expandir la aplicaci\u00f3n de la IA en la atenci\u00f3n cl\u00ednica, espec\u00edficamente en t\u00e9cnicas basadas en ECG, seg\u00fan explic\u00f3 Veer Sangha, autor principal del estudio. BIBLIOGRAF\u00cdA YALE https:\/\/medicine.yale.edu\/news-article\/picture-perfect-can-an-image-based-electrocardiographic-algorithm-improve-access-to-care-in-remote-settings\/ NATURE https:\/\/www.nature.com\/articles\/s41467-022-29153-3 HEALTH IT ANALYSIS https:\/\/healthitanalytics.com\/news\/yale-researchers-develop-ai-model-for-heart-condition-diagnosis<\/p>","protected":false},"author":1,"featured_media":24715,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3393,160],"tags":[145],"class_list":["post-24714","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-inteligencia-artificial-y-ciencia","category-noticias","tag-noticias"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/24714","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=24714"}],"version-history":[{"count":0,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/24714\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media\/24715"}],"wp:attachment":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media?parent=24714"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/categories?post=24714"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/tags?post=24714"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}