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Related Experiment Video

Updated: Jun 9, 2026

The Role of Indocyanine Green Fluorescence in Complex Laparoscopic Cholecystectomy Navigation
03:27

The Role of Indocyanine Green Fluorescence in Complex Laparoscopic Cholecystectomy Navigation

Published on: January 31, 2025

AI-based computer vision as an adjunct tool for CVS documentation in laparoscopic cholecystectomy.

Danit Dayan1,2, Monica Ortenzi3, Eran Nizri4,5

  • 1Division of General Surgery, Tel Aviv Medical Center, 6 Weizman St., 6423909, Tel Aviv, Israel. danitd.75@gmail.com.

Surgical Endoscopy
|June 8, 2026
PubMed
Summary

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An AI platform improved documentation of the Critical View of Safety (CVS) during laparoscopic cholecystectomy (LC). This surgical AI tool demonstrated superior accuracy compared to traditional operative reports, enhancing patient safety.

Area of Science:

  • Surgical Safety
  • Artificial Intelligence in Medicine
  • Medical Documentation

Background:

  • Critical View of Safety (CVS) is crucial for preventing bile duct injury during laparoscopic cholecystectomy (LC).
  • Low attainment of CVS is linked to inaccurate operative reports and lack of visual documentation.
  • Surgical artificial intelligence (AI) offers potential for improved documentation.

Purpose of the Study:

  • To evaluate an AI platform's performance in documenting CVS during LC.
  • To compare AI documentation accuracy against narrative operative reports.
  • To assess AI's reliability as an adjunct for quality assurance in surgical safety.

Main Methods:

  • Retrospective analysis of 279 LC videos with AI-based computer vision analysis.
Keywords:
Artificial intelligenceComputer visionCritical view of safetyLaparoscopic cholecystectomyOperative documentation

Related Experiment Videos

Last Updated: Jun 9, 2026

The Role of Indocyanine Green Fluorescence in Complex Laparoscopic Cholecystectomy Navigation
03:27

The Role of Indocyanine Green Fluorescence in Complex Laparoscopic Cholecystectomy Navigation

Published on: January 31, 2025

  • Expert surgeon review established ground-truth for CVS achievement.
  • Comparison of AI and operative report classifications against expert consensus.
  • Performance metrics including AUROC and MCC were calculated, stratified by disease severity.

    • Main Results:

    • The AI platform showed higher reliability (κ=0.45) and superior discriminatory performance (AUROC=0.77) compared to operative reports (κ=0.13, AUROC=0.55).
    • AI documentation aligned better with expert ground-truth, especially in high disease severity cases.
    • Operative reports overestimated CVS achievement (92.5%) compared to expert review (74.2%).

    Conclusions:

    • The AI platform demonstrates superior discriminatory performance and reliability for CVS documentation compared to narrative reports.
    • AI serves as a valuable adjunct for improving surgical safety documentation and quality assurance.
    • Implementing AI can enhance the accuracy of safety protocols in laparoscopic procedures.