{"id":6361,"date":"2023-10-11T20:13:23","date_gmt":"2023-10-11T20:13:23","guid":{"rendered":"https:\/\/dailyai.com\/?p=6361"},"modified":"2023-10-11T20:21:52","modified_gmt":"2023-10-11T20:21:52","slug":"ai-models-helps-brain-surgeons-detect-and-analyze-tumor-types-on-the-fly","status":"publish","type":"post","link":"https:\/\/dailyai.com\/es\/2023\/10\/ai-models-helps-brain-surgeons-detect-and-analyze-tumor-types-on-the-fly\/","title":{"rendered":"Un nuevo modelo de IA ayuda a los neurocirujanos a analizar tumores sobre la marcha"},"content":{"rendered":"

Los neurocirujanos se enfrentan a menudo a decisiones cr\u00edticas durante el proceso de extirpaci\u00f3n del tumor.\u00a0<\/b><\/p>\n

Ahora, cient\u00edficos holandeses han introducido una herramienta de IA para ayudar en el proceso de toma de decisiones intraquir\u00fargicas, proporcionando a los cirujanos informaci\u00f3n ultrarr\u00e1pida sobre tipos y subtipos de tumores.<\/p>\n

Seg\u00fan un reciente <\/span>estudio publicado en Nature<\/span><\/a>La IA examina segmentos espec\u00edficos del ADN de un tumor, identificando estructuras qu\u00edmicas distintas. <\/span><\/p>\n

Este an\u00e1lisis permite conocer el tipo de tumor y, posiblemente, su subtipo. Esta informaci\u00f3n oportuna puede orientar a los cirujanos en su planteamiento de la operaci\u00f3n quir\u00fargica.<\/span><\/p>\n

\"Es imprescindible conocer el subtipo de tumor en el momento de la cirug\u00eda\", declar\u00f3 Jeroen de Ridder, profesor asociado del UMC de Utrecht. \"Lo que ahora hemos hecho posible de forma \u00fanica es permitir que este diagn\u00f3stico de grano muy fino, robusto y detallado se realice ya durante la cirug\u00eda\".<\/span><\/p>\n

Su sistema de aprendizaje profundo, denominado Sturgeon, fue sometido a rigurosas pruebas. En algunas pruebas iniciales, la IA se abstuvo de diagnosticar debido a datos ambiguos.<\/span><\/p>\n

En conjunto, los investigadores demostraron la eficacia del modelo en tiempo real en 25 intervenciones quir\u00fargicas, logrando un tiempo de diagn\u00f3stico r\u00e1pido inferior a 90 minutos, mucho m\u00e1s r\u00e1pido que los m\u00e9todos tradicionales. 72% de los diagn\u00f3sticos fueron correctos, pero siete no alcanzaron el umbral de confianza requerido.<\/p>\n

Mientras que el proceso de diagn\u00f3stico est\u00e1ndar implica un examen microsc\u00f3pico de muestras de tumores cerebrales, la secuenciaci\u00f3n gen\u00e9tica exhaustiva ofrece conocimientos m\u00e1s profundos. Sin embargo, como destac\u00f3 el Dr. Alan Cohen, de Johns Hopkins, \"tenemos que empezar el tratamiento sin saber qu\u00e9 estamos tratando.\"<\/span><\/p>\n

El Dr. de Ridder explic\u00f3 con m\u00e1s detalle la capacidad de la IA: \"Puede averiguar por s\u00ed misma lo que est\u00e1 viendo y hacer una clasificaci\u00f3n s\u00f3lida\".<\/span><\/p>\n

A\u00fan as\u00ed, persisten algunos retos. La variabilidad dentro del tumor, el tama\u00f1o de la muestra y ciertos tumores escurridizos pueden plantear dificultades. A este respecto, Marc Pag\u00e8s-Gallego, coautor de un estudio, explic\u00f3 c\u00f3mo resolvieron estos problemas.<\/span><\/p>\n

El Dr. Sebastian Brandner, del University College de Londres, coment\u00f3 los aspectos pr\u00e1cticos y afirm\u00f3: \"La aplicaci\u00f3n en s\u00ed es menos sencilla de lo que a menudo se sugiere\".<\/span><\/p>\n

Aunque la herramienta representa un avance significativo, tiene sus limitaciones.\u00a0<\/span><\/p>\n

A<\/span>omo admiti\u00f3 el Dr. Cohen, \"hemos logrado algunos avances, pero no tantos en el tratamiento como en la comprensi\u00f3n del perfil molecular de los tumores\".<\/span><\/p>\n

