Does AI-assisted Colonoscopy Improve Adenoma Detection in Screening Colonoscopy?

July 14, 2020 updated by: Joseph JY SUNG, Chinese University of Hong Kong

Does AI-assisted Colonoscopy Improve Adenoma Detection in Screening Colonoscopy? A Multi-center Randomized Controlled

To date, there is a lack of large-scale randomized controlled study using AI assistance in the detection of polyps/adenoma in a screening population. The correlation of fecal occult blood test (FIT or FOBT) and the advantage of AI-assisted colonoscopy has not been investigated. There is also a lack of information of the benefit of AI-assisted colonoscopy in experienced colonoscopist versus trainee/resident.

Study Overview

Status

Unknown

Conditions

Intervention / Treatment

Detailed Description

There are several studies showing that AI-assisted colonoscopy can help in identifying and characterizing polyps found on colonoscopy.

  • Byrne et al demonstrated that their AI model for real-time assessment of endoscopic video images of colorectal polyp can differentiate between hyperplastic diminutive polyps vs adenomatous polyps with sensitivity of 98% and specificity of 83% (Byrne et al. GUT 2019)
  • Urban et al designed and trained deep CNNs to detect polyps in archived video with a ROC curve of 0.991 and accuracy of 96.4%. The total number of polyps identified is significantly higher but mainly in the small (1-3mm and 4-6mm polyps) (Urban et al. Gastroenterol 2018)
  • Wang et al conducted an open, non-blinded trial consecutive patients (n=1058) prospectively randomized to undergo diagnostic colonoscopy with or without AI assistance. They found that AI system increased ADR from 20.3% to 29.1% and the mean number of adenomas per patients from 0.31 to 0.53. This was due to a higher number of diminutive polyps found while there was no statistic difference in larger adenoma. (Wang et al. GUT 2019). In this study, they excluded patients with IBD, CRC and colorectal surgery. The patients presented with symptoms to hospital for investigation.

To date, there is a lack of large-scale randomized controlled study using AI assistance in the detection of polyps/adenoma in a screening population. The correlation of fecal occult blood test (FIT or FOBT) and the advantage of AI-assisted colonoscopy has not been investigated. There is also a lack of information of the benefit of AI-assisted colonoscopy in experienced colonoscopist versus trainee/resident.

Study Type

Interventional

Enrollment (Anticipated)

2994

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

45 years to 75 years (Adult, Older Adult)

Accepts Healthy Volunteers

Yes

Genders Eligible for Study

All

Description

Inclusion Criteria

  • Patients receiving colonoscopy screening
  • Patients aged 45-75 years
  • Both patients who have or have not done a FIT test and both FIT +ve and FIT -ve subjects

Exclusion Criteria

  • Patients who have symptom(s) suggestive of colorectal diseases
  • Patients who have a history of inflammatory bowel disease, colorectal cancer or polyposis syndrome (anaemia, bloody stool, tenesmus and obstructive symptoms)
  • Patients who had colonoscopy or other investigation of colon and rectum in the past 10 years
  • Patients who had surgery for colorectal diseases
  • Patients who cannot tolerate bowel preparation or have suboptimal bowel preparations (Boston Bowel Preparation Scale)
  • Cannot reach caecum
  • Patients who are incompetent in giving informed consent

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Diagnostic
  • Allocation: Randomized
  • Interventional Model: Parallel Assignment
  • Masking: Single

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Active Comparator: Standard
Procedure: Standard Colonoscopy
Standard Colonoscopy
Active Comparator: AI-assisted Group
Procedure: AI-assisted Colonoscopy
This is a multi-center prospective randomized controlled study comparing real-time AI-assisted colonoscopy versus standard colonoscopy in a real-life setting.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Per-patient ADR in each group
Time Frame: 12 months
For the AI-Assisted group, it is defined as the number of patients with at least 1 adenoma identified in the colon divided by the total number of patients in the AI-Assisted group.
12 months
Per-patient ADR in each group
Time Frame: 12 months
For the Standard group, it is defined as the number of patients with at least 1 adenoma identified in the colon divided by the total number of patients in the Standard group.
12 months

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Chinese University of Hong Kong

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

November 28, 2019

Primary Completion (Anticipated)

November 27, 2020

Study Completion (Anticipated)

November 27, 2020

Study Registration Dates

First Submitted

June 5, 2020

First Submitted That Met QC Criteria

June 8, 2020

First Posted (Actual)

June 9, 2020

Study Record Updates

Last Update Posted (Actual)

July 16, 2020

Last Update Submitted That Met QC Criteria

July 14, 2020

Last Verified

July 1, 2020

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • AI-CLN Study_Protocol

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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