Clinical trial NCT06786793: Artificial Intelligence in Colonoscopy
Official title: Artificial Intelligence in Endoscopic Diagnosis of Colorectal Polyps: A Prospective Randomized Study.
Study type: INTERVENTIONAL
Overall status: RECRUITING
Phase: NA
Conditions: Quality Indicators, Health Care
Artificial Intelligence (AI)
Colonoscopy Diagnostic Techniques and Procedures
Keywords: Quality Indicatiors
Colonoscopy
Artificial Intelligence (AI)
Computer-aided Detection (CADe)
Adenoma detection rate (ADR)
Lead sponsor: Jagiellonian University
Sponsor class: OTHER
First posted: 2025-01-22
Enrollment: 630
Sex: ALL
Minimum age (years): 50
Maximum age (years): 65
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: RANDOMIZED
Intervention model: PARALLEL
Primary purpose: DIAGNOSTIC

Brief summary: Colorectal cancer is the second most common malignancy in the countries of the European Union. Colonoscopy is the primary method for detecting and preventing the development of colorectal cancer is endoscopic examination. This study aims to evaluate the impact of artificial intelligence on the detection rate of polyps and early stages of colorectal cancer.

Detailed description: Colorectal cancer is the second most common malignancy in the countries of the European Union. The primary method for detecting and preventing the development of colorectal cancer is endoscopic examination-colonoscopy, during which precancerous lesions such as adenomas and serrated polyps can be removed. The effectiveness of colonoscopy depends on the adenoma detection rate, which varies among endoscopists and is influenced by their skills and experience. It has been proven that high-quality colonoscopy prevents the omission of colorectal cancer, which might develop in the future as so-called interval cancer. A breakthrough in machine learning in recent years has enabled the development of commercial artificial intelligence systems. These systems aim to improve the detection rates of precancerous polyps and, consequently, potentially reduce the risk of developing colorectal cancer. Artificial intelligence is also expected to help standardize performance across endoscopic procedures of varying quality, thereby contributing to a reduction in colorectal cancer incidence in the future. This study aims to evaluate the impact of artificial intelligence on the detection rate of polyps and early stages of colorectal cancer.

Eligibility criteria: Inclusion Criteria:

* Consent to participate in the study,
* Age between 50 and 65 years,
* Scheduled outpatient colonoscopy.

Exclusion Criteria:

* Previous colonoscopy,
* History of colorectal surgery,
* Ongoing biological therapy for any indication,
* Primary sclerosing cholangitis,
* Familial polyposis syndrome,
* Chronic diarrhea,
* Ulcerative colitis,
* Crohn's disease.

Primary outcomes:
measure: Adenoma detection rate (ADR)
timeFrame: During the colonoscopy examination
Secondary outcomes:
measure: Utility of artificial intelligence for both novice and experienced endoscopists
timeFrame: During the colonoscopy examination
measure: Assessing the morphology of polyps detected during colonoscopy
timeFrame: During the colonoscopy examination
measure: Cost analysis of procedures performed with the use of artificial intelligence
timeFrame: Through study completion, an average of 6 months
Other outcomes:
Locations:
city: Krakow
country: Poland
facility: MEDICINA Medical Center
state: Lesser Poladn
city: Krakow
country: Poland
facility: Brothers Hospitallers Medical Center, Hospital of St John of god in Krakow
state: Lesser Polasd
Study officials:
affiliation: Jagiellonian University in Krakow
name: Miroslaw Szura, Prof.
role: STUDY_CHAIR
affiliation: Jagiellonian University in Krakow
name: Zofia Orzeszko, MD
role: PRINCIPAL_INVESTIGATOR
References:
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citation: van Doorn SC, Klanderman RB, Hazewinkel Y, Fockens P, Dekker E. Adenoma detection rate varies greatly during colonoscopy training. Gastrointest Endosc. 2015 Jul;82(1):122-9. doi: 10.1016/j.gie.2014.12.038. Epub 2015 Mar 24.
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citation: 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.
citation: 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.
citation: 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.