Clinical trial NCT04894708: Study on the Use of Artificial Intelligence (Fujifilm) for Polyp Detection in Colonoscopy
Official title: Prospective Randomized Study on the Use of Artificial Intelligence (Fujifilm) for Polyp Detection in Colonoscopy
Study type: INTERVENTIONAL
Overall status: UNKNOWN
Phase: NA
Conditions: Screening Colonoscopy
Colonoscopic Control After Polypectomy
Suspected Colon Polyps
Lead sponsor: Universitätsklinikum Hamburg-Eppendorf
Sponsor class: OTHER
Collaborators: name: FUJIFILM Deutschland, Branch office of FUJIFILM Europe GmbH
First posted: 2021-05-20
Enrollment: 1572
Sex: ALL
Minimum age (years): 35
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: RANDOMIZED
Intervention model: PARALLEL
Primary purpose: DIAGNOSTIC

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.

Eligibility criteria: 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

Primary outcomes:
measure: Adenoma detection rate
timeFrame: during procedure to histological examination result, approximately 2 days
Secondary outcomes:
measure: Patient rate difference
timeFrame: during procedure to histological examination result, approximately 2 days
measure: Adenoma subgroup differences
timeFrame: histological examination result, approximately 2 days
measure: rate of hyperplastic polyp detection in both groups
timeFrame: histological examination result, approximately 2 days
measure: rate of polyp detection in preventive and diagnostic colonoscopy
timeFrame: during procedure to histological examination result, approximately 2 days
measure: Switching number (BLI, LCI) in both groups
timeFrame: during procedure
measure: incidence of reasons for switching to BLI/LCI
timeFrame: during procedure
measure: quality of polyp detection rate by image evaluation
timeFrame: until 2 months after recruitment stop
Other outcomes:
Locations:
city: Kassel
country: Germany
facility: Gastroenterologiepraxis Dr. Moog
state: Hessen
city: Leipzig
country: Germany
facility: Universitätsklinikum Leipzig
state: Sachsen
city: Bad Salzuflen
country: Germany
facility: GastroZentrum Lippe
city: Berlin
country: Germany
facility: Gastroenterologie am Bayerischen Platz
city: Bonn
country: Germany
facility: University Hospital Bonn
city: Hamburg
country: Germany
facility: University Hospital Eppendorf
city: Köln
country: Germany
facility: St. Vinzenz-Hospital / Akademisches Lehrkrankenhaus der Universität zu Köln
city: Magdeburg
country: Germany
facility: University Hospital Magdeburg
city: Osnabrück
country: Germany
facility: Marienhospital Osnabrück
city: Wiesbaden
country: Germany
facility: Asklepios Paulinen Klinik Wiesbaden
Study officials:
affiliation: Universitätsklinikum Hamburg-Eppendorf
name: Thomas Rösch, Prof. dr.
role: PRINCIPAL_INVESTIGATOR
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