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

N/ANot Yet RecruitingNCT07495137

Qilu Hospital of Shandong University160 enrolled

Overview

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.

Study Type

INTERVENTIONAL

Allocation

RANDOMIZED

Purpose

TREATMENT

Masking

TRIPLE

Enrollment

160

Conditions

Endoscopic Submucosal Dissection

Interventions

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

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.

Eligibility

Sex: ALLMin age: 18 YearsMax age: 80 Years

Medical Language ↔ Plain English

Inclusion Criteria 1. Aged 18-80 years; 2. Lesions meet the indications for ESD treatment of the esophagus, stomach, or colorectum according to relevant guidelines; 3. Anticoagulant drugs have been suspended according to relevant guidelines; 4. Patients with American Society of Anesthesiologists (ASA) classification Grade I or II; 5. Patients who voluntarily sign the informed consent form. Exclusion Criteria 1. Patients with severe cardiopulmonary diseases, coagulation dysfunction or other severe comorbidities that may increase surgical risks; 2. Patients undergoing dialysis treatment; 3. Pregnant or lactating women; 4. Deemed unsuitable for participation in this study by the principal investigator or other researchers.

Outcomes

Primary Outcomes

Average single hemostasis time

Time frame: Periprocedural

Secondary Outcomes

Incidence of postoperative complications

Complications include intraoperative and postoperative perforation, damage to the muscularis propria, etc

Time frame: 14 days post-procedure

Total operation duration

Time frame: Periprocedural

Psychological stress experienced by the endoscopist

Be evaluated subjectively on a five point scale

Time frame: Periprocedural

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

Overview

Conditions

Interventions

Eligibility

Outcomes

Primary Outcomes

Secondary Outcomes

Central Contacts