Cholec80-CVS: An open dataset with an evaluation of Strasberg's critical view of safety for Artificial Intelligence

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 injuri...

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Hauptverfasser:	Ríos, Manuel Sebastián, Guillén, Camilo Andrés, Giraldo, Luis Felipe, Molina, María Alejandra, Londono, Daniella, Zapata, Sebastián, Zapata, Felipe
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Sprache:	eng
Schlagworte:	Artificial Intelligence and Image Processing FOS: Clinical medicine FOS: Computer and information sciences Surgery
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creator	Ríos, Manuel Sebastián Guillén, Camilo Andrés Giraldo, Luis Felipe Molina, María Alejandra Londono, Daniella Zapata, Sebastián Zapata, Felipe
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.
doi_str_mv	10.6084/m9.figshare.22183885
format	Dataset
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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.</description><identifier>DOI: 10.6084/m9.figshare.22183885</identifier><language>eng</language><publisher>figshare</publisher><subject>Artificial Intelligence and Image Processing ; FOS: Clinical medicine ; FOS: Computer and information sciences ; Surgery</subject><creationdate>2023</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>777,1888</link.rule.ids><linktorsrc>$$Uhttps://commons.datacite.org/doi.org/10.6084/m9.figshare.22183885$$EView_record_in_DataCite.org$$FView_record_in_$$GDataCite.org$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>Ríos, Manuel Sebastián</creatorcontrib><creatorcontrib>Guillén, Camilo Andrés</creatorcontrib><creatorcontrib>Giraldo, Luis Felipe</creatorcontrib><creatorcontrib>Molina, María Alejandra</creatorcontrib><creatorcontrib>Londono, Daniella</creatorcontrib><creatorcontrib>Zapata, Sebastián</creatorcontrib><creatorcontrib>Zapata, Felipe</creatorcontrib><title>Cholec80-CVS: An open dataset with an evaluation of Strasberg's critical view of safety for Artificial Intelligence</title><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. 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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.</abstract><pub>figshare</pub><doi>10.6084/m9.figshare.22183885</doi><oa>free_for_read</oa></addata></record>
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identifier	DOI: 10.6084/m9.figshare.22183885
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subjects	Artificial Intelligence and Image Processing FOS: Clinical medicine FOS: Computer and information sciences Surgery
title	Cholec80-CVS: An open dataset with an evaluation of Strasberg's critical view of safety for Artificial Intelligence
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