LC-Smart: A Deep Learning-Based Quality Control Model for Laparoscopic Cholecystectomy

Objective: Critical view of safety (CVS) is a successful technique to reduce bile duct injury during laparoscopic cholecystectomy (LC). We aimed to create a deep learning-based quality control model for LC and reduce the learning curve for junior surgeons, which would automatically assess whether surgeons are CVS conscious during procedures.Methods: We retrospectively collected 308 LC videos from public datasets (Cholec80, Endoscapes) and Sun Yat-sen Memorial Hospital. Video frames were labeled using binary classification and feature optimization methods, such as black border clipping and sliding windows. Two neural networks, ResNet-50 and EfficientNetV2-S, were trained and evaluated based on F1 scores and accuracy. Additionally, We created an online CVS recognition system (LC-Smart), tested it using 171 films from two hospitals, and compared the results to two local senior doctors.

Study Overview

• Status: Completed
• Conditions: Laparoscopic Cholecystectomy

• Study Type: Observational
• Enrollment (Actual): 308

Contacts and Locations

Study Locations

• China
  • Guangdong
    • Guangzhou, Guangdong, China, 510220
      • Sun Yat-sen Memorial Hospital，Sun Yat-sen University

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: N/A

• Sampling Method: Probability Sample

Study Population

people with laparoscopic cholecystectomy

Description

Inclusion Criteria:

• complete video data with no missing footage;
• surgical procedure identified as laparoscopic cholecystectomy;
• full visibility of the surgical area in the video;
• successful completion of the procedure;
• absence of significant anatomical variations
• video resolution no less than 720×560.

Exclusion Criteria:

• substantial intraoperative adhesions
• a history of previous abdominal or pelvic procedures
• a conversion to open surgery during the procedure
• significant bleeding that obscured structural identification.

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Time Frame
the surgical time	Nov/2023-Nov/2024

Collaborators and Investigators

• Sponsor: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

Study record dates

Study Major Dates

• Study Start (Actual): October 24, 2024
• Primary Completion (Actual): November 24, 2024
• Study Completion (Actual): November 30, 2024

Study Registration Dates

• First Submitted: December 9, 2024
• First Submitted That Met QC Criteria: December 11, 2024
• First Posted (Estimated): December 13, 2024

Study Record Updates

• Last Update Posted (Estimated): December 13, 2024
• Last Update Submitted That Met QC Criteria: December 11, 2024
• Last Verified: December 1, 2024

More Information

Terms related to this study

• Other Study ID Numbers: SYSKY-2024-1015-01

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No