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

NCT ID: NCT04894708

Last Updated: 2023-06-28

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

UNKNOWN

Clinical Phase

NA

Total Enrollment

1572 participants

Study Classification

INTERVENTIONAL

Study Start Date

2020-10-28

Study Completion Date

2024-09-30

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.

Conditions

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Screening Colonoscopy Colonoscopic Control After Polypectomy Suspected Colon Polyps

Study Design

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Allocation Method

RANDOMIZED

Intervention Model

PARALLEL

Primary Study Purpose

DIAGNOSTIC

Blinding Strategy

NONE

Study Groups

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AI colonoscopy

colonoscopy with artificial intelligence added

Group Type OTHER

colonoscopy

Intervention Type PROCEDURE

addition of polyp detection algorithm by Fujifilm

conventional colonoscopy

conventional colonoscopy

Group Type SHAM_COMPARATOR

colonoscopy

Intervention Type PROCEDURE

addition of polyp detection algorithm by Fujifilm

Interventions

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colonoscopy

addition of polyp detection algorithm by Fujifilm

Intervention Type PROCEDURE

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
Minimum Eligible Age

35 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes

Sponsors

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FUJIFILM Deutschland, Branch office of FUJIFILM Europe GmbH

UNKNOWN

Sponsor Role collaborator

Universitätsklinikum Hamburg-Eppendorf

OTHER

Sponsor Role lead

Responsible Party

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Prof. Dr. Thomas Rösch

Director of Department of Interdisciplinary Endoscopy of University Hospital Hamburg Eppendorf

Responsibility Role PRINCIPAL_INVESTIGATOR

Principal Investigators

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Thomas Rösch, Prof. dr.

Role: PRINCIPAL_INVESTIGATOR

Universitätsklinikum Hamburg-Eppendorf

Locations

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Gastroenterologiepraxis Dr. Moog

Kassel, Hesse, Germany

Site Status TERMINATED

Universitätsklinikum Leipzig

Leipzig, Saxony, Germany

Site Status RECRUITING

GastroZentrum Lippe

Bad Salzuflen, , Germany

Site Status TERMINATED

Gastroenterologie am Bayerischen Platz

Berlin, , Germany

Site Status RECRUITING

University Hospital Bonn

Bonn, , Germany

Site Status RECRUITING

St. Vinzenz-Hospital / Akademisches Lehrkrankenhaus der Universität zu Köln

Cologne, , Germany

Site Status RECRUITING

University Hospital Eppendorf

Hamburg, , Germany

Site Status RECRUITING

University Hospital Magdeburg

Magdeburg, , Germany

Site Status RECRUITING

Marienhospital Osnabrück

Osnabrück, , Germany

Site Status RECRUITING

Asklepios Paulinen Klinik Wiesbaden

Wiesbaden, , Germany

Site Status RECRUITING

Countries

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Germany

Central Contacts

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Thomas Rösch, Prof. Dr.

Role: CONTACT

+49 40 7410 ext. 50098

Guido Schachschal, PD Dr.

Role: CONTACT

+49 40 7410 ext. 50814

Facility Contacts

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Albrecht Hoffmeister, Prof. Dr.

Role: primary

+49 341 97 ext. 12240

Marcus Hollenbach, Dr.

Role: backup

Stefan Schubert, Dr.

Role: primary

+49 30 814 59 ext. 190

Peter Amerding, Dr.

Role: backup

+49 30 814 59 ext. 190

Dominik J Kaczmarek, PD Dr.

Role: primary

+49 228-287 ext. 14338

Philipp Zervoulakos, Dr.

Role: primary

+49 221 7712 ext. 4660

Thomas Rösch, Prof. Dr

Role: primary

+49 40 7410 ext. 50098

Guido Schachschal, Dr.

Role: backup

+49 40 7410 ext. 50814

Jochen Weigt, PD Dr.

Role: primary

Oliver Möschler, Dr.

Role: primary

+49 541 326 ext. 4100

Andrea May, Prof. Dr.

Role: primary

+49 611 847 ext. 2331

References

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Reference Type DERIVED
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Other Identifiers

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PV7284

Identifier Type: -

Identifier Source: org_study_id

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