Study on the Use of Artificial Intelligence (Fujifilm) for Polyp Detection in Colonoscopy

Study Overview
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Brief Summary
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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
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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.

Official Title
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Prospective Randomized Study on the Use of Artificial Intelligence (Fujifilm) for Polyp Detection in Colonoscopy

Conditions
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Screening Colonoscopy, Colonoscopic Control After Polypectomy, Suspected Colon Polyps

Intervention / Treatment
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* Procedure: colonoscopy


Participation Criteria
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Eligibility Criteria
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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

Ages Eligible for Study
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Minimum Age: 35 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Diagnostic
Allocation: Randomized
Intervention Model: Parallel Assignment
Masking: None (Open Label)


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Other: AI colonoscopy<br>colonoscopy with artificial intelligence added | Procedure: colonoscopy<br>* addition of polyp detection algorithm by Fujifilm<br>|
| Sham Comparator: conventional colonoscopy<br>conventional colonoscopy | Procedure: colonoscopy<br>* addition of polyp detection algorithm by Fujifilm<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Adenoma detection rate | Difference in adenoma detection rate (all adenomas/all patients) between the two groups | during procedure to histological examination result, approximately 2 days | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Patient rate difference | Differences in the patient rate with adenomas (adenoma detection rate, i.e. rate of patients with at least one adenoma) | during procedure to histological examination result, approximately 2 days | 
| Adenoma subgroup differences | Differences subgroups of adenomas (flat, small, high-grade dysplasia) | histological examination result, approximately 2 days | 
| rate of hyperplastic polyp detection in both groups | Differences in the detection of hyperplastic polyps | histological examination result, approximately 2 days | 
| rate of polyp detection in preventive and diagnostic colonoscopy | Differences in preventive vs. diagnostic colonoscopy | 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 | during procedure | 
| incidence of reasons for switching to BLI/LCI | reasons for switching to visual support by colour filters | during procedure | 
| quality of polyp detection rate by image evaluation | differential diagnosis of colon polyps in both groups with/without CADEYE) | until 2 months after recruitment stop |