search
Back to results

Impact of Automatic Polyp Detection System on Adenoma Detection Rate

Primary Purpose

Colonic Polyps, Colorectal Adenomas

Status
Unknown status
Phase
Not Applicable
Locations
China
Study Type
Interventional
Intervention
Automatic polyp detection system
Sponsored by
Changhai Hospital
About
Eligibility
Locations
Arms
Outcomes
Full info

About this trial

This is an interventional diagnostic trial for Colonic Polyps

Eligibility Criteria

40 Years - 85 Years (Adult, Older Adult)All SexesDoes not accept healthy volunteers

Inclusion Criteria:

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

Exclusion Criteria:

  • 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

Sites / Locations

  • Changhai Hospital, Second Military Medical UniversityRecruiting

Arms of the Study

Arm 1

Arm 2

Arm Type

Experimental

No Intervention

Arm Label

AI-assisted withdrawal group

Routine withdrawal group

Arm Description

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

Routine withdrawal without any assist.

Outcomes

Primary Outcome Measures

adenoma detection rate(ADR)
the number of patients with at least one adenoma divided by the total number of patients.

Secondary Outcome Measures

polyp detection rate(PDR)
the number of patients with at least one polyp divided by the total number of patients.
adenoma per colonoscopy
the number of adenomas detected during colonoscopy withdraw divided by the number of colonoscopies.
polyp per colonoscopy
the number of polyps detected during colonoscopy withdraw divided by the number of colonoscopies.

Full Information

First Posted
May 28, 2019
Last Updated
April 3, 2021
Sponsor
Changhai Hospital
Collaborators
The First Affiliated Hospital of Dalian Medical University, Wenzhou Central Hospital, Wuhan Union Hospital, China
search

1. Study Identification

Unique Protocol Identification Number
NCT03967756
Brief Title
Impact of Automatic Polyp Detection System on Adenoma Detection Rate
Official Title
Impact of Automatic Polyp Detection System on Adenoma Detection Rate-a Multicenter,Prospective, Randomized Controlled Trial
Study Type
Interventional

2. Study Status

Record Verification Date
April 2021
Overall Recruitment Status
Unknown status
Study Start Date
June 1, 2019 (Actual)
Primary Completion Date
July 20, 2021 (Anticipated)
Study Completion Date
October 1, 2021 (Anticipated)

3. Sponsor/Collaborators

Responsible Party, by Official Title
Principal Investigator
Name of the Sponsor
Changhai Hospital
Collaborators
The First Affiliated Hospital of Dalian Medical University, Wenzhou Central Hospital, Wuhan Union Hospital, China

4. Oversight

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

5. Study Description

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.

6. Conditions and Keywords

Primary Disease or Condition Being Studied in the Trial, or the Focus of the Study
Colonic Polyps, Colorectal Adenomas

7. Study Design

Primary Purpose
Diagnostic
Study Phase
Not Applicable
Interventional Study Model
Parallel Assignment
Masking
None (Open Label)
Allocation
Randomized
Enrollment
1118 (Anticipated)

8. Arms, Groups, and Interventions

Arm Title
AI-assisted withdrawal group
Arm Type
Experimental
Arm Description
A deep learning-based automatic polyp detection system was used to assist the endoscopist.
Arm Title
Routine withdrawal group
Arm Type
No Intervention
Arm Description
Routine withdrawal without any assist.
Intervention Type
Device
Intervention Name(s)
Automatic polyp detection system
Intervention Description
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.
Primary Outcome Measure Information:
Title
adenoma detection rate(ADR)
Description
the number of patients with at least one adenoma divided by the total number of patients.
Time Frame
30 minutes
Secondary Outcome Measure Information:
Title
polyp detection rate(PDR)
Description
the number of patients with at least one polyp divided by the total number of patients.
Time Frame
30 minutes
Title
adenoma per colonoscopy
Description
the number of adenomas detected during colonoscopy withdraw divided by the number of colonoscopies.
Time Frame
30 minutes
Title
polyp per colonoscopy
Description
the number of polyps detected during colonoscopy withdraw divided by the number of colonoscopies.
Time Frame
30 minutes

10. Eligibility

Sex
All
Minimum Age & Unit of Time
40 Years
Maximum Age & Unit of Time
85 Years
Accepts Healthy Volunteers
No
Eligibility Criteria
Inclusion Criteria: Patients aged between 40-85 years old who have indications for screening, surveillance and diagnostic. Patients who have signed inform consent form. Exclusion Criteria: 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
Central Contact Person:
First Name & Middle Initial & Last Name or Official Title & Degree
Zhaoshen Li, M.D
Phone
86-21-31161365
Email
li.zhaoshen@hotmail.com
First Name & Middle Initial & Last Name or Official Title & Degree
Yu Bai, M.D
Phone
86-21-31161335
Email
baiyu1998@hotmail.com
Overall Study Officials:
First Name & Middle Initial & Last Name & Degree
Zhaoshen Li, M.D
Organizational Affiliation
Changhai Hospital
Official's Role
Principal Investigator
Facility Information:
Facility Name
Changhai Hospital, Second Military Medical University
City
Shanghai
ZIP/Postal Code
200433
Country
China
Individual Site Status
Recruiting
Facility Contact:
First Name & Middle Initial & Last Name & Degree
zhaoshen Li, MD
Phone
86-21-81873241
Email
zhaoshenlismmu@gmail.com
First Name & Middle Initial & Last Name & Degree
Zhaoshen Li, MD

12. IPD Sharing Statement

Plan to Share IPD
No
Citations:
PubMed Identifier
29928897
Citation
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.
Results Reference
background
PubMed Identifier
30527583
Citation
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.
Results Reference
background

Learn more about this trial

Impact of Automatic Polyp Detection System on Adenoma Detection Rate

We'll reach out to this number within 24 hrs