Artificial Intelligence in Colonoscopy

Not Applicable

Recruiting

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

• Registration Number: NCT06786793
• Lead Sponsor: Jagiellonian University

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.

Study Timeline

• Study Start: 2024-11-01
• Status Verified: 2025-01
• Study First Submitted: 2025-01-12
• Study First Submitted QC: 2025-01-15
• Last Update Submitted: 2025-01-15
• Study First Posted: 2025-01-22
• Last Update Posted: 2025-01-22
• Primary Completion: 2025-10-31
• Study Completion: 2025-12-31

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 630

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 & Design

• Study Type: INTERVENTIONAL
• Study Design: PARALLEL

Primary Outcome Measures

Name	Time	Method
Adenoma detection rate (ADR)	During the colonoscopy examination	The percentage of colonoscopies when at least one histologically proven adenoma was found.

Secondary Outcome Measures

Name	Time	Method
Utility of artificial intelligence for both novice and experienced endoscopists	During the colonoscopy examination	The difference in adenoma detection rates (ADR) achieved with and without AI in trainees and expert endoscopists.
Assessing the morphology of polyps detected during colonoscopy	During the colonoscopy examination	Assessment of the differences in polyps' morphology detected in both arms of the study.
Cost analysis of procedures performed with the use of artificial intelligence	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.

Trial Locations

Locations (2)

MEDICINA Medical Center; 🇵🇱 Krakow, Lesser Poladn, Poland

Brothers Hospitallers Medical Center, Hospital of St John of god in Krakow; 🇵🇱 Krakow, Lesser Polasd, Poland