Impact of AI on Trainee ADR

NCT ID: NCT05423964

Last Updated: 2023-03-06

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

UNKNOWN

Clinical Phase

NA

Total Enrollment

25 participants

Study Classification

INTERVENTIONAL

Study Start Date

2023-01-01

Study Completion Date

2025-09-30

Brief Summary

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Adenoma detection rate (ADR) is a validated quality metric for colonoscopy with higher ADR correlated with improved colorectal cancer outcomes. Artificial intelligence (AI) can automatically detect polyps on the video monitor which may allow endoscopists in training to improve their ADR. Objective and Purpose of the study: Measure the effect of AI in a prospective, randomized manner to determine its impact on ADR of Gastroenterology trainees.

Detailed Description

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Our objective is to determine the impact of AI on the adenoma detection rate of Gastroenterology trainees. The secondary aim of this quality improvement study is to determine the impact of AI based endoscopy on the rate of recording of quality improvement metrics versus historical performance in our program.

Fellows will undergo educational session prior to the start of study, describing commonly used metrics for assessing quality of colonoscopy and how to use the artificial intelligence software. Gastroenterology fellows will be consented for the study prior to initiation. The fellows will be randomized on a daily basis to perform colonoscopies in a room. Outcomes will measure the effects of AI in fellows

Conditions

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Adenoma Adenoma Colon Colorectal Cancer

Study Design

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Allocation Method

RANDOMIZED

Intervention Model

PARALLEL

Primary Study Purpose

DIAGNOSTIC

Blinding Strategy

NONE

Study Groups

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Artificial Intelligence Endoscopy Room

The fellows will be randomized on a daily basis to perform colonoscopies in a room with AI (intervention)

Group Type ACTIVE_COMPARATOR

AI use in Endoscopy Room

Intervention Type DIAGNOSTIC_TEST

The use of AI versus no AI in comparing the detection of adenomas during Endoscopy procedures.

Non-Artificial Intelligence Endoscopy Room

The fellows will be randomized on a daily basis to perform colonoscopies in a non-AI endoscopy room (standard of care).

Group Type ACTIVE_COMPARATOR

Non-AI use Standard of Care endoscopy room

Intervention Type OTHER

Non-AI use in comparing the detection of adenomas during Endoscopy procedures.

Interventions

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AI use in Endoscopy Room

The use of AI versus no AI in comparing the detection of adenomas during Endoscopy procedures.

Intervention Type DIAGNOSTIC_TEST

Non-AI use Standard of Care endoscopy room

Non-AI use in comparing the detection of adenomas during Endoscopy procedures.

Intervention Type OTHER

Eligibility Criteria

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Inclusion Criteria

* All Gastroenterology fellows at USC performing Endoscopies will be included in the study.

Exclusion Criteria

* If fellows refuse informed consent they will be excluded.
* Procedures performed in the intensive care unit or the operating room will not be counted toward the study metrics as the AI system will only be available in the endoscopy unit.
* If procedures are performed only by faculty, in which the fellow is not the primary operator, they will not be used for study metrics.
Eligible Sex

ALL

Accepts Healthy Volunteers

Yes

Sponsors

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University of Southern California

OTHER

Sponsor Role lead

Responsible Party

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James Buxbaum

Associate Professor

Responsibility Role PRINCIPAL_INVESTIGATOR

Principal Investigators

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James L Buxbaum, MD

Role: PRINCIPAL_INVESTIGATOR

University of Southern California

Locations

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LAC+USC Medical Center

Los Angeles, California, United States

Site Status RECRUITING

Countries

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United States

Central Contacts

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Jessica Serna, BS

Role: CONTACT

323-409-6939

Alex Rodriguez, BS

Role: CONTACT

323-409-6939

Facility Contacts

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James Buxbaum, MD

Role: primary

References

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Siegel RL, Miller KD, Fedewa SA, Ahnen DJ, Meester RGS, Barzi A, Jemal A. Colorectal cancer statistics, 2017. CA Cancer J Clin. 2017 May 6;67(3):177-193. doi: 10.3322/caac.21395. Epub 2017 Mar 1.

