Impact of Real-time Automatic Quality Control System on Colorectal Adenoma Detection

Study Overview
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Brief Summary
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The purpose of this study was to assess the performance of automatic quality-control system (AQCS) in real-time quality control of colonoscopy.

Detailed Description
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We carried a multicenter randomised controlled trial to assess the performance of this system in quality control of real-time colonoscopy, including the adenoma detection rate (ADR), withdraw time, adequate bowel preparation rate and other indicators.

Official Title
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Impact of Real-time Automatic Quality Control System on Colorectal Adenoma Detection: a Multicenter Randomized Controlled Trial

Conditions
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Colonoscopy

Intervention / Treatment
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* Device: Computer-aided Real-time Automatic Quality Control System (AQCS)


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Consecutive patients (aged 18-80 years) scheduled for colonoscopy examination. Exclusion Criteria: Patients with a history of inflammatory bowel disease, advanced colorectal cancer, polyposis syndromes or known colorectal polyps without complete removal previously. Patients with a history of colorectal surgery. Patients with a contraindication for biopsy. Patients with prior failed colonoscopy. Patients with known stenosis or obstruction. Patients in pregnancy or lactation phase. Patients refused to participate in the trial.

Ages Eligible for Study
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Minimum Age: 18 Years
Maximum Age: 80 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: Other
Allocation: Randomized
Intervention Model: Parallel Assignment
Masking: Single


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: AQCS-aided group<br>Patients in AQCS-aided group will go through colonoscopy examination with the assitance of AQCS. | Device: Computer-aided Real-time Automatic Quality Control System (AQCS)<br>* Automatic quality-control system(AQCS), developed based on deep convolutional neural network (DCNN) models, could improve the colonoscopists' performance during withdrawal phase and significantly increase polyp and adenoma detection.<br>|
| No Intervention: Control group<br>Patients in control group will go through conventional standard colonoscopy examination without the assistance of the AQCS. | |

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Colorectal adenoma detection rate (ADR) | The primary endpoint is the adenoma detection rate (ADR), which is the proportion of patients having one or more adenomas detected by colonoscopy. | 10 months | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Colorectal polyp detection rate (PDR) | The polyp detection rate (PDR) is the proportion of patients having one or more polyps detected by colonoscopy. | 10 months | 
| The mean number of adenomas detected per colonoscopy (APC) | The mean number of adenomas detected per colonoscopy in AQCS and control groups | 10 months | 
| The mean number of polyps detected per colonoscopy (PPC) | The mean number of polyps detected per colonoscopy in AQCS and control groups | 10months | 
| Withdrawl time | Withdrawl time is defined as the time taken to remove the colonoscope from the cecal pole minus the time of therapeutic interventions or biopsy, until the complete withdrawal of the colonoscope. | During procedure | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Artificial Intelligence, Colonoscopy, Quality Control