Artificial Intelligence-assisted Colonoscopy, Tandem Study

NCT07023471 | Enrolling By Invitation DMC

• 1 other identifier: Si299/2025
• Study Type: interventional
• Target: 364
• Locations: 1 country
• Sites: 1

Brief Summary

The goal of this clinical trial is to evaluate effect of artifial intelligent (AI) system, Endoscopy as AI-powered Device (ENAD) on adenoma miss rate from colonoscopy underwent by trainee endoscopist. It will also evaluate effect of AI on adenoma and polyp detection rate from colonoscopy underwent by trainee endoscopist. The main questions it aims to answer are: • Does AI-system lower adenoma miss rate in colonoscopy underwent by trainee endoscopist? Researchers will do the tandem colonoscopy and devided the participant in 4 groups as follows: A. First pass: trainee; Second pass: expert B. First pass: trainee + AI; Second pass: expert C. First pass: trainee; Second pass: expert + AI D. First pass: trainee+AI; Second pass: expert+AI Participants will take bowel preparation in split dose regimen and nothing per oral for 4 hours. They will underwent colonoscopy as above, with sedation by anesthesiologist. Details on qualities of colonoscopy, polyps detection and pathology results will be recorded.

Conditions

Artificial Intelligence (AI); Colonoscopy Education; Adenoma Colon Polyp

Keywords

Adenoma miss rate; Artificial intelligence; colonoscopy; tandem colonoscopy

Outcome Measures

Primary Outcomes (1)

• Adenoma miss rate

  Compare adenoma miss rate (AMR) in each groups including Non-AI/ Non-AI, Non-AI/ AI, AI/ Non-AI, and AI/ AI

  Untill the end of procedure

Secondary Outcomes (2)

• Polyp miss rate

  Untill the end of the procedure

• Adenoma detection rate of colonoscopy underwent by the trainee

  Untill the end of procedure of first pass which will be done by trainee

Study Arms (4)

Group A (Trainee --> expert)

PLACEBO COMPARATOR

The expert endoscopist insert to cecum in both passes. First pass: Trainee withdraw colonoscopy without AI. The polyps detected can be removed as suitable. The pathology for polyp will be sent. Second pass: Expert withdraw colonoscopy without AI. The polyps detected (which is missed from the first pass) can be removed as suitable. The pathology for polyp will be sent.

Device: Group A (Trainee --> expert)

Group B (Trainee +AI --> expert)

OTHER

The expert endoscopist insert to cecum in both passes. First pass: Trainee withdraw colonoscopy with AI. The polyps detected can be removed as suitable. The pathology for polyp will be sent. Second pass: Expert withdraw colonoscopy without AI. The polyps detected (which is missed from the first pass) can be removed as suitable. The pathology for polyp will be sent.

Device: Group B (Trainee +AI --> expert)

Group C (Trainee --> expert + AI)

OTHER

The expert endoscopist insert to cecum in both passes. First pass: Trainee withdraw colonoscopy without AI. The polyps detected can be removed as suitable. The pathology for polyp will be sent. Second pass: Expert withdraw colonoscopy with AI. The polyps detected (which is missed from the first pass) can be removed as suitable. The pathology for polyp will be sent.

Device: Group C (Trainee --> expert + AI)

Group D (Trainee + AI --> expert + AI)

OTHER

The expert endoscopist insert to cecum in both passes. First pass: Trainee withdraw colonoscopy with AI. The polyps detected can be removed as suitable. The pathology for polyp will be sent. Second pass: Expert withdraw colonoscopy with AI. The polyps detected (which is missed from the first pass) can be removed as suitable. The pathology for polyp will be sent.

Device: Group D (Trainee + AI --> expert + AI)

Interventions

Group A (Trainee --> expert) DEVICE

The expert endoscopist insert to cecum in both passes. First pass: Trainee withdraw colonoscopy (white-light mode) without AI. The polyps detected can be removed as suitable. The pathology for polyp will be sent. Second pass: Expert withdraw colonoscopy (white-light mode) without AI. The polyps detected (which is missed from the first pass) can be removed as suitable. The pathology for polyp will be sent.

