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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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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. |
format | Online Article Text |
id | pubmed-10082817 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
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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