NCT03967756

Brief Summary

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.

Trial Health

43
At Risk

Trial Health Score

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

Trial has exceeded expected completion date
Enrollment
1,118

participants targeted

Target at P75+ for not_applicable

Timeline
Completed

Started Jun 2019

Typical duration for not_applicable

Geographic Reach
1 country

1 active site

Status
unknown

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

May 28, 2019

Completed
2 days until next milestone

First Posted

Study publicly available on registry

May 30, 2019

Completed
2 days until next milestone

Study Start

First participant enrolled

June 1, 2019

Completed
2.1 years until next milestone

Primary Completion

Last participant's last visit for primary outcome

July 20, 2021

Completed
2 months until next milestone

Study Completion

Last participant's last visit for all outcomes

October 1, 2021

Completed
Last Updated

April 6, 2021

Status Verified

April 1, 2021

Enrollment Period

2.1 years

First QC Date

May 28, 2019

Last Update Submit

April 3, 2021

Conditions

Colonic PolypsColorectal Adenomas

Outcome Measures

Primary Outcomes (1)

  • adenoma detection rate(ADR)

    the number of patients with at least one adenoma divided by the total number of patients.

    30 minutes

Secondary Outcomes (3)

  • polyp detection rate(PDR)

    30 minutes

  • adenoma per colonoscopy

    30 minutes

  • polyp per colonoscopy

    30 minutes

Study Arms (2)

AI-assisted withdrawal group

EXPERIMENTAL

A deep learning-based automatic polyp detection system was used to assist the endoscopist.

Device: Automatic polyp detection system

Routine withdrawal group

NO INTERVENTION

Routine withdrawal without any assist.

Interventions

Automatic polyp detection systemDEVICE

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.

AI-assisted withdrawal group

Eligibility Criteria

Age40 Years - 85 Years
Sexall
Healthy VolunteersNo
Age GroupsAdult (18-64), Older Adult (65+)

You may qualify if:

  • Patients aged between 40-85 years old who have indications for screening, surveillance and diagnostic.
  • Patients who have signed inform consent form.

You may not qualify if:

  • 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

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (1)

Changhai Hospital, Second Military Medical University

Shanghai, 200433, China

RECRUITING

Related Publications (2)

  • Urban G, Tripathi P, Alkayali T, Mittal M, Jalali F, Karnes W, Baldi P. Deep Learning Localizes and Identifies Polyps in Real Time With 96% Accuracy in Screening Colonoscopy. Gastroenterology. 2018 Oct;155(4):1069-1078.e8. doi: 10.1053/j.gastro.2018.06.037. Epub 2018 Jun 18.

    PMID: 29928897BACKGROUND

  • Ahmad OF, Soares AS, Mazomenos E, Brandao P, Vega R, Seward E, Stoyanov D, Chand M, Lovat LB. Artificial intelligence and computer-aided diagnosis in colonoscopy: current evidence and future directions. Lancet Gastroenterol Hepatol. 2019 Jan;4(1):71-80. doi: 10.1016/S2468-1253(18)30282-6. Epub 2018 Dec 6.

    PMID: 30527583BACKGROUND

MeSH Terms

Conditions

Colonic Polyps

Condition Hierarchy (Ancestors)

Intestinal PolypsPolypsPathological Conditions, AnatomicalPathological Conditions, Signs and Symptoms

Study Officials

  • Zhaoshen Li, M.D

    Changhai Hospital

    PRINCIPAL INVESTIGATOR

Central Study Contacts

Zhaoshen Li, M.D

CONTACT

86-21-31161365li.zhaoshen@hotmail.com

Yu Bai, M.D

CONTACT

86-21-31161335baiyu1998@hotmail.com

Study Design

Study Type
interventional
Phase
not applicable
Allocation
RANDOMIZED
Masking
NONE
Purpose
DIAGNOSTIC
Intervention Model
PARALLEL
Sponsor Type
OTHER
Responsible Party
PRINCIPAL INVESTIGATOR
PI Title
Director of Gastroenterology Dept

Study Record Dates

First Submitted

May 28, 2019

First Posted

May 30, 2019

Study Start

June 1, 2019

Primary Completion

July 20, 2021

Study Completion

October 1, 2021

Last Updated

April 6, 2021

Record last verified: 2021-04

Data Sharing

IPD Sharing
Will not share

Locations

CN(1)