NCT04294355

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

Trial Health

90
On Track

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Enrollment
216

participants targeted

Target at P75+ for not_applicable

Timeline
Completed

Started Mar 2021

Geographic Reach
3 countries

3 active sites

Status
completed

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

First Submitted

Initial submission to the registry

March 2, 2020

Completed
2 days until next milestone

First Posted

Study publicly available on registry

March 4, 2020

Completed
12 months until next milestone

Study Start

First participant enrolled

March 1, 2021

Completed
1.1 years until next milestone

Primary Completion

Last participant's last visit for primary outcome

March 31, 2022

Completed
15 days until next milestone

Study Completion

Last participant's last visit for all outcomes

April 15, 2022

Completed
Last Updated

April 21, 2022

Status Verified

April 1, 2022

Enrollment Period

1.1 years

First QC Date

March 2, 2020

Last Update Submit

April 20, 2022

Conditions

Colon AdenomaColon Polyp

Keywords

ColonoscopyArtificial intelligence

Outcome Measures

Primary Outcomes (1)

  • Proximal adenoma missed rate

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

    One day

Secondary Outcomes (3)

  • Proximal polyp missed rate

    One day

  • Proximal adenoma detection rate

    One day

  • Proximal polyp detection

    One day

Study Arms (2)

Artificial intelligence-Assisted colonoscopy

EXPERIMENTAL

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

Device: Artificial intelligence-Assisted colonoscopyProcedure: Conventional colonoscopy

Conventional colonoscopy

ACTIVE COMPARATOR

Tandem conventional colonoscopy of proximal colon followed by usual conventional colonoscopy

Procedure: Conventional colonoscopy

Interventions

Artificial intelligence-Assisted colonoscopyDEVICE

Artificial intelligence-Assisted colonoscopy for detection of colonic polyp

Artificial intelligence-Assisted colonoscopy
Conventional colonoscopyPROCEDURE

Conventional colonoscopy

Artificial intelligence-Assisted colonoscopyConventional colonoscopy

Eligibility Criteria

Age40 Years+
Sexall
Healthy VolunteersYes
Age GroupsAdult (18-64), Older Adult (65+)

You may qualify if:

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

You may not qualify if:

  • 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.

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (3)

Queen Mary Hospital

Hong Kong, China

Location

Tan Tock Seng Hospital

Singapore, Singapore

Location

Institute of Gastroenterology and Hepatology

Hanoi, 0, Vietnam

Location

Related Publications (1)

  • Lui TKL, Hang DV, Tsao SKK, Hui CKY, Mak LLY, Ko MKL, Cheung KS, Thian MY, Liang R, Tsui VWM, Yeung CK, Dao LV, Leung WK. Computer-assisted detection versus conventional colonoscopy for proximal colonic lesions: a multicenter, randomized, tandem-colonoscopy study. Gastrointest Endosc. 2023 Feb;97(2):325-334.e1. doi: 10.1016/j.gie.2022.09.020. Epub 2022 Oct 5.

    PMID: 36208795DERIVED

MeSH Terms

Conditions

Colonic Polyps

Condition Hierarchy (Ancestors)

Intestinal PolypsPolypsPathological Conditions, AnatomicalPathological Conditions, Signs and Symptoms

Study Officials

  • Ka Luen, Thomas Lui, MBBS

    Queen Mary Hospital, the University of Hong Kong

    PRINCIPAL INVESTIGATOR

  • Wai Keung Leung, MD

    Queen Mary Hospital, the University of Hong Kong

    STUDY DIRECTOR

Study Design

Study Type
interventional
Phase
not applicable
Allocation
RANDOMIZED
Masking
SINGLE
Who Masked
PARTICIPANT
Purpose
DIAGNOSTIC
Intervention Model
PARALLEL
Model Details: Prospective randomized design
Sponsor Type
OTHER
Responsible Party
SPONSOR

Study Record Dates

First Submitted

March 2, 2020

First Posted

March 4, 2020

Study Start

March 1, 2021

Primary Completion

March 31, 2022

Study Completion

April 15, 2022

Last Updated

April 21, 2022

Record last verified: 2022-04

Data Sharing

IPD Sharing
Will not share

Locations

SG(1)CN(1)VN(1)