Artificial Intelligence-assisted Colonoscopy, Tandem Study
NCT ID: NCT07023471
Last Updated: 2025-06-24
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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ENROLLING_BY_INVITATION
NA
364 participants
INTERVENTIONAL
2025-05-13
2026-12-31
Brief Summary
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• 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.
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Detailed Description
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Conditions
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Study Design
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RANDOMIZED
PARALLEL
DIAGNOSTIC
TRIPLE
Study Groups
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Group A (Trainee --> expert)
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.
Group A (Trainee --> expert)
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 B (Trainee +AI --> expert)
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.
Group B (Trainee +AI --> expert)
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 C (Trainee --> expert + AI)
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.
Group C (Trainee --> expert + AI)
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 D (Trainee + AI --> expert + AI)
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.
Group D (Trainee + AI --> expert + AI)
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%.
Interventions
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Group A (Trainee --> expert)
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 B (Trainee +AI --> expert)
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 C (Trainee --> expert + AI)
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 D (Trainee + AI --> expert + AI)
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%.
Eligibility Criteria
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Inclusion Criteria
* Appointment for colonoscopy for colorectal cancer screening
Exclusion Criteria
* 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
40 Years
85 Years
ALL
No
Sponsors
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Mahidol University
OTHER
Responsible Party
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Nonthalee Pausawasdi
Associate Professor
Locations
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Division of Gastroenterology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
Bangkok, Bangkok, Thailand
Countries
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Other Identifiers
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Si299/2025
Identifier Type: -
Identifier Source: org_study_id
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