NCT06732271

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

• Status: Completed
• Phase: —
• Study type: Observational
• Enrollment: 308 (actual)
• Sponsor: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University · Academic / Other
• Sex: All
• Age: —
• Healthy volunteers: —

Summary

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.

Conditions

• Laparoscopic Cholecystectomy

Timeline

• Start date: 2024-10-24
• Primary completion: 2024-11-24
• Completion: 2024-11-30
• First posted: 2024-12-13
• Last updated: 2024-12-13

Locations

1 site across 1 country: China