Prospective Randomized Study on the Use of Artificial Intelligence (Fujifilm) for Polyp Detection in Colonoscopy
Trial Snapshot
- Phase
- Not Applicable
- Status
- Recruiting
- Enrollment
- 1,572
- Locations
- 20
- Primary Endpoint
- Adenoma detection rate
Study Overview
Brief Summary
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.
Detailed Description
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.
Study Design
- Study Type
- Interventional
- Allocation
- Randomized
- Intervention Model
- Parallel
- Primary Purpose
- Diagnostic
- Masking
- None
Eligibility Criteria
- Ages
- 35 Years to — (Adult, Older Adult)
- Sex
- All
- Accepts Healthy Volunteers
- Yes
Inclusion Criteria
- •Persons> 35 years of age who are capable of giving informed consent
- •Planned diagnostic colonoscopy (clarification of symptoms, polyp follow-up)
- •Screening colonoscopy for men >50 or women > 55 years of age
Exclusion Criteria
- •Colon bleeding
- •Colon carcinoma
- •Known polyps for removal
- •Inflammatory bowel disease
- •Colonic stenosis
- •Other suspected colon disease for further clarification
- •Follow-up care after colon cancer surgery (partial colon resection)
- •Anticoagulant drugs that make a biopsy or polypectomy impossible
- •Poor general condition (ASA IV)
- •Incomplete colonoscopy planned
Outcomes
Primary Outcomes
Adenoma detection rate
Time Frame: during procedure to histological examination result, approximately 2 days
Difference in adenoma detection rate (all adenomas/all patients) between the two groups
Secondary Outcomes
- Switching number (BLI, LCI) in both groups(during procedure)
- incidence of reasons for switching to BLI/LCI(during procedure)
- Patient rate difference(during procedure to histological examination result, approximately 2 days)
- rate of polyp detection in preventive and diagnostic colonoscopy(during procedure to histological examination result, approximately 2 days)
- quality of polyp detection rate by image evaluation(until 2 months after recruitment stop)
- Adenoma subgroup differences(histological examination result, approximately 2 days)
- rate of hyperplastic polyp detection in both groups(histological examination result, approximately 2 days)
Investigators
Prof. Dr. Thomas Rösch
Director of Department of Interdisciplinary Endoscopy of University Hospital Hamburg Eppendorf
Universitätsklinikum Hamburg-Eppendorf