Artificial Intelligence in Colonoscopy

Jagiellonian University

Status: Enrolling

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

Treatments: Device: Computer-aided detection (CADe)

Study type: Interventional

Funder types: Other

Identifiers

NCT06786793

2024.000.421

Details and patient eligibility

About

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.

Full 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.

Enrollment: 630 estimated patients

Sex: All

Ages: 50 to 65 years old

Volunteers: No Healthy Volunteers

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.

Trial design

Primary purpose: Diagnostic

Allocation: Randomized

Interventional model: Parallel Assignment

Masking: None (Open label)

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630 participants in 2 patient groups

AI-group

Experimental group

Description:

AI-group will include patients undergoing colonoscopy with the support of the ENDO-AID OIP-1 artificial intelligence system for colorectal polyp detection.

Treatment:

Device: Computer-aided detection (CADe)

Non-AI-group

No Intervention group

Description:

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