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.
Consent to participate in the study,
Age between 50 and 65 years,
Scheduled outpatient colonoscopy.
Previous colonoscopy,
History of colorectal surgery,
Ongoing biological therapy for any indication,
Primary sclerosing cholangitis,
Parallel
Computer-aided detection (CADe)
DeviceEndo-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.
Adenoma detection rate (ADR)
The percentage of colonoscopies when at least one histologically proven adenoma was found.
Utility of artificial intelligence for both novice and experienced endoscopists
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
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
The assessment of cost-efficiency of AI implementation, including the increased cost of pathological evaluation and additional surveillance examinations.
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