Research on Identifying Critical Surgical Anatomy in Cholecystectomy Videos Based on Deep Learning

Recruiting

• Conditions: Cholecystectomy; Surgical Video Identification

• Registration Number: NCT07158372
• Lead Sponsor: Chinese Academy of Sciences

Brief Summary

Laparoscopic cholecystectomy is a common surgical procedure, but it carries the potential for bile duct injury and other surgical risks. To provide visual assistance to surgeons during surgery and mitigate these risks, this research project aims to develop a real-time object recognition algorithm based on deep learning technology. This algorithm will label key anatomical structures in laparoscopic cholecystectomy videos, providing surgeons with immediate information on dangerous and safe areas.

Detailed Description

Laparoscopic cholecystectomy is a common surgical procedure, but it carries the potential for bile duct injury and other surgical risks. To provide visual assistance to surgeons during surgery and mitigate these risks, this research project aims to develop a real-time object recognition algorithm based on deep learning technology. This algorithm will label key anatomical structures in laparoscopic cholecystectomy videos, providing surgeons with immediate information on dangerous and safe areas.

Study Timeline

• Status Verified: 2025-08
• Study Start: 2025-08-15
• Study First Submitted: 2025-08-28
• Study First Submitted QC: 2025-08-28
• Last Update Submitted: 2025-08-28
• Study First Posted: 2025-09-05
• Last Update Posted: 2025-09-05
• Primary Completion: 2026-08-15
• Study Completion: 2028-08-15

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 200

Inclusion Criteria

• Patients aged 18 or above who are diagnosed by a doctor as needing laparoscopic cholecystectomy

Exclusion Criteria

• Patients who did not undergo surgery at the original hospital and those whose videos were blurry were excluded.

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Dice Similarity Coefficient	3 years	Dice Similarity Coefficient is a statistical measure of the similarity between two sets of data. In the context of image segmentation, it is used to quantify the spatial overlap between a predicted segmentation mask and its corresponding ground truth mask.
Mean Intersection over Union	3 years	Mean Intersection over Union provides a measure of the overlap between the predicted segmentation and the ground truth, averaged across all classes present in the dataset.
Global Accuracy	3 years	The proportion of correctly classified pixels out of the total number of pixels in the image.

Secondary Outcome Measures

Name	Time	Method
Inference Latency	3 years	time taken by the algorithm to process a single video frame and generate the segmentation masks (inference latency), or equivalently, the number of frames processed per second

Trial Locations

Locations (5)

The First Affiliated Hospital of Zhengzhou University; 🇨🇳 Zhengzhou, Henan, China

Beijing Anzhen Hospital, Capital Medical University; 🇨🇳 Beijing, China

Beijing Luhe Hospital, Capital Medical University; 🇨🇳 Beijing, China

Peking university people's hospital; 🇨🇳 Beijing, China

Shanghai East Hospital of Tongji University; 🇨🇳 Shanghai, China