Improving Polyp Detection Rate by Artificial Intelligence in Colonoscopy

NCT ID: NCT05322993

Last Updated: 2026-01-22

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

COMPLETED

Clinical Phase

NA

Total Enrollment

5034 participants

Study Classification

INTERVENTIONAL

Study Start Date

2021-11-19

Study Completion Date

2025-06-30

Brief Summary

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The aim of this study is to investigate if the use of artificial intelligence (AI) in colonoscopy improves the polyp detection rate, and if the use of AI has a learning effect.

Detailed Description

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The endoscopists will use GI Genius from Medtronic, a device that uses artificial intelligence (AI) based on machine learning to detect polyps in the colon in real time during colonoscopy. The device interprets the endoscopy pictures and superimposes possible polyps with frames.

The patients will be included in regular outpatient clinics in Western Norway. The endoscopists will be divided into groups depending on their experience. The endoscopists will perform colonoscopies in three phases; (1) before the use of AI, (2) during the use of AI and (3) after the use of AI. The investigators will then evaluate the polyp detection rate (PDR) in the three phases to see if AI increases PDR, and if there is a learning effect on PDR after the use of AI. The investigators will also evaluate if there is a difference in the learning-effect from AI-use depending on if the endoscopist is experienced or inexperienced.

The PDR's are registered as part of Norway's national quality register of colonoscopy, Gastronet. The data registered in Gastronet can also help the investigators evaluate other outcomes such as withdrawal time, bowel preparation, patient reported pain, patient satisfaction and complications.

Conditions

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Colonic Polyp

Study Design

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Allocation Method

NON_RANDOMIZED

Intervention Model

CROSSOVER

Endoscopists will perform colonoscopies in three consecutive phases; 1. without artificial intelligence (AI), 2. with AI, 3. without (AI).
Primary Study Purpose

DIAGNOSTIC

Blinding Strategy

NONE

Study Groups

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With artificial intelligence (AI)

Use of GI Genius artificial intelligence device during colonoscopy.

Group Type ACTIVE_COMPARATOR

GI Genius

Intervention Type DEVICE

The use of artificial intelligence during colonoscopy to improve polyp detection.

Without artificial intelligence

Use of standard colonoscopy equipment without GI Genius.

Group Type ACTIVE_COMPARATOR

Standard white light colonoscope

Intervention Type DEVICE

Standard colonoscopy.

Interventions

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GI Genius

The use of artificial intelligence during colonoscopy to improve polyp detection.

Intervention Type DEVICE

Standard white light colonoscope

Standard colonoscopy.

Intervention Type DEVICE

Eligibility Criteria

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Inclusion Criteria

* Patients coming to outpatient clinics to perform colonoscopies

Exclusion Criteria

* Total colectomy
* Reservation against registration in Gastronet, the national quality register for colonoscopy in Norway
Eligible Sex

ALL

Accepts Healthy Volunteers

Yes

Sponsors

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Haukeland University Hospital

OTHER

Sponsor Role collaborator

European Society of Gastrointestinal Endoscopy

OTHER

Sponsor Role collaborator

Kanalspesialistene AS

UNKNOWN

Sponsor Role collaborator

Haraldsplass Deaconess Hospital

OTHER

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Principal Investigators

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Roald F. Havre, Professor

Role: STUDY_DIRECTOR

Helse Bergen

Locations

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Haraldsplass Deaconess Hospital

Bergen, Vestland, Norway

Site Status

Haukeland University Hospital

Bergen, Vestland, Norway

Site Status

Kanalspesialistene AS

Bergen, Vestland, Norway

Site Status

Countries

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Norway

Related Links

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https://www.sthf.no/gastronet

Gastronet, the national quality register of colonoscopies

Other Identifiers

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REK 275068

Identifier Type: -

Identifier Source: org_study_id

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