Group A (Trainee --> expert)

Group B (Trainee +AI --> expert) DEVICE

The expert endoscopist insert to cecum in both passes. First pass: Trainee withdraw colonoscopy with AI. The polyps detected can be removed as suitable. The pathology for polyp will be sent. Second pass: Expert withdraw colonoscopy (white-light mode) without AI. The polyps detected (which is missed from the first pass) can be removed as suitable. The pathology for polyp will be sent. Artificial intelligent (AI) assisted colonoscopy; ENdoscopy as AI-powered Device (ENAD) ENAD system (ENdoscopy as AI-powered Device, AINEX Corporation, Seoul, South Korea) is the system using CADe system (Computer-aided detection) which developed from 66,397 images and 8,756 polyps via deep learning-based object detection algorithm (YOLOv4) . It was validated by 15,753 images of polyp from 80 colonoscopy videos and 90,144 images of non-polyps from 50 colonoscopy videos. This system decreases false positive rate from 3.2% to 0.6% and increases sensitivity from 86.4% to 87.1%.

Group B (Trainee +AI --> expert)

Group C (Trainee --> expert + AI) DEVICE

The expert endoscopist insert to cecum in both passes. First pass: Trainee withdraw colonoscopy (white-light mode) without AI. The polyps detected can be removed as suitable. The pathology for polyp will be sent. Second pass: Expert withdraw colonoscopy with AI. The polyps detected (which is missed from the first pass) can be removed as suitable. The pathology for polyp will be sent. Artificial intelligent (AI) assisted colonoscopy; ENdoscopy as AI-powered Device (ENAD) ENAD system (ENdoscopy as AI-powered Device, AINEX Corporation, Seoul, South Korea) is the system using CADe system (Computer-aided detection) which developed from 66,397 images and 8,756 polyps via deep learning-based object detection algorithm (YOLOv4) . It was validated by 15,753 images of polyp from 80 colonoscopy videos and 90,144 images of non-polyps from 50 colonoscopy videos. This system decreases false positive rate from 3.2% to 0.6% and increases sensitivity from 86.4% to 87.1%.

Group C (Trainee --> expert + AI)

Group D (Trainee + AI --> expert + AI) DEVICE

The expert endoscopist insert to cecum in both passes. First pass: Trainee withdraw colonoscopy with AI. The polyps detected can be removed as suitable. The pathology for polyp will be sent. Second pass: Expert withdraw colonoscopy with AI. The polyps detected (which is missed from the first pass) can be removed as suitable. The pathology for polyp will be sent. Artificial intelligent (AI) assisted colonoscopy; ENdoscopy as AI-powered Device (ENAD) ENAD system (ENdoscopy as AI-powered Device, AINEX Corporation, Seoul, South Korea) is the system using CADe system (Computer-aided detection) which developed from 66,397 images and 8,756 polyps via deep learning-based object detection algorithm (YOLOv4) . It was validated by 15,753 images of polyp from 80 colonoscopy videos and 90,144 images of non-polyps from 50 colonoscopy videos. This system decreases false positive rate from 3.2% to 0.6% and increases sensitivity from 86.4% to 87.1%.

Group D (Trainee + AI --> expert + AI)

Eligibility Criteria

• Age: 40 Years - 85 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Age 40 - 85 years old
• Appointment for colonoscopy for colorectal cancer screening

You may not qualify if:

• Previous history of bowel obstruction or perforation
• Presence of coagulopathy (Prothrombin time \>, = 3 second ULN; Platelet \< 50,000)
• Previously diagnosed with inflammatory bowel disease or polyposis syndrome
• Pregnancy or lactation
• Severe comorbities or American Society of Anesthesiologist classification \>, = 3
• Unable to sign informed consent

Sponsors & Collaborators

• Mahidol University: lead

Study Sites (1)

Division of Gastroenterology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand

Bangkok, Bangkok, Thailand

Location

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: TRIPLE
• Who Masked: PARTICIPANT, INVESTIGATOR, OUTCOMES ASSESSOR
• Purpose: DIAGNOSTIC
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Associate Professor

Study Record Dates

• First Submitted: June 7, 2025
• First Posted: June 17, 2025
• Study Start: May 13, 2025
• Primary Completion: May 13, 2025
• Study Completion (Estimated): December 31, 2026
• Last Updated: June 24, 2025

Record last verified: 2025-06

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL
• Time Frame: Other researchers can contact us anytime
• Access Criteria: The investigators will provide our protocol and/or data upon request.

Locations

TH (1)