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Cholec80-CVS: An open dataset with an evaluation of Strasberg’s critical view of safety for AI

Strasberg’s criteria to detect a critical view of safety is a widely known strategy to reduce bile duct injuries during laparoscopic cholecystectomy. In spite of its popularity and efficiency, recent studies have shown that human miss-identification errors have led to important bile duct injuries oc...

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Autores principales: Ríos, Manuel Sebastián, Molina-Rodriguez, María Alejandra, Londoño, Daniella, Guillén, Camilo Andrés, Sierra, Sebastián, Zapata, Felipe, Giraldo, Luis Felipe
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10082817/
https://www.ncbi.nlm.nih.gov/pubmed/37031247
http://dx.doi.org/10.1038/s41597-023-02073-7
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author Ríos, Manuel Sebastián
Molina-Rodriguez, María Alejandra
Londoño, Daniella
Guillén, Camilo Andrés
Sierra, Sebastián
Zapata, Felipe
Giraldo, Luis Felipe
author_facet Ríos, Manuel Sebastián
Molina-Rodriguez, María Alejandra
Londoño, Daniella
Guillén, Camilo Andrés
Sierra, Sebastián
Zapata, Felipe
Giraldo, Luis Felipe
author_sort Ríos, Manuel Sebastián
collection PubMed
description Strasberg’s criteria to detect a critical view of safety is a widely known strategy to reduce bile duct injuries during laparoscopic cholecystectomy. In spite of its popularity and efficiency, recent studies have shown that human miss-identification errors have led to important bile duct injuries occurrence rates. Developing tools based on artificial intelligence that facilitate the identification of a critical view of safety in cholecystectomy surgeries can potentially minimize the risk of such injuries. With this goal in mind, we present Cholec80-CVS, the first open dataset with video annotations of Strasberg’s Critical View of Safety (CVS) criteria. Our dataset contains CVS criteria annotations provided by skilled surgeons for all videos in the well-known Cholec80 open video dataset. We consider that Cholec80-CVS is the first step towards the creation of intelligent systems that can assist humans during laparoscopic cholecystectomy.
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spelling pubmed-100828172023-04-10 Cholec80-CVS: An open dataset with an evaluation of Strasberg’s critical view of safety for AI Ríos, Manuel Sebastián Molina-Rodriguez, María Alejandra Londoño, Daniella Guillén, Camilo Andrés Sierra, Sebastián Zapata, Felipe Giraldo, Luis Felipe Sci Data Data Descriptor Strasberg’s criteria to detect a critical view of safety is a widely known strategy to reduce bile duct injuries during laparoscopic cholecystectomy. In spite of its popularity and efficiency, recent studies have shown that human miss-identification errors have led to important bile duct injuries occurrence rates. Developing tools based on artificial intelligence that facilitate the identification of a critical view of safety in cholecystectomy surgeries can potentially minimize the risk of such injuries. With this goal in mind, we present Cholec80-CVS, the first open dataset with video annotations of Strasberg’s Critical View of Safety (CVS) criteria. Our dataset contains CVS criteria annotations provided by skilled surgeons for all videos in the well-known Cholec80 open video dataset. We consider that Cholec80-CVS is the first step towards the creation of intelligent systems that can assist humans during laparoscopic cholecystectomy. Nature Publishing Group UK 2023-04-08 /pmc/articles/PMC10082817/ /pubmed/37031247 http://dx.doi.org/10.1038/s41597-023-02073-7 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Data Descriptor
Ríos, Manuel Sebastián
Molina-Rodriguez, María Alejandra
Londoño, Daniella
Guillén, Camilo Andrés
Sierra, Sebastián
Zapata, Felipe
Giraldo, Luis Felipe
Cholec80-CVS: An open dataset with an evaluation of Strasberg’s critical view of safety for AI
title Cholec80-CVS: An open dataset with an evaluation of Strasberg’s critical view of safety for AI
title_full Cholec80-CVS: An open dataset with an evaluation of Strasberg’s critical view of safety for AI
title_fullStr Cholec80-CVS: An open dataset with an evaluation of Strasberg’s critical view of safety for AI
title_full_unstemmed Cholec80-CVS: An open dataset with an evaluation of Strasberg’s critical view of safety for AI
title_short Cholec80-CVS: An open dataset with an evaluation of Strasberg’s critical view of safety for AI
title_sort cholec80-cvs: an open dataset with an evaluation of strasberg’s critical view of safety for ai
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10082817/
https://www.ncbi.nlm.nih.gov/pubmed/37031247
http://dx.doi.org/10.1038/s41597-023-02073-7
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