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Artificial Intelligence-assisted Colonoscopy on Detection of Missed Proximal Lesions

Primary Purpose

Colon Adenoma, Colon Polyp

Status
Completed
Phase
Not Applicable
Locations
International
Study Type
Interventional
Intervention
Artificial intelligence-Assisted colonoscopy
Conventional colonoscopy
Sponsored by
The University of Hong Kong
About
Eligibility
Locations
Arms
Outcomes
Full info

About this trial

This is an interventional diagnostic trial for Colon Adenoma focused on measuring Colonoscopy, Artificial intelligence

Eligibility Criteria

40 Years - undefined (Adult, Older Adult)All SexesAccepts Healthy Volunteers

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.

Sites / Locations

  • Queen Mary Hospital
  • Tan Tock Seng Hospital
  • Institute of Gastroenterology and Hepatology

Arms of the Study

Arm 1

Arm 2

Arm Type

Experimental

Active Comparator

Arm Label

Artificial intelligence-Assisted colonoscopy

Conventional colonoscopy

Arm Description

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

Tandem conventional colonoscopy of proximal colon followed by usual conventional colonoscopy

Outcomes

Primary Outcome Measures

Proximal adenoma missed rate
The proportion of patients with missed adenomas detected in the second examination only

Secondary Outcome Measures

Proximal polyp missed rate
The proportion of patients with missed adenomas detected in the second examination only
Proximal adenoma detection rate
The proportion of patients with at least one adenoma
Proximal polyp detection
The proportion of patients with at least one polyp

Full Information

First Posted
March 2, 2020
Last Updated
April 20, 2022
Sponsor
The University of Hong Kong
Collaborators
Tan Tock Seng Hospital, Institute of Gastroenterology and Hepatology, Vietnam
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1. Study Identification

Unique Protocol Identification Number
NCT04294355
Brief Title
Artificial Intelligence-assisted Colonoscopy on Detection of Missed Proximal Lesions
Official Title
Artificial Intelligence-assisted Colonoscopy Versus Conventional Colonoscopy for Missed Lesions in the Proximal Colon: A Prospective Multi-center Randomized Study in Asia
Study Type
Interventional

2. Study Status

Record Verification Date
April 2022
Overall Recruitment Status
Completed
Study Start Date
March 1, 2021 (Actual)
Primary Completion Date
March 31, 2022 (Actual)
Study Completion Date
April 15, 2022 (Actual)

3. Sponsor/Collaborators

Responsible Party, by Official Title
Sponsor
Name of the Sponsor
The University of Hong Kong
Collaborators
Tan Tock Seng Hospital, Institute of Gastroenterology and Hepatology, Vietnam

4. Oversight

Studies a U.S. FDA-regulated Drug Product
No
Studies a U.S. FDA-regulated Device Product
No
Data Monitoring Committee
No

5. Study Description

Brief Summary
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
Detailed Description
Centers Queen Mary Hospital, Hong Kong, China (Co-ordinating Center) Tan Tock Seng Hospital, Singapore, Singapore 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.

6. Conditions and Keywords

Primary Disease or Condition Being Studied in the Trial, or the Focus of the Study
Colon Adenoma, Colon Polyp
Keywords
Colonoscopy, Artificial intelligence

7. Study Design

Primary Purpose
Diagnostic
Study Phase
Not Applicable
Interventional Study Model
Parallel Assignment
Model Description
Prospective randomized design
Masking
Participant
Allocation
Randomized
Enrollment
216 (Actual)

8. Arms, Groups, and Interventions

Arm Title
Artificial intelligence-Assisted colonoscopy
Arm Type
Experimental
Arm Description
Tandem colonoscopy of proximal colon assisted with artificial intelligence followed by conventional colonoscopy
Arm Title
Conventional colonoscopy
Arm Type
Active Comparator
Arm Description
Tandem conventional colonoscopy of proximal colon followed by usual conventional colonoscopy
Intervention Type
Device
Intervention Name(s)
Artificial intelligence-Assisted colonoscopy
Intervention Description
Artificial intelligence-Assisted colonoscopy for detection of colonic polyp
Intervention Type
Procedure
Intervention Name(s)
Conventional colonoscopy
Intervention Description
Conventional colonoscopy
Primary Outcome Measure Information:
Title
Proximal adenoma missed rate
Description
The proportion of patients with missed adenomas detected in the second examination only
Time Frame
One day
Secondary Outcome Measure Information:
Title
Proximal polyp missed rate
Description
The proportion of patients with missed adenomas detected in the second examination only
Time Frame
One day
Title
Proximal adenoma detection rate
Description
The proportion of patients with at least one adenoma
Time Frame
One day
Title
Proximal polyp detection
Description
The proportion of patients with at least one polyp
Time Frame
One day

10. Eligibility

Sex
All
Minimum Age & Unit of Time
40 Years
Accepts Healthy Volunteers
Accepts Healthy Volunteers
Eligibility Criteria
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.
Overall Study Officials:
First Name & Middle Initial & Last Name & Degree
Ka Luen, Thomas Lui, MBBS
Organizational Affiliation
Queen Mary Hospital, the University of Hong Kong
Official's Role
Principal Investigator
First Name & Middle Initial & Last Name & Degree
Wai Keung Leung, MD
Organizational Affiliation
Queen Mary Hospital, the University of Hong Kong
Official's Role
Study Director
Facility Information:
Facility Name
Queen Mary Hospital
City
Hong Kong
Country
China
Facility Name
Tan Tock Seng Hospital
City
Singapore
Country
Singapore
Facility Name
Institute of Gastroenterology and Hepatology
City
Hanoi
ZIP/Postal Code
0
Country
Vietnam

12. IPD Sharing Statement

Plan to Share IPD
No

Learn more about this trial

Artificial Intelligence-assisted Colonoscopy on Detection of Missed Proximal Lesions

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