M\u00e1s informaci\u00f3n sobre el estudio<\/span><\/h2>\n

La innovadora herramienta 'Sturgeon' utiliza el aprendizaje profundo para mejorar la clasificaci\u00f3n intraoperatoria de los tumores cerebrales, ayudando a una mejor toma de decisiones quir\u00fargicas.\u00a0<\/span><\/p>\n

El modelo est\u00e1 dise\u00f1ado para ser \"ultrarr\u00e1pido\" en la clasificaci\u00f3n de tipos y subtipos de tumores.<\/span><\/p>\n

    \n
  • Desaf\u00edos:<\/b> Clasificar los tumores durante la cirug\u00eda es dif\u00edcil debido al tiempo limitado de secuenciaci\u00f3n y a la cobertura incierta de los datos.\u00a0<\/span><\/li>\n
  • La soluci\u00f3n de Sturgeon:<\/b> Este sistema de aprendizaje profundo utiliza datos derivados de matrices de metilaci\u00f3n ampliamente disponibles, que se utilizan para crear perfiles de tumores cerebrales. Sturgeon subcontrata tareas inform\u00e1ticas intensivas para minimizar los recursos inform\u00e1ticos durante la cirug\u00eda.<\/span><\/li>\n
  • Rendimiento:<\/b> El modelo ha mostrado resultados consistentes, pero a menudo no tiene en cuenta la diversidad intratumoral. Proporcion\u00f3 el diagn\u00f3stico correcto en 72% de las pruebas quir\u00fargicas.\u00a0<\/span><\/li>\n
  • Desarrollo futuro:<\/b> A medida que se adquieran m\u00e1s datos, los de Sturgeon se ir\u00e1n ampliando, aunque las restricciones a la hora de compartir datos por motivos de privacidad dificultan el aprendizaje interinstitucional.\u00a0<\/span><\/li>\n
  • Limitaciones:<\/b> Una posible limitaci\u00f3n es la cantidad de tejido necesaria. El modelo requiere un tama\u00f1o de muestra de unos 5 x 5 x 5 mm para obtener los mejores resultados, pero se ha extra\u00eddo con \u00e9xito suficiente ADN de muestras m\u00e1s peque\u00f1as.<\/span><\/li>\n<\/ul>\n

    Y lo que es m\u00e1s importante, con Sturgeon, los resultados del diagn\u00f3stico tumoral pueden obtenerse en 90 minutos, en consonancia con los plazos quir\u00fargicos. De este modo, los cirujanos disponen de informaci\u00f3n en tiempo real que les permite tomar mejores decisiones durante la intervenci\u00f3n.\u00a0<\/span><\/p>\n

    Aunque los datos de Sturgeon son beneficiosos, lo ideal ser\u00eda utilizarlos junto con la evaluaci\u00f3n de un pat\u00f3logo cualificado, seg\u00fan el estudio.\u00a0<\/span><\/p>\n

    Sturgeon se une a la legi\u00f3n de modelos de IA de vanguardia orientados a la medicina creados este a\u00f1o, lo que ilustra la capacidad de la tecnolog\u00eda para transformar los resultados de la atenci\u00f3n sanitaria.<\/p>","protected":false},"excerpt":{"rendered":"

    Los cirujanos cerebrales se enfrentan a menudo a decisiones cr\u00edticas durante el proceso de extirpaci\u00f3n de un tumor. Ahora, unos cient\u00edficos holandeses han introducido una herramienta de IA para facilitar el proceso de toma de decisiones intraquir\u00fargicas, proporcionando a los cirujanos una visi\u00f3n ultrarr\u00e1pida de los tipos y subtipos de tumores. Seg\u00fan un reciente estudio publicado en Nature, la IA examina segmentos espec\u00edficos del ADN de un tumor, identificando estructuras qu\u00edmicas distintas. Este an\u00e1lisis permite conocer el tipo de tumor y, posiblemente, su subtipo. Esta informaci\u00f3n oportuna puede orientar a los cirujanos en su planteamiento de la operaci\u00f3n quir\u00fargica. \"Es imprescindible conocer el subtipo de tumor en el momento de la intervenci\u00f3n quir\u00fargica\", afirma Jeroen de Ridder, investigador de la Universidad de Harvard.<\/p>","protected":false},"author":2,"featured_media":6362,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[88],"tags":[203,204,101],"class_list":["post-6361","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ethics","tag-biotech","tag-healthcare","tag-medtech"],"yoast_head":"\nNew AI model helps brain surgeons analyze tumors on the fly | DailyAI<\/title>\n<meta name=\"description\" content=\"Brain surgeons often grapple with critical decisions when addressing the boundaries of tumors.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, 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