{"id":23299,"date":"2021-12-29T09:33:01","date_gmt":"2021-12-29T15:33:01","guid":{"rendered":"https:\/\/saluddigital.com\/?p=23299"},"modified":"2025-10-21T10:34:04","modified_gmt":"2025-10-21T16:34:04","slug":"cientificos-desarrollaron-algoritmo-para-la-deteccion-de-fracturas-en-imagenes-de-rayos-x","status":"publish","type":"post","link":"https:\/\/saluddigital.com\/en\/big-data\/cientificos-desarrollaron-algoritmo-para-la-deteccion-de-fracturas-en-imagenes-de-rayos-x\/","title":{"rendered":"Scientists developed algorithm for detecting fractures in X-ray images"},"content":{"rendered":"
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t

The algorithm designed through Artificial Intelligence (AI), could help doctors quickly detect fractures in patients in medical emergencies or emergency rooms.<\/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
\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

Researchers at the Boston University School of Medicine (BSUM) have developed an AI-based algorithm that could help specialists detect fractures on X-ray images more effectively.<\/p>

Dr. Ali Guermazi, chief of radiology at Boston Health System and professor of Radiology and Medicine at BUSM, explained that the algorithm can quickly and automatically detect positive X-rays and identify and mark those images so radiologists can prioritize your reading.<\/p>

\u201cThe system also highlights regions of interest with bounding boxes around areas where fractures are suspected. This can potentially contribute to reducing the waiting time at the time of the hospital or clinic visit before patients can obtain a positive fracture diagnosis,\u201d explained Dr. Guermazi.<\/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

\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"\"\t\t\t\t\t\t\t\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
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

The application of AI would be very useful since fracture interpretation errors often occur, which affects a patient's diagnosis, especially in the emergency department. The fatigue of specialist doctors in the radiographic analysis of images, especially at night and early morning hours, may be a problem that would be solved thanks to the application of this innovation.<\/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

\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

AI BoneView, the algorithm used, was trained with a large number of radiographs with multiple instructions, so that it could detect fractures in the extremities, pelvis, torso, lumbar spine and rib cage.<\/p>

In this sense, the study carried out by the BUSM compared the fracture diagnosis results of a group of doctors without the help of AI with another group of doctors with the help of AI. Thus, the assistance of AI and algorithms helped reduce missed fractures by 29%, increased the sensitivity of readers by 16%, and also increased the sensitivity of studies with more than one fracture by 30%.<\/p>

\u201cOur study focused on fracture diagnosis, but a similar concept can be applied to other diseases and disorders. Our ongoing research interest is how best to use artificial intelligence to help human health care providers improve patient care, rather than have artificial intelligence replace human health care providers. . Our study showed one such example,\u201d concluded Dr. Guermazi.<\/p>

The findings were published in the scientific journal Radiology<\/em>:<\/p>

https:\/\/pubs.rsna.org\/doi\/10.1148\/radiol.210937<\/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

\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
\n\t\t\t\t\t
\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

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

https:\/\/healthitanalytics.com\/news\/usingartificial-intelligencetoidentifyfractures-on-x-rays<\/a><\/p>

