An interventional study of colonoscopy in Screening Colonoscopy, Colonoscopic Control After Polypectomy and Suspected Colon Polyps, sponsored by Universitätsklinikum Hamburg-Eppendorf. Recruiting at 10 sites in Germany. Open to participants aged 35 Years and older, including healthy volunteers. Per ClinicalTrials.gov, last updated 2023-06-28.
Sponsored by Universitätsklinikum Hamburg-Eppendorf · Not applicable, Interventional, and Diagnostic
Colonoscopy is currently the best method of detection of intestinal tumors and polyps, particularly because polyps can also be biopsied and removed. There is a clear correlation between the adenoma detection rate and prevented carcinomas, so adenoma detection rate is the main parameter for the outcome quality of diagnostic colonoscopy. The efficiency of preventive colonoscopy needs optimisation by increase in adenoma detection rate, as it is known from many studies that approximately 15-30% of all adenomas can be overlooked. This mainly applies to smaller and flat adenomas. However, since even smaller polyps may be relevant for colorectal cancer development, the aim of colonoscopy should be to preferably be able to recognize all polyps and other changes.The latest and by far the most interesting development in this field is the use of artificial intelligence systems. They consist of a switched-on software with a small computer connected to the endoscope processor; the patient's introduced endoscope is completely unchanged.
The present study therefore compares the adenoma detection rate (ADR) of the latest generation of devices with high-resolution imaging from Fujifilm with and without the connection of artificial intelligence.
Methods of Computer Vision (CV) and Artificial Intelligence (AI) provide completely new opportunities, e.g. in the automatic polyp detection and differentiation of a lesion based on its endoscopic image. Computer vision using artificial intelligence methods means the application of "trained" so-called deep neural net (DNN) with a set of defined images (e.g. everyday scenes) and well-known solutions ( e.g. name of the pictured item; c.f. e.g. the "ImageNet Challenge"). The technical feasibility of using AI algorithms in endoscopy has already been proven in many cases. In the present study, it is an AI system from Fujifilm, which is already clinically usable. By using Fujifilm high-resolution imaging devices in colonoscopies, AI will be added randomly.
385 studies on the registry are indexed under Polyps; 59 are open to participants now.
This study's planned enrollment of 1,572 is above the median of 160 across 250 interventional studies indexed under Polyps.
Browse Polyps studies →Universitätsklinikum Hamburg-Eppendorf is the lead sponsor of 403 studies on the registry; 83 are open to participants now.
Counted across the registry records on this site, refreshed daily.
Exclusion Criteria:
colonoscopy with artificial intelligence added
Procedure: colonoscopy
conventional colonoscopy
Procedure: colonoscopy
addition of polyp detection algorithm by Fujifilm
Adenoma detection rate
Difference in adenoma detection rate (all adenomas/all patients) between the two groups
Time frame: during procedure to histological examination result, approximately 2 days
Patient rate difference
Differences in the patient rate with adenomas (adenoma detection rate, i.e. rate of patients with at least one adenoma)
Time frame: during procedure to histological examination result, approximately 2 days
Adenoma subgroup differences
Differences subgroups of adenomas (flat, small, high-grade dysplasia)
Time frame: histological examination result, approximately 2 days
rate of hyperplastic polyp detection in both groups
Differences in the detection of hyperplastic polyps
Time frame: histological examination result, approximately 2 days
rate of polyp detection in preventive and diagnostic colonoscopy
Differences in preventive vs. diagnostic colonoscopy
Time frame: during procedure to histological examination result, approximately 2 days
Switching number (BLI, LCI) in both groups
number of switches to visual support by colour filters
Time frame: during procedure
incidence of reasons for switching to BLI/LCI
reasons for switching to visual support by colour filters
Time frame: during procedure
quality of polyp detection rate by image evaluation
differential diagnosis of colon polyps in both groups with/without CADEYE)
Time frame: until 2 months after recruitment stop
Plan to share: No
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Universitätsklinikum Hamburg-Eppendorf