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Computer Vision Applications in Laparoscopic Cholecystectomy: Are We Ready? A Systematic Review
Intekhab Hossain1,2,3,4, Abdulrahman Alomar4,5, Daniel Tham4,6,7
1Division of General Surgery, University Health Network, Toronto, ON, Canada.
Surgical Innovation
|August 14, 2026
Summary
Computer vision models show promise in laparoscopic cholecystectomy for tasks like workflow analysis and safety assessment. However, clinical implementation faces challenges, requiring standardized reporting and validation for effective integration.
Area of Science:
- Medical Imaging
- Surgical Technology
- Artificial Intelligence in Surgery
Background:
- Laparoscopic cholecystectomy is a common surgical procedure.
- Computer vision (CV) offers potential advancements in surgical procedures.
- The application of CV in laparoscopic cholecystectomy requires systematic evaluation.
Purpose of the Study:
- To systematically review computer vision models applied to laparoscopic cholecystectomy.
- To identify key application areas and reported performance metrics.
- To assess the challenges and future directions for CV implementation in this surgical domain.
Main Methods:
- Systematic literature review adhering to PRISMA guidelines.
- Inclusion of studies on primary CV model applications in laparoscopic cholecystectomy up to March 2026.
- Analysis of 85 studies categorized by CV application (e.g., workflow, anatomy, safety).
Main Results:
- CV models were applied to workflow analysis, anatomy recognition, safety assessment, instrument detection, tissue characterization, event detection, and performance assessment.
- Performance metrics were heterogeneous, including accuracy, F1-score, mAP, Dice coefficient, MAE, and AUC.
- Despite strong reported performances, clinical implementation remains a significant challenge.
Conclusions:
- Computer vision holds potential for enhancing laparoscopic cholecystectomy.
- Standardized performance reporting and multi-institutional validation are crucial.
- Coordinated frameworks are needed for safe and effective clinical implementation of CV in surgery.