BUSM<\/strong><\/p>

https:\/\/www.bumc.bu.edu\/busm\/2021\/12\/21\/artificial-intelligence-accurately-detects-fractures-on-x-rays-alert-human-readers\/<\/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 algoritmo dise\u00f1ado a trav\u00e9s de Inteligencia Artificial (IA), podr\u00eda ayudar a los m\u00e9dicos a detectar de manera r\u00e1pida las fracturas en pacientes de emergencias m\u00e9dicas o salas de urgencias. Investigadores de la Escuela de Medicina de la Universidad de Boston (BSUM), han desarrollado un algoritmo basado en IA, que podr\u00eda ayudar a especialistas a detectar fracturas en im\u00e1genes de rayos X, de manera m\u00e1s eficaz. El Dr. Ali Guermazi, jefe de radiolog\u00eda del Sistema de Salud de Boston y profesor de Radiolog\u00eda y Medicina en BUSM, explic\u00f3 que el algoritmo puede detectar r\u00e1pidamente y de forma autom\u00e1tica rayos X positivos e identificar y marcar esas im\u00e1genes para que los radi\u00f3logos puedan priorizar su lectura. \u201cEl sistema tambi\u00e9n resalta las regiones de inter\u00e9s con cuadros delimitadores alrededor de las \u00e1reas donde se sospecha que hay fracturas. Esto puede contribuir potencialmente a reducir el tiempo de espera en el momento de la visita al hospital o la cl\u00ednica antes de que los pacientes puedan obtener un diagn\u00f3stico positivo de fractura\u201d, explic\u00f3 el Dr. Guermazi. La aplicaci\u00f3n de IA ser\u00eda muy \u00fatil ya que suelen ocurrir errores de interpretaci\u00f3n de fracturas, lo que afecta el diagn\u00f3stico de un paciente, especialmente en el departamento de emergencias. La fatiga de los m\u00e9dicos especialistas en el an\u00e1lisis radiogr\u00e1fico de las im\u00e1genes especialmente en horarios durante la noche y la madrugada, puede ser un problema que ser\u00eda solucionado gracias a la aplicaci\u00f3n de esta innovaci\u00f3n. AI BoneView, el algoritmo utilizado, fue entrenado con una gran cantidad de radiograf\u00edas con m\u00faltiples instrucciones, para que lograra detectar fracturas en extremidades, pelvis, torso, columna lumbar y caja tor\u00e1cica. En ese sentido, el estudio realizado por la BUSM, compar\u00f3 los resultados de diagn\u00f3stico de fracturas de un grupo de m\u00e9dicos sin ayuda de IA con otro grupo de m\u00e9dicos con ayuda de IA. De esta forma, la asistencia de la IA y los algoritmos ayud\u00f3 a reducir las fracturas perdidas por 29%, aument\u00f3 la sensibilidad de los lectores en un 16%, y tambi\u00e9n aument\u00f3 en 30% la sensibilidad de estudios con m\u00e1s de una fractura. \u201cNuestro estudio se centr\u00f3 en el diagn\u00f3stico de fracturas, pero un concepto similar se puede aplicar a otras enfermedades y trastornos. Nuestro inter\u00e9s de investigaci\u00f3n en curso es saber cu\u00e1l es la mejor manera de utilizar la inteligencia artificial para ayudar a los proveedores de atenci\u00f3n m\u00e9dica humana a mejorar la atenci\u00f3n al paciente, en lugar de hacer que la inteligencia artificial reemplace a los proveedores de atenci\u00f3n m\u00e9dica humana. Nuestro estudio mostr\u00f3 uno de esos ejemplos\u201d, concluy\u00f3 el Dr. Guermazi. Los hallazgos fueron publicados en la revista cient\u00edfica Radiology: https:\/\/pubs.rsna.org\/doi\/10.1148\/radiol.210937 BIBLIOGRAF\u00cdA HEALTH IT ANALYTICS https:\/\/healthitanalytics.com\/news\/usingartificial-intelligencetoidentifyfractures-on-x-rays BUSM https:\/\/www.bumc.bu.edu\/busm\/2021\/12\/21\/artificial-intelligence-accurately-detects-fractures-on-x-rays-alert-human-readers\/<\/p>","protected":false},"author":1,"featured_media":23300,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[156,3393,160],"tags":[145],"class_list":["post-23299","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-big-data","category-inteligencia-artificial-y-ciencia","category-noticias","tag-noticias"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/23299","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=23299"}],"version-history":[{"count":0,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/23299\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media\/23300"}],"wp:attachment":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media?parent=23299"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/categories?post=23299"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/tags?post=23299"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}