Real Life AI in Polyp Detection

NCT ID: NCT04335318

Last Updated: 2021-04-08

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

230 participants

Study Classification

INTERVENTIONAL

Study Start Date

2020-05-01

Study Completion Date

2020-10-01

Brief Summary

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The objective of this study is to compare the polyp detection rate (PDR) of endoscopists unaware of a commercially available artificial intelligence (AI) device for polyp detection during colonoscopy and the PDR of endoscopists with the aid of such a device. Moreover, an extensive characterization of the performance of this device will be done.

Detailed Description

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Recently, there have been remarkable breakthroughs in the introduction of deep learning techniques, especially convolutional neural networks (CNNs), in assisting clinical diagnosis in different medical fields. One of these artificial intelligence (AI) devices to diagnose colon polyps during colonoscopy was launched in October 2019. Its intended use is to work as an adjunct to the endoscopist during a colonoscopy with the purpose of highlighting regions with visual characteristics consistent with different types of mucosal abnormalities.

It is essential to know whether deep learning algorithms can really help endoscopists during colonoscopies. Several studies have already addressed this issue with different approaches and results. However, one common drawback of these type of Machine vs Human retrospective studies is endoscopist bias. It is usually generated because of human natural competitive spirit against machine or human relaxation because of AI-reliance. This can have an effect in the overall results.

The investigators perfomed colonoscopies with the use of a commercially available AI system to detect colonic polyps and recorded them during clinical routine. Additionally from March 2019 - May 2019, 120 colonoscopy videos were performed and captured prospectively without the use of AI.

In this study, the investigators plan to retrospectively compare those two video sets regarding the polyp detection rate, withdrawal time and polyp identification characteristics of the AI system.

Conditions

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Colonic Polyp

Study Design

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Allocation Method

NON_RANDOMIZED

Intervention Model

PARALLEL

Primary Study Purpose

DIAGNOSTIC

Blinding Strategy

NONE

Study Groups

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Colonoscopy with AI-assistance group

Colonoscopies were performed with AI-assistance.

Group Type EXPERIMENTAL

AI-assisted colonoscopy

Intervention Type DEVICE

Colonoscopies performed with assistance of an AI tool that highlights the areas that are susceptible to be a polyp.

Standard Colonoscopy group

Standard clinical procedure

Group Type NO_INTERVENTION

No interventions assigned to this group

Interventions

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AI-assisted colonoscopy

Colonoscopies performed with assistance of an AI tool that highlights the areas that are susceptible to be a polyp.

Intervention Type DEVICE

Eligibility Criteria

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Inclusion Criteria

* Colonoscopies for Polyp detection

Exclusion Criteria

* Colonoscopies for Inflammatory Bowel Disease (IBD).
* Colonoscopies for work up of an active bleeding
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Wuerzburg University Hospital

OTHER

Sponsor Role lead

Responsible Party

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Alexander Hann

Principal Investigator

Responsibility Role PRINCIPAL_INVESTIGATOR

Principal Investigators

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Alexander Hann, PD Dr. Med

Role: PRINCIPAL_INVESTIGATOR

Wuerzburg University Hospital

Locations

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Universitätsklinikum Würzburg

Würzburg, Bavaria, Germany

Site Status

Countries

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Germany

Other Identifiers

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AI01

Identifier Type: -

Identifier Source: org_study_id

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