NCT06786793

Artificial Intelligence in Colonoscopy

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

• Status: Recruiting
• Phase: N/A
• Study type: Interventional
• Enrollment: 630 (estimated)
• Sponsor: Jagiellonian University · Academic / Other
• Sex: All
• Age: 50 Years – 65 Years
• Healthy volunteers: Not accepted

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

Interventions

Type	Name	Description
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.

Timeline

• Start date: 2024-11-01
• Primary completion: 2025-10-31
• Completion: 2025-12-31
• First posted: 2025-01-22
• Last updated: 2025-01-22

Locations

2 sites across 1 country: Poland