{"id":31011,"date":"2022-08-17T10:28:20","date_gmt":"2022-08-17T15:28:20","guid":{"rendered":"https:\/\/saluddigital.com\/?p=31011"},"modified":"2025-10-20T11:22:17","modified_gmt":"2025-10-20T17:22:17","slug":"cientificos-desarrollan-modelo-de-ia-para-detectar-defectos-de-nacimiento-en-imagenes-de-ecografias","status":"publish","type":"post","link":"https:\/\/saluddigital.com\/en\/big-data\/cientificos-desarrollan-modelo-de-ia-para-detectar-defectos-de-nacimiento-en-imagenes-de-ecografias\/","title":{"rendered":"Scientists develop AI model to detect birth defects in ultrasound images"},"content":{"rendered":"
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A team from the University of Ottawa (uOttawa) has created a model enabled by Artificial Intelligence (AI) to diagnose birth defects on obstetric ultrasound images.<\/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 study conducted by a team from the uOttawa School of Medicine shows the use of a deep learning model as an assistive tool for fast and accurate reading of fetal ultrasounds. The study was published in the scientific journal PLOS One: https:\/\/journals.plos.org\/plosone\/article?id=10.1371\/journal.pone.0269323<\/a>.<\/p>

The objective of the study was to demonstrate the potential of deep learning for the early and reliable identification of cystic hygroma, through ultrasound performed during the first trimester. This condition causes the embryos to suffer changes in their lymphatic vascular system and does not develop properly. In this sense, it is a rare and life-threatening disorder, which requires early diagnosis.<\/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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This defect is easy to spot at a routine ultrasound appointment, however the researchers sought to test how effective AI was at pattern recognition in these types of images. For this they used around 300 fetal ultrasounds obtained from the Ottawa Hospital.<\/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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Subsequently, the images were analyzed using the DenseNet model, which was able to correctly identify the cases of cystic hygroma. Heat maps were also generated to evaluate the interpretability of the model. Finally, the model achieved an overall accuracy of the 93%.<\/p>

\u201cThe model was outstanding, even with a small number of training images. So potentially what we showed was that in the field of ultrasound we can use the same tools for image classification and identification with high sensitivity and specificity,\u201d said Dr. Mark Walker, lead of the study.<\/p>

Dr. Walker explained that the team now aims to develop a cloud-based database for uploading obstetric ultrasound images to help specialists in low- and middle-income countries easily interpret these types of diagnoses.<\/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

UOTTAWA<\/strong><\/p>

https:\/\/med.uottawa.ca\/en\/news\/uottawa-led-team-develops-ai-enabled-model-diagnose-birth-defect-fetal-ultrasound-images<\/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":"

Equipo de la Universidad de Ottawa (uOttawa) ha creado un modelo habilitado para que la Inteligencia Artificial (IA) pueda diagnosticar defectos de nacimiento en im\u00e1genes de ecograf\u00eda obst\u00e9trica. Un estudio realizado por un equipo de la Facultad de Medicina de uOttawa, muestra el uso de un modelo de aprendizaje profundo como herramienta de asistencia para la lectura r\u00e1pida y precisa de ultrasonidos fetales. El estudio fue publicado en la revista cient\u00edfica PLOS One: https:\/\/journals.plos.org\/plosone\/article?id=10.1371\/journal.pone.0269323. El objetivo del estudio fue demostrar el potencial del aprendizaje profundo para la identificaci\u00f3n temprana y confiable de higroma qu\u00edstico, por medio de ecograf\u00edas realizadas durante el primer trimestre. Esta afecci\u00f3n hace que los embriones sufran cambios en su sistema vascular linf\u00e1tico y no se desarrolle de manera apropiada. En este sentido, se trata de un trastorno poco com\u00fan y potencialmente mortal, por lo que requiere un diagn\u00f3stico temprano. Este defecto es f\u00e1cil de detectar en una cita de ultrasonidos de rutina, sin embargo, los investigadores buscaron probar qu\u00e9 tan efectiva era la IA para el reconocimiento de patrones en este tipo de im\u00e1genes. Para ello utilizaron alrededor de 300 ecograf\u00edas fetales obtenidas del Hospital de Ottawa. Posteriormente, las im\u00e1genes fueron analizadas por medio del modelo DenseNet, que logr\u00f3 identificar correctamente los casos de higroma qu\u00edstico, asimismo se generaron mapas de calor, para evaluar la interpretabilidad del modelo. Finalmente, el modelo logr\u00f3 una precisi\u00f3n general del 93%. \u201cEl modelo fue sobresaliente, incluso con una peque\u00f1a cantidad de im\u00e1genes de entrenamiento. Entonces, potencialmente, lo que demostramos fue que en el campo del ultrasonido podemos usar las mismas herramientas para la clasificaci\u00f3n e identificaci\u00f3n de im\u00e1genes con una alta sensibilidad y especificidad\u201d, dijo el Dr. Mark Walker, director del estudio. El Dr. Walker explic\u00f3 que ahora el equipo tiene como objetivo desarrollar una base de datos basada en la nube para cargar im\u00e1genes de ecograf\u00edas obst\u00e9tricas y ayudar a que especialistas de pa\u00edses con ingresos bajos y medianos interpreten con facilidad este tipo de diagn\u00f3sticos. BIBLIOGRAF\u00cdA UOTTAWA https:\/\/med.uottawa.ca\/en\/news\/uottawa-led-team-develops-ai-enabled-model-diagnose-birth-defect-fetal-ultrasound-images<\/p>","protected":false},"author":1,"featured_media":31013,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[156,160],"tags":[145],"class_list":["post-31011","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-big-data","category-noticias","tag-noticias"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/31011","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=31011"}],"version-history":[{"count":0,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/31011\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media\/31013"}],"wp:attachment":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media?parent=31011"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/categories?post=31011"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/tags?post=31011"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}