62 Evaluation of Laparoscopic Cholecystectomy with the help of AI-Video Analytics Platform. (12th October 2021)

• Record Type: Journal Article
• Authors: Gendia, A; Korambayil, S; Cota, A; Finlay, I; Clarke, M; Al-Ardah, M; Clark, J
• Is Part Of: British journal of surgery. Volume 108:Supplement 6(2021)
• Publication Date: 2021-10-12
• Subjects: Surgery -- Periodicals; 617.005
• DOI: 10.1093/bjs/znab258.020
• Languages: English
• ISSNs: 0007-1323

Abstract

Abstract: Aim: This report aims to evaluate the use of an AI video analytics platform in laparoscopic cholecystectomy (LC) based on the achievement of the critical view of safety (CVS) and to assess its ability to correctly comment on CVS. Method: Touch surgery video platform, an AI-video based analytic tool, was screened for laparoscopic cholecystectomy in our institute and analysed from April 2019 till October 2020. Data collected by the AI included identification of the critical view of safety and time needed to achieve CVS. A reviewer graded the LC according to Nasser grading and evaluated the ability of AI to identify the CVS. Results: 66 LC were included from our video database. CVS was achieved in only 56% (37/66) in all LC videos included. Mean time spent to attend CVS from start of dissection of Calot's triangle 16.8 (±13.6) mins. 26 (39.4%) LCs were Nasser grade 2 and 20 (30.3%) each were grade 1 and grade 3. There was no significant difference between number of CVS obtained between all grades. Time spent to achieve CVS from dissecting Calot's triangle were the longest in grade 3 LCs (28.4±17.4 mins) with significant difference between all 3 grades. Finally, the platform correctly commented on CVS in 92.4% of the all LC videos. Conclusions: AI video analytics can provide a useful tool to assess laparoscopic cholecystectomies and the critical view of safety. Additionally, more studies should explore the use of the platfrom and integrate the results with the clinical outcomes.