Artificial Intelligence-assisted Colonoscopy on Detection of Missed Proximal Lesions

Study Results

Results pending

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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Recruitment Status

COMPLETED

Clinical Phase

NA

Total Enrollment

216 participants

Study Classification

INTERVENTIONAL

Study Start Date

2021-03-01

Study Completion Date

2022-04-15

Brief Summary

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This is a prospective multi-center randomized study is to determine whether the use of artificial intelligence (AI)-assistance could reduce the miss rates of polyps and adenomas in the proximal colon during tandem examination

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Detailed Description

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Centers

1. Queen Mary Hospital, Hong Kong, China (Co-ordinating Center)
2. Tan Tock Seng Hospital, Singapore, Singapore
3. Institute of Gastroenterology and Hepatology, Vietnam Union of Science and Technology Association, Hanoi, Vietnam

Study population

Inclusion:

All adult patients, aged 40 or above, undergoing outpatient colonoscopy in the participating centers will be recruited.

Exclusion:

* history of inflammatory bowel disease
* history of colorectal cancer
* previous bowel resection (apart from appendectomy)
* Peutz-Jeghers syndrome, familial adenomatous polyposis or other polyposis syndromes
* bleeding tendency or severe comorbid illnesses for which polypectomy is considered unsafe.

Post-randomization exclusion:

* Cecum could not be intubated for various reasons
* Boston Bowel Preparation Scale (BBPS) score of the proximal colon is \<2

Study design This is a prospective randomized trial comparing the miss rates of proximal colonic lesions by AI assisted colonoscopy or conventional colonoscopy (Fig. 1). The study will be conducted in the Endoscopy Centre of the participating hospitals.

Randomization Eligible patients in each center will be randomly allocated in a 1:1 ratio to undergo tandem colonoscopy of the proximal colon first with AI-assistance and follow by conventional white light colonoscopy (Group 1) or conventional white light colonoscopy without AI assistance follow by conventional colonoscopy (Group 2). Proximal colon refers to colonic segment proximal to the splenic flexure. Randomization will be conducted in blocks of 4 by computer generated random sequences and stratified according to indications of colonoscopy (symptomatic vs screening/surveillance). Patients will be blinded to the group assignment.

Conditions

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Colon Adenoma Colon Polyp

Study Design

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Allocation Method

RANDOMIZED

Intervention Model

PARALLEL

Prospective randomized design

Primary Study Purpose

DIAGNOSTIC

Blinding Strategy

SINGLE

Participants

Study Groups

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Artificial intelligence-Assisted colonoscopy

Tandem colonoscopy of proximal colon assisted with artificial intelligence followed by conventional colonoscopy

Group Type EXPERIMENTAL

Artificial intelligence-Assisted colonoscopy

Intervention Type DEVICE

Artificial intelligence-Assisted colonoscopy for detection of colonic polyp

Conventional colonoscopy

Intervention Type PROCEDURE

Conventional colonoscopy

Tandem conventional colonoscopy of proximal colon followed by usual conventional colonoscopy

Group Type ACTIVE_COMPARATOR

Conventional colonoscopy

Intervention Type PROCEDURE

Conventional colonoscopy

Interventions

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Artificial intelligence-Assisted colonoscopy

Artificial intelligence-Assisted colonoscopy for detection of colonic polyp

Intervention Type DEVICE

Conventional colonoscopy

Intervention Type PROCEDURE

Eligibility Criteria

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Inclusion Criteria

* All adult patients, aged 40 or above, undergoing outpatient colonoscopy in the participating centers will be recruited

Exclusion Criteria

* history of inflammatory bowel disease
* history of colorectal cancer
* previous bowel resection (apart from appendectomy)
* Peutz-Jeghers syndrome, familial adenomatous polyposis or other polyposis syndromes
* bleeding tendency or severe comorbid illnesses for which polypectomy is considered unsafe.

Minimum Eligible Age

40 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes