- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT06623331
The Implementation of Computer-aided Detection in Training Improves the Quality of Future Colonoscopies
January 15, 2025 updated by: Zofia Orzeszko, Jagiellonian University
The Implementation of Computer-aided Detection in an Initial Endoscopy Training Improves the Quality Measures of Trainees' Future Colonoscopies
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.
Study Overview
Status
Completed
Conditions
Intervention / Treatment
Detailed Description
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.
A study included 6,000 adult patients who underwent a colonoscopy for various reasons.
The study retrospectively evaluated the first 1,000 procedures performed by six endoscopists after completing training relying entirely on endoscopists' detection skills without AI enhancement.
Three of those young endoscopists were trained with CADe, and three without additional assistance.
Quality indicators were assessed in both groups.
The morphology of detected polyps was evaluated to determine the influence of AI-enhanced training on laterally spreading tumors (LST) detection rate.
Study Type
Observational
Enrollment (Actual)
6000
Contacts and Locations
This section provides the contact details for those conducting the study, and information on where this study is being conducted.
Study Locations
-
-
-
Kraków, Poland, 31007
- Jagiellonian University
-
-
Participation Criteria
Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
No
Sampling Method
Non-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.
Description
Inclusion Criteria:
- adult participants who underwent a colonoscopy for various reasons performed by specific endoscopists that were assessed in terms of quality indicators
Exclusion Criteria:
- a history of bowel resection
- confirmed inflammatory bowel disease
- suspicion of polyps or cancer in other imaging tests
- suspicion of familial adenomatous polyposis
Study Plan
This section provides details of the study plan, including how the study is designed and what the study is measuring.
How is the study designed?
Design Details
Cohorts and Interventions
Group / Cohort |
Intervention / Treatment |
|---|---|
|
Group A
Colonoscopies performed by endoscopists trained in AI-enhanced environment.
The quality indicators are measured after completing training without AI enhancement.
|
Endoscopists trained in AI-enhanced environment.
Their quality indicators are measured after completing training, without additional AI enhancement.
|
|
Group B
Colonoscopies performed by endoscopists trained conventionally.
The quality indicators are measured after completing training without AI enhancement.
|
Endoscopists trained conventionally
|
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
withdrawal time
Time Frame: During the colonoscopy examination
|
The time from the cecal intubation to the end of the examination
|
During the colonoscopy examination
|
|
Cecal intubation rate (CIR)
Time Frame: During the colonoscopy examination
|
The percentage of colonoscopies with successful cecal intubations
|
During the colonoscopy examination
|
|
Adenoma Detection Rate (ADR)
Time Frame: During the colonoscopy examination
|
The percentage of colonoscopies when the adenoma was found
|
During the colonoscopy examination
|
|
Advanced adenoma detection rate (AADR)
Time Frame: During the colonoscopy examination
|
The percentage of colonoscopies when the advanced adenoma (>10mm) was found
|
During the colonoscopy examination
|
|
Adenoma per colonoscopy score (APC)
Time Frame: During the colonoscopy examination
|
The average number of adenomas detected in a single colonoscopy
|
During the colonoscopy examination
|
|
Serrated polyp detection rate (SDR)
Time Frame: During the colonoscopy examination
|
The percentage of colonoscopies when the serrated polyp was found
|
During the colonoscopy examination
|
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Laterally spreading tumor detection rate
Time Frame: During the colonoscopy examination
|
The percentage of colonoscopies when the laterally spreading tumor lesion was found
|
During the colonoscopy examination
|
Collaborators and Investigators
This is where you will find people and organizations involved with this study.
Sponsor
Investigators
- Principal Investigator: Zofia Orzeszko, MD, Jagiellonian University
- Study Chair: Miroslaw Szura, PhD, Prof., Jagiellonian University
Publications and helpful links
The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.
General Publications
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
Study record dates
These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.
Study Major Dates
Study Start (Actual)
January 1, 2022
Primary Completion (Actual)
January 31, 2024
Study Completion (Actual)
March 31, 2024
Study Registration Dates
First Submitted
September 30, 2024
First Submitted That Met QC Criteria
October 1, 2024
First Posted (Actual)
October 2, 2024
Study Record Updates
Last Update Posted (Actual)
March 25, 2025
Last Update Submitted That Met QC Criteria
January 15, 2025
Last Verified
January 1, 2025
More Information
Terms related to this study
Keywords
Other Study ID Numbers
- 2024.000.367
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
NO
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
No
Studies a U.S. FDA-regulated device product
No
product manufactured in and exported from the U.S.
No
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
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