Reference Type BACKGROUND
PMID: 28248415 (View on PubMed)

Winawer SJ, Zauber AG, Ho MN, O'Brien MJ, Gottlieb LS, Sternberg SS, Waye JD, Schapiro M, Bond JH, Panish JF, et al. Prevention of colorectal cancer by colonoscopic polypectomy. The National Polyp Study Workgroup. N Engl J Med. 1993 Dec 30;329(27):1977-81. doi: 10.1056/NEJM199312303292701.

Reference Type BACKGROUND
PMID: 8247072 (View on PubMed)

Zauber AG, Winawer SJ, O'Brien MJ, Lansdorp-Vogelaar I, van Ballegooijen M, Hankey BF, Shi W, Bond JH, Schapiro M, Panish JF, Stewart ET, Waye JD. Colonoscopic polypectomy and long-term prevention of colorectal-cancer deaths. N Engl J Med. 2012 Feb 23;366(8):687-96. doi: 10.1056/NEJMoa1100370.

Reference Type BACKGROUND
PMID: 22356322 (View on PubMed)

Rex DK, Boland CR, Dominitz JA, Giardiello FM, Johnson DA, Kaltenbach T, Levin TR, Lieberman D, Robertson DJ. Colorectal Cancer Screening: Recommendations for Physicians and Patients from the U.S. Multi-Society Task Force on Colorectal Cancer. Am J Gastroenterol. 2017 Jul;112(7):1016-1030. doi: 10.1038/ajg.2017.174. Epub 2017 Jun 6.

Reference Type BACKGROUND
PMID: 28555630 (View on PubMed)

Corley DA, Jensen CD, Marks AR, Zhao WK, Lee JK, Doubeni CA, Zauber AG, de Boer J, Fireman BH, Schottinger JE, Quinn VP, Ghai NR, Levin TR, Quesenberry CP. Adenoma detection rate and risk of colorectal cancer and death. N Engl J Med. 2014 Apr 3;370(14):1298-306. doi: 10.1056/NEJMoa1309086.

Reference Type BACKGROUND
PMID: 24693890 (View on PubMed)

Abadir AP, Ali MF, Karnes W, Samarasena JB. Artificial Intelligence in Gastrointestinal Endoscopy. Clin Endosc. 2020 Mar;53(2):132-141. doi: 10.5946/ce.2020.038. Epub 2020 Mar 30.

Reference Type BACKGROUND
PMID: 32252506 (View on PubMed)

Urban G, Tripathi P, Alkayali T, Mittal M, Jalali F, Karnes W, Baldi P. Deep Learning Localizes and Identifies Polyps in Real Time With 96% Accuracy in Screening Colonoscopy. Gastroenterology. 2018 Oct;155(4):1069-1078.e8. doi: 10.1053/j.gastro.2018.06.037. Epub 2018 Jun 18.

Reference Type BACKGROUND
PMID: 29928897 (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)

Calderwood AH, Jacobson BC. Comprehensive validation of the Boston Bowel Preparation Scale. Gastrointest Endosc. 2010 Oct;72(4):686-92. doi: 10.1016/j.gie.2010.06.068.

Reference Type BACKGROUND
PMID: 20883845 (View on PubMed)

Kaminski MF, Regula J, Kraszewska E, Polkowski M, Wojciechowska U, Didkowska J, Zwierko M, Rupinski M, Nowacki MP, Butruk E. Quality indicators for colonoscopy and the risk of interval cancer. N Engl J Med. 2010 May 13;362(19):1795-803. doi: 10.1056/NEJMoa0907667.

Reference Type BACKGROUND
PMID: 20463339 (View on PubMed)

Jovani M, Campbell EJ, Hur C, Joshi AD, Nishioka NS. Effect of video monitor size on polyp detection: a prospective, randomized, controlled trial. Gastrointest Endosc. 2019 Aug;90(2):254-258.e2. doi: 10.1016/j.gie.2019.03.1172. Epub 2019 Apr 12.

Reference Type BACKGROUND
PMID: 30986402 (View on PubMed)

Other Identifiers

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HS-21-00094

Identifier Type: -

Identifier Source: org_study_id

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