Impact of Automatic Polyp Detection System on Adenoma Detection Rate

Study Overview
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Brief Summary
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In recent years, with the continuous development of artificial intelligence, automatic polyp detection systems have shown its potential in increasing the colorectal lesions. Yet, whether this system can increase polyp and adenoma detection rates in the real clinical setting is still need to be proved. The primary objective of this study is to examine whether a combination of colonoscopy and a deep learning-based automatic polyp detection system is a feasible way to increase adenoma detection rate compared to standard colonoscopy.

Official Title
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Impact of Automatic Polyp Detection System on Adenoma Detection Rate-a Multicenter,Prospective, Randomized Controlled Trial

Conditions
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Colonic Polyps, Colorectal Adenomas

Intervention / Treatment
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* Device: Automatic polyp detection system


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Patients aged between 40-85 years old who have indications for screening, surveillance and diagnostic. Patients who have signed inform consent form. Exclusion Criteria: Patients who have undergone colonic resection Patients with intracranial and/or central nervous system disease, including cerebral infarction and cerebral hemorrhage. Patients with severe chronic cardiopulmonary and renal disease. Patients who are unwilling or unable to consent. Patients who are not suitable for colonoscopy Patients who received urgent or therapeutic colonoscopy Patients with pregnancy, inflammatory bowel disease, polyposis of colon, colorectal cancer, or intestinal obstruction Patients who are taking aspirin, clopidogrel or other anticoagulants Patients with withdrawal time < 6 min

Ages Eligible for Study
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Minimum Age: 40 Years
Maximum Age: 85 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Diagnostic
Allocation: Randomized
Intervention Model: Parallel Assignment
Masking: None (Open Label)


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: AI-assisted withdrawal group<br>A deep learning-based automatic polyp detection system was used to assist the endoscopist. | Device: Automatic polyp detection system<br>* When colonoscopists withdraw the colonoscopies and inspect the colons, the video streaming of colonoscopies was real-time switched to the automatic polyp detection system, which made it feasible to detect lesions in real time. When any potential polyp is detected by the system， there will be a tracing box on an adjacent monitor to locate the lesion with a simultaneous sound alarm.<br>|
| No Intervention: Routine withdrawal group<br>Routine withdrawal without any assist. | |

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| adenoma detection rate(ADR) | the number of patients with at least one adenoma divided by the total number of patients. | 30 minutes | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| polyp detection rate(PDR) | the number of patients with at least one polyp divided by the total number of patients. | 30 minutes | 
| adenoma per colonoscopy | the number of adenomas detected during colonoscopy withdraw divided by the number of colonoscopies. | 30 minutes | 
| polyp per colonoscopy | the number of polyps detected during colonoscopy withdraw divided by the number of colonoscopies. | 30 minutes |