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Enrolling by Invitation

NCT Number: NCT07023471

Artificial Intelligence-assisted Colonoscopy, Tandem Study

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.

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Key information

Conditions

Artificial Intelligence (AI) Adenoma Colon Polyp Colonoscopy Education

Age range

40 year–85 year

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

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

Bangkok, Thailand

About this study

Colon cancer accounts for one of the most common cancer worldwide and also cancer-related death. Colonoscopy is accepted to be an effective tool in colon cancer screening since the polypectomy of small adenoma can prevent colon cancer. Missed adenoma is one of the causes of interval cancer between routine colonoscopy screening. Nonvisualization is the cause of missed adenoma during colonoscopy. Artificial intelligence (AI)-assisted colonoscopy was superior then routine colonoscopy from parallel study and tandem study. Previous studies often used one same endoscopist in doing tandem colonoscopy which may still have bias. Only one previous study designed to use trainee endoscopist in the first pass and expert endoscopist in the second pass, some subgroups used AI-assisted. The result revealed the lower of adenoma miss rate (AMR) in AI-assisted colonoscopy in the first pass. This study designed to evaluate AMR of AI-assisted colonoscopy in trainee endoscopist compared to expert endoscopist, the trainee will do colonoscopy in the first pass (with or without AI) and the expert will do colonoscopy in the second pass (with or without AI). The present study aimed to evaluate effect of AI-assisted colonoscopy in trainee endoscopist.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

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

Exclusion criteria

  • 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

Treatment and study plan

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 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 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 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%.

Primary outcomes

  1. Adenoma miss rate

    Time frame: Untill the end of procedure

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

Secondary outcomes

  1. Polyp miss rate

    Time frame: Untill the end of the procedure

    Compare polyp miss rate (PMR) in each groups

  2. Adenoma detection rate of colonoscopy underwent by the trainee

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

    Compare adenoma detection rate (ADR) of colonoscopy underwent by trainee with or without AI

Sponsors and collaborators

Lead sponsor

Mahidol University

Other

Registry information

Official study title

Effect of an Artificial Intelligence-assisted Colonoscopy on Adenoma Miss Rate of Trainee-performed Colonoscopy: A Four-group Randomized Controlled Tandem Colonoscopy Study

Important dates

Study start
2025
Primary completion
2025
Study completion
2026
First posted
Jun 17, 2025
Registry last updated
Jun 24, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

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This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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