The AID Study 2: Artificial Intelligence for Colorectal Adenoma Detection 2
Colonoscopy is clinically used as the gold standard for detection of colon cancer (CRC) and removal of adenomatous polyps. Despite the success of colonoscopy in reducing cancer-related deaths, there exists a disappointing level of adenomas missed at colonoscopy. "Back-to-back" colonoscopies have indicated significant miss rates of 27% for small adenomas (< 5 mm) and 6% for adenomas of more than 10 mm in diameter. Studies performing both CT colonography and colonoscopy estimate that the colonoscopy miss rate for polyps over 10 mm in size may be as high as 12%. The clinical importance of missed lesions should be emphasized because these lesions may ultimately progress to CRC.
Limitations in human visual perception and other human biases such as fatigue, distraction, level of alertness during examination increases such recognition errors and way of mitigating them may be the key to improve polyp detection and further reduction in mortality from CRC. In the past years, a number of CAD systems for detection of polyps from endoscopy images have been described. However, the benefits of traditional CAD technologies in colonoscopy appear to be contradictory, therefore they should be improved to be ultimately considered useful. Recent advances in artificial intelligence (AI), deep learning (DL), and computer vision have shown potential to assist polyp detection during colonoscopy.
Average experienced endoscopists (each having performed <2000 screening colonoscopies) will perform the endoscopic procedure.
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Study Type
Study Type
Enrollment (Actual)
Enrollment
Contacts and Locations
Study Locations
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-
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Como, Italy, 22100
- Ospedale Valduce
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Rome, Italy, 00153
- Digestive Endoscopy Unit, Nuovo Regina Margherita Hospital
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Italia
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Brescia, Italia, Italy
- Fondazione Poliambulanza
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Milano
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Rozzano, Milano, Italy, 20089
- Endoscopy Unit, Humanitas Research Hospital
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Lugano, Switzerland, 6900
- Ente Ospedaliero Cantonale, Ospedale Italiano
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Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Genders Eligible for Study
Sampling Method
Study Population
Based on the observed prevalence of adenomas (35%) among patients undergoing colonoscopies at our center within the last 12 months, a sample size of 322 subjects per arm could allow for a 90% power to show the non-inferiority (primary end-point) of the AI-aided arm by excluding that the one-side 95% CI will exclude a difference of 10% in favour of the standard group. Such sample size will also have a 80% power to detect as statistical significant (α=0.05; two-sided test) a 10% absolute increase in the detection rate of adenomas in the AI-aided arm (secondary end-point).
Performing a sub-stratification according to operator experience, we will planning to enroll the 322 subjects per arm performed by experts and 322 subjects per arm performed by average operator.
Description
Inclusion Criteria:
- All 40-80 years-old subjects undergoing a colonoscopy
Exclusion Criteria:
- subjects with personal history of CRC, or IBD.
- patients with inadequate bowel preparation (defined as Boston Bowel Preparation Scale > 2 in any colonic segment).
- patients with previous colonic resection.
- patients on antithrombotic therapy, precluding polyp resection.
- patients who were not able or refused to give informed written consent.
Study Plan
How is the study designed?
Design Details
- Observational Models: Cohort
- Time Perspectives: Prospective
Number of groups / cohorts
Cohorts and Interventions
Group / CohortGroup / Cohort |
Intervention / TreatmentIntervention / Treatment |
|---|---|
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AI
Artificial intelligence colonoscopy
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Artificial intelligence colonoscopy
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Control
White light colonoscopy
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What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Non-inferiority of AI-aided colonoscopy in terms of ADR
Time Frame: 5 Months
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The proportion of participants with at least one adenoma (per-patient analysis).
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5 Months
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Collaborators and Investigators
Sponsor
Sponsor
Investigators
Investigators
- Principal Investigator: Alessandro Repici, MD, Humanitas Research Hospital
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Actual)
Primary Completion
Study Completion (Actual)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Keywords
Additional Relevant MeSH Terms
Other Study ID Numbers
Other Study ID Numbers
- 2363-2
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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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