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

• Published in: Scientific data Vol. 10; no. 1; p. 194
• Main Authors: Ríos, Manuel Sebastián, Molina-Rodriguez, María Alejandra, Londoño, Daniella, Guillén, Camilo Andrés, Sierra, Sebastián, Zapata, Felipe, Giraldo, Luis Felipe
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 08-04-2023; Nature Publishing Group; Nature Portfolio
• Subjects: 692/699/1503/1328/1498/1375; 692/699/1503/1328/1498/1376; Annotations; Artificial intelligence; Bile ducts; Cholecystectomy; Data Descriptor; Datasets; Gallbladder; Gallbladder diseases; Humanities and Social Sciences; Identification; Intelligent systems; Laparoscopy; multidisciplinary; Neural networks; Safety; Science; Science (multidisciplinary); Surgeons; Surgery
• ISSN: 2052-4463; 2052-4463
• DOI: 10.1038/s41597-023-02073-7

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.