Computer Assisted Detection of Neoplasia During Colonoscopy Evaluation

NCT ID: NCT05888623

Last Updated: 2025-10-14

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

Total Enrollment

334200 participants

Study Classification

OBSERVATIONAL

Study Start Date

2022-10-01

Study Completion Date

2023-12-31

Brief Summary

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The goal of this cluster randomized study is to determine if artificial intelligence systems used during colonoscopy can improve the detection of precancerous polyps in the colon. The primary question it aims to answer is whether computer-assisted detection devices improve the proportion of colonoscopies found to have precancerous adenomatous polyps.

Secondary aims will assess if computer-assisted detection devices improve the proportion of colonoscopies found to other types of precancerous polyps known as sessile serrated lesions, or cancer of the colon and rectum. The study will also assess possible negative effects of use of computer-assisted detection (e.g., prolonging the procedure time or false-positive biopsies) and survey device users to learn about their experience with this technology.

The study team will provide computer-assisted detection devices to randomly chosen VA medical centers for use during colonoscopy and compare colonoscopy findings for patients who undergo colonoscopy at facilities that are equipped with these devices to the findings of patients who undergo colonoscopy at VA facilities that do not have these devices.

A survey will be distributed to physicians who perform colonoscopy to assess their experience using computer-assisted detection devices.

Detailed Description

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Conditions

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Colorectal Neoplasms

Study Design

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Observational Model Type

OTHER

Study Time Perspective

PROSPECTIVE

Study Groups

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Computer Assisted Detection

Colonoscopies performed at a VA facility with computer assisted detection (CADe) artificial intelligence available.

Computer Assisted Detection

Intervention Type DEVICE

Computer-assisted polyp detection system that utilizes artificial intelligence (AI) during colonoscopy

Conventional Colonoscopy

Colonoscopies performed at a VA facility without CADe artificial intelligence available

No interventions assigned to this group

Interventions

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Computer Assisted Detection

Computer-assisted polyp detection system that utilizes artificial intelligence (AI) during colonoscopy

Intervention Type DEVICE

Other Intervention Names

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GI Genius Intelligent Endoscopy Module (Medtronic)

Eligibility Criteria

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Inclusion Criteria

* Colonoscopy performed at a Veterans Affairs (VA) medical center

Exclusion Criteria

* Colonoscopy performed at VA medical centers that acquired computer-assisted detection artificial intelligence devices through non-random assignment
* Colonoscopy performed at a VA medical center where pathology results are not available
Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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VA Salt Lake City Health Care System

FED

Sponsor Role collaborator

San Francisco Veterans Affairs Medical Center

FED

Sponsor Role collaborator

VA Puget Sound Health Care System

FED

Sponsor Role lead

Responsible Party

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Jason A. Dominitz, MD, MHS

Executive Director, National Gastroenterology and Hepatology Program

Responsibility Role PRINCIPAL_INVESTIGATOR

Principal Investigators

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Jason A. Dominitz, MD, MHS

Role: STUDY_DIRECTOR

US Department of Veterans Affairs

Locations

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VA Puget Sound Health Care System

Seattle, Washington, United States

Site Status

Countries

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United States

References

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Levy I, Bruckmayer L, Klang E, Ben-Horin S, Kopylov U. Artificial Intelligence-Aided Colonoscopy Does Not Increase Adenoma Detection Rate in Routine Clinical Practice. Am J Gastroenterol. 2022 Nov 1;117(11):1871-1873. doi: 10.14309/ajg.0000000000001970. Epub 2022 Aug 23.

Reference Type BACKGROUND
PMID: 36001408 (View on PubMed)

Ladabaum U, Shepard J, Weng Y, Desai M, Singer SJ, Mannalithara A. Computer-aided Detection of Polyps Does Not Improve Colonoscopist Performance in a Pragmatic Implementation Trial. Gastroenterology. 2023 Mar;164(3):481-483.e6. doi: 10.1053/j.gastro.2022.12.004. Epub 2022 Dec 15. No abstract available.

Reference Type BACKGROUND
PMID: 36528131 (View on PubMed)

Wallace MB, Sharma P, Bhandari P, East J, Antonelli G, Lorenzetti R, Vieth M, Speranza I, Spadaccini M, Desai M, Lukens FJ, Babameto G, Batista D, Singh D, Palmer W, Ramirez F, Palmer R, Lunsford T, Ruff K, Bird-Liebermann E, Ciofoaia V, Arndtz S, Cangemi D, Puddick K, Derfus G, Johal AS, Barawi M, Longo L, Moro L, Repici A, Hassan C. Impact of Artificial Intelligence on Miss Rate of Colorectal Neoplasia. Gastroenterology. 2022 Jul;163(1):295-304.e5. doi: 10.1053/j.gastro.2022.03.007. Epub 2022 Mar 15.

Reference Type BACKGROUND
PMID: 35304117 (View on PubMed)

Repici A, Badalamenti M, Maselli R, Correale L, Radaelli F, Rondonotti E, Ferrara E, Spadaccini M, Alkandari A, Fugazza A, Anderloni A, Galtieri PA, Pellegatta G, Carrara S, Di Leo M, Craviotto V, Lamonaca L, Lorenzetti R, Andrealli A, Antonelli G, Wallace M, Sharma P, Rosch T, Hassan C. Efficacy of Real-Time Computer-Aided Detection of Colorectal Neoplasia in a Randomized Trial. Gastroenterology. 2020 Aug;159(2):512-520.e7. doi: 10.1053/j.gastro.2020.04.062. Epub 2020 May 1.

Reference Type BACKGROUND
PMID: 32371116 (View on PubMed)

Hassan C, Spadaccini M, Iannone A, Maselli R, Jovani M, Chandrasekar VT, Antonelli G, Yu H, Areia M, Dinis-Ribeiro M, Bhandari P, Sharma P, Rex DK, Rosch T, Wallace M, Repici A. Performance of artificial intelligence in colonoscopy for adenoma and polyp detection: a systematic review and meta-analysis. Gastrointest Endosc. 2021 Jan;93(1):77-85.e6. doi: 10.1016/j.gie.2020.06.059. Epub 2020 Jun 26.

Reference Type BACKGROUND
PMID: 32598963 (View on PubMed)

Gawron AJ, Yao Y, Gupta S, Cole G, Whooley MA, Dominitz JA, Kaltenbach T. Simplifying Measurement of Adenoma Detection Rates for Colonoscopy. Dig Dis Sci. 2021 Sep;66(9):3149-3155. doi: 10.1007/s10620-020-06627-2. Epub 2020 Oct 8.

Reference Type BACKGROUND
PMID: 33029706 (View on PubMed)

Provided Documents

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Document Type: Study Protocol and Statistical Analysis Plan

View Document

Other Identifiers

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VHA_NGHP_001

Identifier Type: -

Identifier Source: org_study_id

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