Computer-aided Detection During Screening Colonoscopy (Experts)

March 30, 2022 updated by: Instituto Ecuatoriano de Enfermedades Digestivas

Real-time Computer-aided Polyp Detection During Screening Colonoscopy Performed by Expert Endoscopists

Evaluation of the colonic mucosa with a high definition colonoscope (EPKi7010 video processor).

The endoscopy images will be seen on a 27inch, flat-panel, high-definition LCD monitor (Radiance™ ultraSC-WU27-G1520 model) only by one expert endoscopist, randomly assigned.

The number, location, and polyps' features (Paris classification) will be recorded by the operator. If a polyp is detected, the endoscopist will remove the polyp endoscopically with a cold snare.

The same patient will be submitted to a second, the same session, computed aided real-time colonoscopy using the DISCOVERY, AI-assisted polyp detector. Colonoscopy will be performed by a same-level-of-expertise operator in comparison to the initial procedure. Any polyp or lesion detected with the AI system will be recorded and endoscopically removed and considered as a missed lesion from standard colonoscopy.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Detailed Description

Screening colonoscopy has decreased the incidence of colorectal carcinoma in the previous decades. However, there are reports of missed polyps and interval CRC following screening colonoscopy. Several factors may affect the ADR, PDR, and missed lesions rates, such as bowel preparation, percentage of mucosal surface evaluation, and the training levels of operators.

Artificial intelligence using deep-learning algorithms has been implemented in gastrointestinal endoscopy, mainly for the detection and diagnosis of GI tract lesions such as colonic polyps and adenomas. The implementation of automated polyp detection software during screening colonoscopy may prevent the missing of polyp and adenoma during screening colonoscopy. Therefore, improving the ADR and PDR during colonoscopies. All of this, with the aim of decrease the incidence of interval colorectal carcinoma (CRC), and CRC-related morbidity and mortality.

The Discovery Artificial Intelligence assisted polyp detector (Pentax Medical, Hoya Group) was recently launched for clinical practice. This AI software was trained with 120,000 files from approximately 300 clinical cases. The visual aided detection (bounding box locating a polyp on the monitor) will alert the endoscopist if a polyp/adenoma was missed during the standard, screening procedure.

To the best of our knowledge, this may be the first study evaluating the Discovery AI-assisted polyp detector on clinical practice in the western hemisphere. The investigators aim to evaluate the real-world effectiveness of AI-assisted colonoscopy in clinical practice. The investigators will also evaluate the role of endoscopists' levels of training in the ADR, PDR, and missed lesion rate.

Study Type

Interventional

Enrollment (Anticipated)

209

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 Contact

Study Locations

  • Ecuador
    • Guayas
      • Guayaquil, Guayas, Ecuador, 090505
        • Recruiting
        • Ecuadorian Institute of Digestive Diseases
        • Contact:
        • Principal Investigator:
          • Carlos A Robles-Medranda, MD
        • Sub-Investigator:
          • Roberto A Oleas, MD
        • Sub-Investigator:
          • Martha G Arevalo-Mora, MD
        • Sub-Investigator:
          • Daniel Calle, MD
        • Sub-Investigator:
          • Miguel A Puga-Tejada, MD

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 80 years (Adult, Older Adult)

Accepts Healthy Volunteers

No

Genders Eligible for Study

All

Description

Inclusion Criteria:

  • Provided informed written consent
  • Age greater than 45 years of age
  • Adequate Bowel preparation

Exclusion Criteria:

  • History of inflammatory bowel disease, familial polyposis syndrome
  • History of colorectal carcinoma, colorectal surgery
  • History of uncontrolled coagulopathy
  • History of previously failed attempt colonoscopy

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: N/A
  • Interventional Model: Single Group Assignment
  • Masking: None (Open Label)

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Experimental: Patients for CRC screening and diagnostic colonoscopy
Consecutive patients >45 years of age submitted for diagnostic colonoscopy
Diagnostic test: Standard high-definition colonoscopy

Evaluation of the colonic mucosa with a high definition colonoscope (EPKi7010 video processor).

The endoscopy images will be seen on a 27inch, flat panel, high-definition LCD monitor (Radiance™ ultraSC-WU27-G1520 model) only by one expert endoscopist, randomly assigned.

The number, location and polyps' features (Paris classification) will be recorded by the operator. If a polyp is detected, the endoscopist will remove the polyp endoscopically with a cold snare and forceps biopsy.

Diagnostic test: Colonoscopy with real-time AI assisted automated polyp detection
The same patient will be submitted to a second, same session, computed aided real-time colonoscopy using the DISCOVERY, AI assisted polyp detector. Colonoscopy will be performed by a same-level-of-expertise operator in comparison to the initial procedure. Any polyp or lesion detected with the AI system will be recorded and endoscopically removed and considered as a missed lesion from standard colonoscopy.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Adenoma detection rate of computer-aided after standard colonoscopy.
Time Frame: 30 days
Number of examinations with at least one adenoma detected during colonoscopy while using the AI-based model
30 days
Polyp detection rate of computer-aided following standard colonoscopy.
Time Frame: 30 days
Number of examination with at least one polyp detected while using the AI-based model
30 days

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Polyp miss rate of standard high-definition colonoscopy.
Time Frame: 30 days
Total number of missed polyps/ (total number of missed polyps + total number of polyps on initial examination)
30 days
Adenoma miss rate of standard high-definition colonoscopy.
Time Frame: 30 days
Total number of missed adenomas/ (total number of missed adenomas + total number of adenomas on initial examination)
30 days

Collaborators and Investigators

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

Sponsor

Instituto Ecuatoriano de Enfermedades Digestivas

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)

April 26, 2021

Primary Completion (Anticipated)

April 30, 2022

Study Completion (Anticipated)

June 28, 2022

Study Registration Dates

First Submitted

June 1, 2021

First Submitted That Met QC Criteria

June 1, 2021

First Posted (Actual)

June 7, 2021

Study Record Updates

Last Update Posted (Actual)

March 31, 2022

Last Update Submitted That Met QC Criteria

March 30, 2022

Last Verified

March 1, 2022

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • IECED-042621

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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