Artificial Intelligence in Colonoscopy

January 15, 2025 updated by: Zofia Orzeszko, Jagiellonian University

Artificial Intelligence in Endoscopic Diagnosis of Colorectal Polyps: A Prospective Randomized Study.

Colorectal cancer is the second most common malignancy in the countries of the European Union. Colonoscopy is the primary method for detecting and preventing the development of colorectal cancer is endoscopic examination. This study aims to evaluate the impact of artificial intelligence on the detection rate of polyps and early stages of colorectal cancer.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Detailed Description

Colorectal cancer is the second most common malignancy in the countries of the European Union. The primary method for detecting and preventing the development of colorectal cancer is endoscopic examination-colonoscopy, during which precancerous lesions such as adenomas and serrated polyps can be removed. The effectiveness of colonoscopy depends on the adenoma detection rate, which varies among endoscopists and is influenced by their skills and experience. It has been proven that high-quality colonoscopy prevents the omission of colorectal cancer, which might develop in the future as so-called interval cancer. A breakthrough in machine learning in recent years has enabled the development of commercial artificial intelligence systems. These systems aim to improve the detection rates of precancerous polyps and, consequently, potentially reduce the risk of developing colorectal cancer. Artificial intelligence is also expected to help standardize performance across endoscopic procedures of varying quality, thereby contributing to a reduction in colorectal cancer incidence in the future. This study aims to evaluate the impact of artificial intelligence on the detection rate of polyps and early stages of colorectal cancer.

Study Type

Interventional

Enrollment (Estimated)

630

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

  • Poland
    • Lesser Poladn
      • Krakow, Lesser Poladn, Poland, 31559
        • Recruiting
        • MEDICINA Medical Center
        • Contact:
        • Principal Investigator:
          • Zofia Orzeszko, MD
    • Lesser Polasd
      • Krakow, Lesser Polasd, Poland, 31061
        • Recruiting
        • Brothers Hospitallers Medical Center, Hospital of St John of god in Krakow
        • Contact:
        • Principal Investigator:
          • Tomasz Gach, PhD

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

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Description

Inclusion Criteria:

  • Consent to participate in the study,
  • Age between 50 and 65 years,
  • Scheduled outpatient colonoscopy.

Exclusion Criteria:

  • Previous colonoscopy,
  • History of colorectal surgery,
  • Ongoing biological therapy for any indication,
  • Primary sclerosing cholangitis,
  • Familial polyposis syndrome,
  • Chronic diarrhea,
  • Ulcerative colitis,
  • Crohn's disease.

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: None (Open Label)

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Experimental: AI-group
AI-group will include patients undergoing colonoscopy with the support of the ENDO-AID OIP-1 artificial intelligence system for colorectal polyp detection.
Device: Computer-aided detection (CADe)
Endo-Aid CADe system is an AI-assisted computer-aided lesion detection application on ENDO-AID hardware. It uses a complex algorithm created via a neural network developed and taught by Olympus. With this new app, the sophisticated machine learning system can alert the endoscopist in real-time when a suspicious lesion appears on the screen. The image from the vision processor is transferred to the CADe device. The computer application recognizes the shape of the polyps and marks their place on the monitor screen.
No Intervention: Non-AI-group
Non-AI-group will consist of patients undergoing colonoscopy without the assistance of this system.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Adenoma detection rate (ADR)
Time Frame: During the colonoscopy examination
The percentage of colonoscopies when at least one histologically proven adenoma was found.
During the colonoscopy examination

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Utility of artificial intelligence for both novice and experienced endoscopists
Time Frame: During the colonoscopy examination
The difference in adenoma detection rates (ADR) achieved with and without AI in trainees and expert endoscopists.
During the colonoscopy examination
Assessing the morphology of polyps detected during colonoscopy
Time Frame: During the colonoscopy examination
Assessment of the differences in polyps' morphology detected in both arms of the study.
During the colonoscopy examination
Cost analysis of procedures performed with the use of artificial intelligence
Time Frame: Through study completion, an average of 6 months
The assessment of cost-efficiency of AI implementation, including the increased cost of pathological evaluation and additional surveillance examinations.
Through study completion, an average of 6 months

Collaborators and Investigators

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

Sponsor

Jagiellonian University

Investigators

  • Study Chair: Miroslaw Szura, Prof., Jagiellonian University in Krakow
  • Principal Investigator: Zofia Orzeszko, MD, Jagiellonian University in Krakow

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 1, 2024

Primary Completion (Estimated)

October 31, 2025

Study Completion (Estimated)

December 31, 2025

Study Registration Dates

First Submitted

January 12, 2025

First Submitted That Met QC Criteria

January 15, 2025

First Posted (Actual)

March 25, 2025

Study Record Updates

Last Update Posted (Actual)

March 25, 2025

Last Update Submitted That Met QC Criteria

January 15, 2025

Last Verified

January 1, 2025

More Information

Terms related to this study

Keywords

Other Study ID Numbers

  • 2024.000.421

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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