NCT06623331

Brief Summary

Computer-aided detection (CADe) based on artificial intelligence (AI) may improve colonoscopy quality. An increasing number of young endoscopists are trained in an AI environment. However its impact on trainees' future outcomes remains unclear. The study aimed to evaluate the quality indicators of endoscopists trained in an AI environment compared to those trained conventionally.

Trial Health

87
On Track

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Enrollment
6,000

participants targeted

Target at P75+ for all trials

Timeline
Completed

Started Jan 2022

Typical duration for all trials

Geographic Reach
1 country

1 active site

Status
completed

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

Study Start

First participant enrolled

January 1, 2022

Completed
2.1 years until next milestone

Primary Completion

Last participant's last visit for primary outcome

January 31, 2024

Completed
2 months until next milestone

Study Completion

Last participant's last visit for all outcomes

March 31, 2024

Completed
6 months until next milestone

First Submitted

Initial submission to the registry

September 30, 2024

Completed
2 days until next milestone

First Posted

Study publicly available on registry

October 2, 2024

Completed
Last Updated

January 17, 2025

Status Verified

January 1, 2025

Enrollment Period

2.1 years

First QC Date

September 30, 2024

Last Update Submit

January 15, 2025

Conditions

Quality Indicators, Health CareColonoscopy Diagnostic Techniques and ProceduresArtificial Intelligence (AI)

Keywords

Quality IndicatorsColonoscopyArtificial Intelligence (AI)Computer-aided detection (CADe)Adenoma detection rate (ADR)

Outcome Measures

Primary Outcomes (6)

  • Serrated polyp detection rate (SDR)

    The percentage of colonoscopies when the serrated polyp was found

    During the colonoscopy examination

  • withdrawal time

    The time from the cecal intubation to the end of the examination

    During the colonoscopy examination

  • Cecal intubation rate (CIR)

    The percentage of colonoscopies with successful cecal intubations

    During the colonoscopy examination

  • Adenoma Detection Rate (ADR)

    The percentage of colonoscopies when the adenoma was found

    During the colonoscopy examination

  • Advanced adenoma detection rate (AADR)

    The percentage of colonoscopies when the advanced adenoma (\>10mm) was found

    During the colonoscopy examination

  • Adenoma per colonoscopy score (APC)

    The average number of adenomas detected in a single colonoscopy

    During the colonoscopy examination

Secondary Outcomes (1)

  • Laterally spreading tumor detection rate

    During the colonoscopy examination

Study Arms (2)

Group A

Colonoscopies performed by endoscopists trained in AI-enhanced environment. The quality indicators are measured after completing training without AI enhancement.

Other: AI-enhanced endoscopy training

Group B

Colonoscopies performed by endoscopists trained conventionally. The quality indicators are measured after completing training without AI enhancement.

Other: Conventional endoscopy training

Interventions

AI-enhanced endoscopy trainingOTHER

Endoscopists trained in AI-enhanced environment. Their quality indicators are measured after completing training, without additional AI enhancement.

Group A
Conventional endoscopy trainingOTHER

Endoscopists trained conventionally

Group B

Eligibility Criteria

Age18 Years+
Sexall
Healthy VolunteersNo
Age GroupsAdult (18-64), Older Adult (65+)
Sampling MethodNon-Probability Sample
Study Population

This study included 6,000 adult patients who underwent a colonoscopy for various reasons in a single high-volume endoscopy clinic in Krakow.

You may qualify if:

  • adult participants who underwent a colonoscopy for various reasons performed by specific endoscopists that were assessed in terms of quality indicators

You may not qualify if:

  • a history of bowel resection
  • confirmed inflammatory bowel disease
  • suspicion of polyps or cancer in other imaging tests
  • suspicion of familial adenomatous polyposis

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (1)

Jagiellonian University

Krakow, 31007, Poland

Location

Related Publications (7)

  • Spadaccini M, Iannone A, Maselli R, Badalamenti M, Desai M, Chandrasekar VT, Patel HK, Fugazza A, Pellegatta G, Galtieri PA, Lollo G, Carrara S, Anderloni A, Rex DK, Savevski V, Wallace MB, Bhandari P, Roesch T, Gralnek IM, Sharma P, Hassan C, Repici A. Computer-aided detection versus advanced imaging for detection of colorectal neoplasia: a systematic review and network meta-analysis. Lancet Gastroenterol Hepatol. 2021 Oct;6(10):793-802. doi: 10.1016/S2468-1253(21)00215-6. Epub 2021 Aug 5.

    PMID: 34363763BACKGROUND

  • Barua I, Vinsard DG, Jodal HC, Loberg M, Kalager M, Holme O, Misawa M, Bretthauer M, Mori Y. Artificial intelligence for polyp detection during colonoscopy: a systematic review and meta-analysis. Endoscopy. 2021 Mar;53(3):277-284. doi: 10.1055/a-1201-7165. Epub 2020 Sep 29.

    PMID: 32557490BACKGROUND

  • 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.

    PMID: 32371116BACKGROUND

  • Wang P, Liu P, Glissen Brown JR, Berzin TM, Zhou G, Lei S, Liu X, Li L, Xiao X. Lower Adenoma Miss Rate of Computer-Aided Detection-Assisted Colonoscopy vs Routine White-Light Colonoscopy in a Prospective Tandem Study. Gastroenterology. 2020 Oct;159(4):1252-1261.e5. doi: 10.1053/j.gastro.2020.06.023. Epub 2020 Jun 17.

    PMID: 32562721BACKGROUND

  • Glissen Brown JR, Mansour NM, Wang P, Chuchuca MA, Minchenberg SB, Chandnani M, Liu L, Gross SA, Sengupta N, Berzin TM. Deep Learning Computer-aided Polyp Detection Reduces Adenoma Miss Rate: A United States Multi-center Randomized Tandem Colonoscopy Study (CADeT-CS Trial). Clin Gastroenterol Hepatol. 2022 Jul;20(7):1499-1507.e4. doi: 10.1016/j.cgh.2021.09.009. Epub 2021 Sep 14.

    PMID: 34530161BACKGROUND

  • 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.

    PMID: 24693890BACKGROUND

  • 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.

    PMID: 20463339BACKGROUND

Study Officials

  • Zofia Orzeszko, MD

    Jagiellonian University

    PRINCIPAL INVESTIGATOR

  • Miroslaw Szura, PhD, Prof.

    Jagiellonian University

    STUDY CHAIR

Study Design

Study Type
observational
Observational Model
CASE CONTROL
Time Perspective
RETROSPECTIVE
Sponsor Type
OTHER
Responsible Party
PRINCIPAL INVESTIGATOR
PI Title
Principal Investigator

Study Record Dates

First Submitted

September 30, 2024

First Posted

October 2, 2024

Study Start

January 1, 2022

Primary Completion

January 31, 2024

Study Completion

March 31, 2024

Last Updated

January 17, 2025

Record last verified: 2025-01

Data Sharing

IPD Sharing
Will not share

Locations

PL(1)