Nationwide Study of Artificial Intelligence in Adenoma Detection for Colonoscopy

NCT ID: NCT05870332

Last Updated: 2024-03-06

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

RECRUITING

Total Enrollment

4000 participants

Study Classification

OBSERVATIONAL

Study Start Date

2023-10-16

Study Completion Date

2025-05-31

Brief Summary

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The goal of this trial is to determine whether use of a Computer Assisted Detection (CADe) programme leads to an increase in ADR for either units or individual colonoscopists, independent of setting or expertise

Detailed Description

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This is a case-control study comparing adenoma detection rate (ADR) in hospitals (and individual colonoscopists), before, during and after use with an artificial intelligence unit called GI Genius™ (GIG). GIG is a Computer-assisted detection (CADe) module that assists the human colonoscopist in real-time, by detecting and marking out polyps during colonoscopy. It has been shown to be effective in expert colonoscopists, but the effect in non-expert, general, colonoscopists is not known.

The investigator wish to deploy GIG into colonoscopy through the UK using a step-wedge design. Sites will be randomly allocated a start date for GIG deployment, collecting data for four months prior to this. In this way, all sites will have the active intervention and will provide their own case-control data. (4 months collection prior to activating GIG, 4 months with GIG, 4 months afterwards without GIG)

The study will concentrate on non-expert colonoscopists, to determine whether GIG can increase ADR. Patients will undergo the same colonoscopy that they would have had in any case, with no additional trial visits or interventions. There will be no alteration to the usual care pathway from the patient's perspective.

If the investigator can prove GIG increases ADR in this way, it will provide support to roll out this technology routinely to improve the quality of colonoscopy nationwide.

Conditions

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Colonic Polyp Colonic Adenoma Colo-rectal Cancer

Study Design

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Observational Model Type

CASE_CONTROL

Study Time Perspective

PROSPECTIVE

Study Groups

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All patients

Patient ≥18 years old, with capacity to consent, scheduled for diagnostic colonoscopy

GI Genius (GIG)

Intervention Type DEVICE

GIG is an artificial intelligence unit that assists human colonoscopist in real-time to detect polyps during colonoscopy.

Four months collection period prior to activating GIG, then four months with GIG, and Four months afterwards without GIG

Interventions

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GI Genius (GIG)

GIG is an artificial intelligence unit that assists human colonoscopist in real-time to detect polyps during colonoscopy.

Four months collection period prior to activating GIG, then four months with GIG, and Four months afterwards without GIG

Intervention Type DEVICE

Other Intervention Names

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CADe

Eligibility Criteria

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Inclusion Criteria

* Any patient aged 18-85 scheduled for colonoscopy by current NHSE / British Society of Gastroenterology criteria

Exclusion Criteria

* Colonoscopy being performed for polyp surveillance
* Unable to provide informed, written consent
Minimum Eligible Age

18 Years

Maximum Eligible Age

85 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes

Sponsors

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Medtronic

INDUSTRY

Sponsor Role collaborator

National Institute for Health Research, United Kingdom

OTHER_GOV

Sponsor Role collaborator

King's College Hospital NHS Trust

OTHER

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Principal Investigators

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Prof Bu'Hussain B Hayee

Role: PRINCIPAL_INVESTIGATOR

King's College Hospital NHS Trust

Locations

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King's College Hospital NHS Foundation Trust

London, , United Kingdom

Site Status RECRUITING

Countries

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United Kingdom

Central Contacts

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Prof Bu'Hussain B Hayee, PHD FRCP

Role: CONTACT

02032996044

Dr Olaolu Olabintan, MBBS MRCP

Role: CONTACT

07939056819

Facility Contacts

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Alena B Marynina

Role: primary

+442032996044

Olaolu Olabintan

Role: backup

07939 056819

Other Identifiers

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292323

Identifier Type: -

Identifier Source: org_study_id

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