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

NCT07495137 | Not Yet Recruiting

• 1 other identifier: 2026SDU-QILU-1
• Study Type: interventional
• Target: 160
• Locations: 0 countries
• Sites: N/A

Brief 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. 1.Does the AI system shorten the time it takes to stop each bleed during ESD?
2. 2.How does the AI system affect the rate of surgery-related problems and total surgery time?
3. 3.AI group: During ESD, the AI system will real-time spot and mark bleeding points. Doctors will use these marks to stop bleeding.
4. 4.Control group: Doctors will use the same equipment but without the AI system - they will find and stop bleeding using their own experience.
5. 5.Have ESD surgery for esophageal, stomach, or colorectal lesions that need this treatment;
6. 6.Be randomly assigned to either the AI group or the control group;
7. 7.Attend follow-up checks in 14 days after surgery to check for complications;
8. 8.Have their surgery videos reviewed by experts to record hemostasis time and total surgery time.

Conditions

Endoscopic Submucosal Dissection; Endoscopic Submucosal Dissection (ESD)

Keywords

Endoscopic submucosal dissection; Artificial intelligence

Outcome Measures

Primary Outcomes (1)

• Average single hemostasis time

  Periprocedural

Secondary Outcomes (3)

• Incidence of postoperative complications

  14 days post-procedure

• Total operation duration

  Periprocedural

• Psychological stress experienced by the endoscopist

  Periprocedural

Study Arms (2)

AI-assisted Group

EXPERIMENTAL

During the endoscopic submucosal dissection (ESD) procedure, an artificial intelligence (AI) real-time bleeding point recognition system is utilized. The system dynamically analyzes endoscopic images to identify and mark bleeding sites in real time. Endoscopists perform hemostatic operations promptly based on these AI-generated markers.

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

Conventional treatment group

NO INTERVENTION

Patients undergo ESD using the same hardware platform, but the AI system is deactivated. Hemostatic decisions and operations are solely dependent on the endoscopists' clinical experience-they independently judge the location of bleeding points and perform hemostasis without AI assistance.

Interventions

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

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.

AI-assisted Group

Eligibility Criteria

• Age: 18 Years - 80 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Aged 18-80 years;
• Lesions meet the indications for ESD treatment of the esophagus, stomach, or colorectum according to relevant guidelines;
• Anticoagulant drugs have been suspended according to relevant guidelines;
• Patients with American Society of Anesthesiologists (ASA) classification Grade I or II;
• Patients who voluntarily sign the informed consent form.

You may not qualify if:

• Patients with severe cardiopulmonary diseases, coagulation dysfunction or other severe comorbidities that may increase surgical risks;
• Patients undergoing dialysis treatment;
• Pregnant or lactating women;
• Deemed unsuitable for participation in this study by the principal investigator or other researchers.

Sponsors & Collaborators

• Qilu Hospital of Shandong University: lead
• Qianfoshan Hospital: collaborator
• Shandong Second Provincial General Hospital: collaborator

MeSH Terms

Interventions

Endoscopic Mucosal Resection

Intervention Hierarchy (Ancestors)

Endoscopy, Gastrointestinal; Endoscopy, Digestive System; Diagnostic Techniques, Digestive System; Diagnostic Techniques and Procedures; Diagnosis; Endoscopy; Diagnostic Techniques, Surgical; Digestive System Surgical Procedures; Surgical Procedures, Operative; Minimally Invasive Surgical Procedures

Central Study Contacts

Zhen Li

CONTACT

+86-18560086106; qilulizhen@sdu.edu.cn

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: TRIPLE
• Who Masked: PARTICIPANT, INVESTIGATOR, OUTCOMES ASSESSOR
• Purpose: TREATMENT
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: February 6, 2026
• First Posted: March 27, 2026
• Study Start: March 31, 2026
• Primary Completion (Estimated): June 30, 2027
• Study Completion (Estimated): December 31, 2027
• Last Updated: March 27, 2026

Record last verified: 2026-01

Data Sharing

• IPD Sharing: Will not share