NCT07158372

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.

Trial Health

77
On Track

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Enrollment
200

participants targeted

Target at P75+ for all trials

Timeline
29mo left

Started Aug 2025

Typical duration for all trials

Geographic Reach
1 country

5 active sites

Status
recruiting

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

Study Progress22%
Aug 2025Aug 2028

Study Start

First participant enrolled

August 15, 2025

Completed
13 days until next milestone

First Submitted

Initial submission to the registry

August 28, 2025

Completed
8 days until next milestone

First Posted

Study publicly available on registry

September 5, 2025

Completed
11 months until next milestone

Primary Completion

Last participant's last visit for primary outcome

August 15, 2026

Expected
2 years until next milestone

Study Completion

Last participant's last visit for all outcomes

August 15, 2028

Last Updated

September 5, 2025

Status Verified

August 1, 2025

Enrollment Period

1 year

First QC Date

August 28, 2025

Last Update Submit

August 28, 2025

Conditions

Keywords

Surgical Video IdentificationDeep LearningCholecystectomyArtificial Intelligence

Outcome Measures

Primary Outcomes (3)

  • Dice Similarity Coefficient

    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.

    3 years

  • Mean Intersection over Union

    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.

    3 years

  • Global Accuracy

    The proportion of correctly classified pixels out of the total number of pixels in the image.

    3 years

Secondary Outcomes (1)

  • Inference Latency

    3 years

Other Outcomes (1)

  • Class-Specific Precision and Recall for Critical Structures

    3 years

Study Arms (4)

The First Affiliated Hospital of Zhengzhou University

patients aged 18 years and older diagnosed with laparoscopic cholecystectomy. We will collect information such as laparoscopic cholecystectomy videos and procedure type, excluding patients who did not undergo surgery at the original hospital or whose videos were blurry.

Diagnostic Test: AI-assisted Intraoperative Anatomy Analysis

Beijing Luhe Hospital, Capital Medical University

patients aged 18 years and older diagnosed with laparoscopic cholecystectomy. We will collect information such as laparoscopic cholecystectomy videos and procedure type, excluding patients who did not undergo surgery at the original hospital or whose videos were blurry.

Diagnostic Test: AI-assisted Intraoperative Anatomy Analysis

Shanghai East Hospital of Tongji University

patients aged 18 years and older diagnosed with laparoscopic cholecystectomy. We will collect information such as laparoscopic cholecystectomy videos and procedure type, excluding patients who did not undergo surgery at the original hospital or whose videos were blurry.

Diagnostic Test: AI-assisted Intraoperative Anatomy Analysis

Peking university people's hospital

patients aged 18 years and older diagnosed with laparoscopic cholecystectomy. We will collect information such as laparoscopic cholecystectomy videos and procedure type, excluding patients who did not undergo surgery at the original hospital or whose videos were blurry

Diagnostic Test: AI-assisted Intraoperative Anatomy Analysis

Interventions

This is a prospective study on patients aged 18 years or more diagnosed with laparoscopic cholecystectomy. We will collect information such as laparoscopic cholecystectomy videos and procedure type, excluding patients who did not undergo surgery at the original hospital or whose videos were blurry.

Beijing Luhe Hospital, Capital Medical UniversityPeking university people's hospitalShanghai East Hospital of Tongji UniversityThe First Affiliated Hospital of Zhengzhou University

Eligibility Criteria

Age18 Years+
Sexall
Healthy VolunteersNo
Age GroupsAdult (18-64), Older Adult (65+)
Sampling MethodNon-Probability Sample
Study Population

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

You may qualify if:

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

You may not qualify if:

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

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (5)

The First Affiliated Hospital of Zhengzhou University

Zhengzhou, Henan, China

RECRUITING

Beijing Anzhen Hospital, Capital Medical University

Beijing, China

RECRUITING

Beijing Luhe Hospital, Capital Medical University

Beijing, China

RECRUITING

Peking university people's hospital

Beijing, China

RECRUITING

Shanghai East Hospital of Tongji University

Shanghai, China

RECRUITING

Biospecimen

Retention: SAMPLES WITHOUT DNA

We are collecting laparoscopic cholecystectomy videos and related information. We plan to collect 2,000 retrospective and 200 prospective cases. Enrollment is limited to patients aged 18 years and older diagnosed with laparoscopic cholecystectomy. We will collect information such as laparoscopic cholecystectomy videos and procedure type, excluding patients who did not undergo surgery at the original hospital or whose videos were blurry.

Central Study Contacts

Qian Liang, M.A.

CONTACT

Study Design

Study Type
observational
Observational Model
COHORT
Time Perspective
PROSPECTIVE
Sponsor Type
OTHER GOV
Responsible Party
PRINCIPAL INVESTIGATOR
PI Title
Professor

Study Record Dates

First Submitted

August 28, 2025

First Posted

September 5, 2025

Study Start

August 15, 2025

Primary Completion (Estimated)

August 15, 2026

Study Completion (Estimated)

August 15, 2028

Last Updated

September 5, 2025

Record last verified: 2025-08

Locations