Artificial Intelligence in Colonoscopy

NCT ID: NCT06786793

Last Updated: 2025-01-22

Basic Information

• Recruitment Status: RECRUITING
• Clinical Phase: NA
• Total Enrollment: 630 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2024-11-01
• Study Completion Date: 2025-12-31

Brief Summary

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.

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.

Conditions

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

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: DIAGNOSTIC
• Blinding Strategy: NONE

Study Groups

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.

Group Type: EXPERIMENTAL

Computer-aided detection (CADe)

Intervention Type: 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.

Non-AI-group

Non-AI-group will consist of patients undergoing colonoscopy without the assistance of this system.

Group Type: NO_INTERVENTION

No interventions assigned to this group

Interventions

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.

Intervention Type: DEVICE

Eligibility Criteria

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.

• Minimum Eligible Age: 50 Years
• Maximum Eligible Age: 65 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Jagiellonian University

OTHER

Sponsor Role: lead

Responsible Party

Zofia Orzeszko

Principal Investigator

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Miroslaw Szura, Prof.

Role: STUDY_CHAIR

Jagiellonian University in Krakow

Zofia Orzeszko, MD

Role: PRINCIPAL_INVESTIGATOR

Jagiellonian University in Krakow

Locations

MEDICINA Medical Center

Krakow, Lesser Poladn, Poland

Site Status: RECRUITING

Brothers Hospitallers Medical Center, Hospital of St John of god in Krakow

Krakow, Lesser Polasd, Poland

Site Status: RECRUITING

Countries

Poland

Central Contacts

Zofia Orzeszko, MD

Role: CONTACT

z.orzeszko@bonifratrzy.krakow.pl

+48123797145

Facility Contacts

Zofia Orzeszko, MD

Role: primary

z.orzeszko@bonifratrzy.krakow.pl

+48123797145

Zofia Orzeszko, MD

Role: primary

z.orzeszko@bonifratrzy.krakow.pl

+48123797145

References

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Reference Type: BACKGROUND

PMID: 23567353 (View on PubMed)

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Reference Type: BACKGROUND

PMID: 32240683 (View on PubMed)

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Reference Type: BACKGROUND

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Reference Type: BACKGROUND

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Reference Type: BACKGROUND

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Corley DA, Jensen CD, Marks AR, Zhao WK, Lee JK, Doubeni CA, Zauber AG, de Boer J, Fireman BH, Schottinger JE, Quinn VP, Ghai NR, Levin TR, Quesenberry CP. Adenoma detection rate and risk of colorectal cancer and death. N Engl J Med. 2014 Apr 3;370(14):1298-306. doi: 10.1056/NEJMoa1309086.

Reference Type: BACKGROUND

PMID: 24693890 (View on PubMed)

Kaminski MF, Regula J, Kraszewska E, Polkowski M, Wojciechowska U, Didkowska J, Zwierko M, Rupinski M, Nowacki MP, Butruk E. Quality indicators for colonoscopy and the risk of interval cancer. N Engl J Med. 2010 May 13;362(19):1795-803. doi: 10.1056/NEJMoa0907667.

Reference Type: BACKGROUND

PMID: 20463339 (View on PubMed)

Other Identifiers

2024.000.421

Identifier Type: -

Identifier Source: org_study_id