Computer Aided Detection of Polyps During Colonoscopy Procedures

NCT ID: NCT04195646

Last Updated: 2021-09-02

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

300 participants

Study Classification

INTERVENTIONAL

Study Start Date

2019-10-01

Study Completion Date

2020-02-28

Brief Summary

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The focus of the study is to evaluate impact on Adenomas Per Colonoscopy (APC) with a Computer Aided Detection (CAD) software assisting the gastroenterologist during a colonoscopy procedure.

Detailed Description

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The details of the proposed study are as follows:

1. Colonoscopy will be performed in the same standard of care manner as if no study was taking place. The video signal from the colonoscope will be fed into a computer running the EndoVigilant CAD software in addition to the standard video output to the procedure monitor. The EndoVigilant CAD software will display an annotated video on an additional monitor that includes both the colonoscopy picture and annotation by the software indicating the location of any polyps on the screen.
2. The endoscopist performing the procedure will therefore be able to observe a standard colonoscopy video on the primary monitor and the annotated video on the second monitor.
3. The endoscopist will primarily rely on the second monitor (augmented with annotation from EndoVigilant ColonCAD software) but the standard procedure monitor will be always operational and available for maneuvers such as fast insertion, polypectomy etc.

Conditions

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Colo-rectal Cancer Polyp of Colon Adenomatous Polyps

Study Design

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Allocation Method

NA

Intervention Model

SINGLE_GROUP

Primary Study Purpose

DEVICE_FEASIBILITY

Blinding Strategy

NONE

Study Groups

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EndoVigilant CAD Software assisted Colonoscopy Procedure

The gastroenterologist performing the colonoscopy procedure will be able to observe a standard colonoscopy video on the primary monitor and video augmented by EndoVigilant CAD software on the second monitor. The gastroenterologist will primarily rely on the second monitor but the standard procedure monitor will be always operational and available for maneuvers such as fast insertion, polypectomy etc.

Group Type EXPERIMENTAL

EndoVigilant CAD Software

Intervention Type DEVICE

The CAD software is deep learning algorithm used to aid in the detection of polyps (abnormal tissue growths in the wall of the colon and adenomas (pre-cancerous growths) during colonoscopy. In its current form, the CAD software is installed on a computer system unit that utilizes an an operating system.

Interventions

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EndoVigilant CAD Software

The CAD software is deep learning algorithm used to aid in the detection of polyps (abnormal tissue growths in the wall of the colon and adenomas (pre-cancerous growths) during colonoscopy. In its current form, the CAD software is installed on a computer system unit that utilizes an an operating system.

Intervention Type DEVICE

Eligibility Criteria

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Inclusion Criteria

* Patient presenting for routine colonoscopy for screening and/or surveillance purposes
* Ability to provide written, informed consent and understand the responsibilities of study participation

Exclusion Criteria

* Patients with diminished cognitive capacity
* Patients with inflammatory bowel disease, ulcerative colitis or Crohn's colitis
* Patients with incomplete colonoscopies (due to technical difficulties or poor bowel prep)
Minimum Eligible Age

18 Years

Maximum Eligible Age

85 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes

Sponsors

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EndoVigilant Inc

INDUSTRY

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Locations

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VA Palo Alto Healthcare

Palo Alto, California, United States

Site Status

Greenbelt Endoscopy Center

Greenbelt, Maryland, United States

Site Status

Countries

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United States

Other Identifiers

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EV-CS-001

Identifier Type: -

Identifier Source: org_study_id

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