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

Author:

Ríos Manuel Sebastián,Molina-Rodriguez María Alejandra,Londoño Daniella,Guillén Camilo Andrés,Sierra Sebastián,Zapata Felipe,Giraldo Luis FelipeORCID

Abstract

AbstractStrasberg’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.

Funder

Google Deepmind funded this project through the scholarship programme.

Publisher

Springer Science and Business Media LLC

Subject

Library and Information Sciences,Statistics, Probability and Uncertainty,Computer Science Applications,Education,Information Systems,Statistics and Probability

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