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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| 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. |
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| 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. |
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| 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. |
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| 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. |
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| 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. |
| Measure | Description | Time Frame |
|---|---|---|
| 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 |
| Measure | Description | Time Frame |
|---|---|---|
| Polyp miss rate | Compare polyp miss rate (PMR) in each groups | Untill the end of the procedure |
| Adenoma detection rate of colonoscopy underwent by the trainee | Compare adenoma detection rate (ADR) of colonoscopy underwent by trainee with or without AI |
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Inclusion Criteria:
Exclusion Criteria:
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Division of Gastroenterology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand | Bangkok | Bangkok | Thailand |
The investigators are willing to provide our data to researchers who require it. For example, those who want to do systematic review and meta-analysis.
Other researchers can contact us anytime
The investigators will provide our protocol and/or data upon request.
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| 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%. |
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| 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%. |
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| 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%. |
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| Untill the end of procedure of first pass which will be done by trainee |