An interventional study of Computer-aided detection (CADe) in Quality Indicators, Health Care, Artificial Intelligence (AI) and Colonoscopy Diagnostic Techniques and Procedures, sponsored by Jagiellonian University. Recruiting at 2 sites in Poland. Open to participants aged 50 Years to 65 Years. Per ClinicalTrials.gov, last updated 2025-01-22.
Sponsored by Jagiellonian University · Not applicable, Interventional, and Diagnostic
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.
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.
Jagiellonian University is the lead sponsor of 160 studies on the registry; 35 are open to participants now.
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Exclusion Criteria:
AI-group will include patients undergoing colonoscopy with the support of the ENDO-AID OIP-1 artificial intelligence system for colorectal polyp detection.
Device: Computer-aided detection (CADe)
Non-AI-group will consist of patients undergoing colonoscopy without the assistance of this system.
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.
Adenoma detection rate (ADR)
The percentage of colonoscopies when at least one histologically proven adenoma was found.
Time frame: During the colonoscopy examination
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.
Time frame: During the colonoscopy examination
Assessing the morphology of polyps detected during colonoscopy
Assessment of the differences in polyps' morphology detected in both arms of the study.
Time frame: During the colonoscopy examination
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.
Time frame: Through study completion, an average of 6 months
Plan to share: No
Started Nov 2024 (actual). Primary completion is estimated for Oct 2025, 11 months after the start.
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