Evaluación automática de la vista crítica de seguridad utilizando deep learning en la base de datos Cholec80-CVS

dc.contributorGiraldo Trujillo, Luis Felipe
dc.contributorSegura Quijano, Fredy Enrique
dc.creatorMugnier Zuluaga, Andrés
dc.date.accessioned2023-07-07T19:36:31Z
dc.date.accessioned2023-09-06T23:14:58Z
dc.date.available2023-07-07T19:36:31Z
dc.date.available2023-09-06T23:14:58Z
dc.date.created2023-07-07T19:36:31Z
dc.date.issued2023-06-27
dc.identifierhttp://hdl.handle.net/1992/68213
dc.identifierinstname:Universidad de los Andes
dc.identifierreponame:Repositorio Institucional Séneca
dc.identifierrepourl:https://repositorio.uniandes.edu.co/
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/8726346
dc.description.abstractLa colecistectomía laparoscópica es un procedimiento quirúrgico mínimamente invasivo utilizado para la extirpación de la vesícula biliar que puede provocar lesiones en el conducto biliar. Para prevenir estas lesiones, Strasberg y sus colegas propusieron el método Critical View of Safety para identificar el conducto cístico y la arteria durante estos procedimientos. En este trabajo entrenamos modelos de aprendizaje profundo con el primer conjunto de datos de código abierto de vídeos de colistectomía laparoscópica que contienen anotaciones de los criterios de Strasberg, llamado Cholec80-CVS. Este estudio representa el primer intento de investigar el desempeño de los modelos de aprendizaje profundo para ayudar a identificar la vista crítica de seguridad durante los procedimientos de colecistectomía laparoscópica utilizando el conjunto de datos Cholec80-CVS, y proporciona información sobre las limitaciones y los enfoques potenciales para futuras investigaciones en esta área
dc.languagespa
dc.publisherUniversidad de los Andes
dc.publisherIngeniería Electrónica
dc.publisherFacultad de Ingeniería
dc.publisherDepartamento de Ingeniería Eléctrica y Electrónica
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dc.rightsAtribución 4.0 Internacional
dc.rightsAtribución 4.0 Internacional
dc.rightshttp://creativecommons.org/licenses/by/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.titleEvaluación automática de la vista crítica de seguridad utilizando deep learning en la base de datos Cholec80-CVS
dc.typeTrabajo de grado - Pregrado


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