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NCT Number: NCT06786793

Artificial Intelligence in Colonoscopy

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.

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Key information

Conditions

Quality Indicators, Health Care Artificial Intelligence (AI) Colonoscopy Diagnostic Techniques and Procedures

Age range

50 year–65 year

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

MEDICINA Medical Center, Krakow, Lesser Poladn, Poland

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About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

Computer-aided detection (CADe)

Device

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.

Primary outcomes

  1. Adenoma detection rate (ADR)

    Time frame: During the colonoscopy examination

    The percentage of colonoscopies when at least one histologically proven adenoma was found.

Secondary outcomes

  1. 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.

  2. 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.

  3. 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.

Study contacts

Contact information is provided by the study sponsor or research team.

Zofia Orzeszko, MD

CONTACT

[email protected]

+48123797145

Sponsors and collaborators

Lead sponsor

Jagiellonian University

Other

Registry information

Official study title

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

Important dates

Study start
2024
Primary completion
2025
Study completion
2025
First posted
Jan 22, 2025
Registry last updated
Jan 22, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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