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

Not Applicable

Recruiting

• Conditions: Suspected Colon Polyps; Screening Colonoscopy; Colonoscopic Control After Polypectomy
• Interventions: Procedure: colonoscopy

• Registration Number: NCT04894708
• Lead Sponsor: Universitätsklinikum Hamburg-Eppendorf

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.

Basic Information
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Recruitment & Eligibility
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Study & Design
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Trial Locations

Study Timeline

• Study Start: 2020-10-28
• Study First Submitted: 2020-12-01
• Study First Submitted QC: 2021-05-18
• Study First Posted: 2021-05-20
• Status Verified: 2023-06
• Last Update Submitted: 2023-06-27
• Last Update Posted: 2023-06-28
• Primary Completion: 2024-05
• Study Completion: 2024-09

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 1572

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

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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

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Study & Design

• Study Type: INTERVENTIONAL
• Study Design: PARALLEL

Arm && Interventions

Group	Intervention	Description
AI colonoscopy	colonoscopy	colonoscopy with artificial intelligence added
conventional colonoscopy	colonoscopy	conventional colonoscopy

Primary Outcome Measures

Name	Time	Method
Adenoma detection rate	during procedure to histological examination result, approximately 2 days	Difference in adenoma detection rate (all adenomas/all patients) between the two groups

Secondary Outcome Measures

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

Trial Locations

Locations (10)

GastroZentrum Lippe; 🇩🇪 Bad Salzuflen, Germany

Gastroenterologiepraxis Dr. Moog; 🇩🇪 Kassel, Hessen, Germany

Universitätsklinikum Leipzig; 🇩🇪 Leipzig, Sachsen, Germany

University Hospital Bonn; 🇩🇪 Bonn, Germany

University Hospital Eppendorf; 🇩🇪 Hamburg, Germany

St. Vinzenz-Hospital / Akademisches Lehrkrankenhaus der Universität zu Köln; 🇩🇪 Köln, Germany

University Hospital Magdeburg; 🇩🇪 Magdeburg, Germany

Marienhospital Osnabrück; 🇩🇪 Osnabrück, Germany

Asklepios Paulinen Klinik Wiesbaden; 🇩🇪 Wiesbaden, Germany

Gastroenterologie am Bayerischen Platz; 🇩🇪 Berlin, Germany