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The Improvement Effect of Real-time Artificial Intelligence Assisted Identification of Bleeding Points on Hemostasis Efficiency in Endoscopic Submucosal Dissection

NCT07495137 · Status: NOT_YET_RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 160

Last updated 2026-03-27

No results posted yet for this study

Summary

The goal of this clinical trial is to learn if an artificial intelligence (AI) system that identifies bleeding points in real time can help stop bleeding faster during endoscopic submucosal dissection (ESD) - a minimally invasive surgery for early digestive tract cancer or precancerous lesions. It will also learn about the AI system's effect on surgery-related problems (like perforation or delayed bleeding) and total surgery time.

The main questions it aims to answer are:

1. Does the AI system shorten the time it takes to stop each bleed during ESD?
2. How does the AI system affect the rate of surgery-related problems and total surgery time?

Researchers will compare two groups to see if the AI system improves hemostasis efficiency:

1. AI group: During ESD, the AI system will real-time spot and mark bleeding points. Doctors will use these marks to stop bleeding.
2. Control group: Doctors will use the same equipment but without the AI system - they will find and stop bleeding using their own experience.

Participants will:

1. Have ESD surgery for esophageal, stomach, or colorectal lesions that need this treatment;
2. Be randomly assigned to either the AI group or the control group;
3. Attend follow-up checks in 14 days after surgery to check for complications;
4. Have their surgery videos reviewed by experts to record hemostasis time and total surgery time.

Conditions

  • Endoscopic Submucosal Dissection
  • Endoscopic Submucosal Dissection (ESD)

Interventions

DEVICE

AI real-time assistance in endoscopic submucosal dissection (ESD) for bleeding spot identification and marking

Patients undergo ESD with real-time AI assistance. During the operation, the pre-trained and validated AI system continuously analyzes endoscopic images to automatically identify and mark active bleeding points in real time. Endoscopists perform hemostatic operations (e.g., coagulation with hemostatic forceps or electrosurgical knives) based on the AI-generated marks to target bleeding sites promptly.

Sponsors & Collaborators

  • Qianfoshan Hospital

    collaborator OTHER

  • Shandong Second Provincial General Hospital

    collaborator UNKNOWN

  • Qilu Hospital of Shandong University

    lead OTHER

Study Design

Allocation
RANDOMIZED
Purpose
TREATMENT
Masking
TRIPLE
Model
PARALLEL

Eligibility

Min Age
18 Years
Max Age
80 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2026-03-31
Primary Completion
2027-06-30
Completion
2027-12-31

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07495137 on ClinicalTrials.gov