Assessing the Additional Neoplasia Yield of Computer-aided Colonoscopy in Follow-up Patients in a Screening Setting

NCT ID: NCT06160466

Last Updated: 2025-08-06

Basic Information

• Recruitment Status: COMPLETED
• Clinical Phase: NA
• Total Enrollment: 1160 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2020-12-16
• Study Completion Date: 2024-05-01

Brief Summary

The goal of this clinical trial is to evaluate the diagnostic yield of CADe in a consecutive population undergoing colonoscopy. The main question it aims to answer is the Adenoma Detection Rate (ADR). Participants undergoing colonoscopy for follow-up in a screening setting will be randomized in a 1:1 ratio to either receive Computer-Aided Detection (CADe) colonoscopy or a conventional colonoscopy (CC). GI Genius is the AI software that will be used in the present trial and is intended to be used as an adjunct to colonic endoscopy procedures to help endoscopists to detect in real time mucosal lesions (such as polyps and adenomas, including those with flat (non-polypoid) morphology) during standard screening and surveillance endoscopic mucosal evaluations. It is not intended to replace histopathological sampling as a means of diagnosis.Researchers will compare the CADe group and the CC-group to see if CAD-e can increase the ADR significantly.

Detailed Description

en if colonoscopy is considered the reference standard for the detection of colonic neoplasia, polyps are still missed. In large administrative cohort or case-control studies, the risk of early post-colonoscopy cancer appeared to be independently predicted by a relatively low polyp/adenoma detection rate. The adenoma detection rate among different endoscopists has been shown to be strictly related with the risk of post-colonoscopy interval cancer. When considering the very high prevalence of advanced neoplasia in the FIT-positive enriched population, the risk of post-colonoscopy interval cancer due to a suboptimal quality of colonoscopy may be substantial. Available evidence justifies therefore the implementation of efforts aimed at improving adenoma detection rate, based on retraining interventions and on the adoption of innovative technologies, designed to enhance the accuracy of the endoscopic examination.Nowadays, Artificial intelligence (AI) is gaining increased attention and investigation, since it seems to improve the quality of medical diagnosis and treatment. In the field of gastrointestinal endoscopy, two potential roles of AI in colonoscopy have been examined so far: automated polyp detection (CADe) and automated polyp histology characterization (CADx). CADe can minimize the probability of missing a polyp during colonoscopy, thereby improving the adenoma detection rate (ADR) and potentially decreasing the incidence of interval cancer. GI Genius is the AI software that will be used in the present trial. The software is developed by Medtronic Inc. (Dublin, Ireland) and is intended to be used as an adjunct to colonic endoscopy procedures to help endoscopists to detect in real time mucosal lesions (such as polyps and adenomas, including those with flat (non-polypoid) morphology) during standard screening and surveillance endoscopic mucosal evaluations. It is not intended to replace histopathological sampling as a means of diagnosis.

The objective of this study was to compare the diagnostic yield obtained by using CADe colonoscopy to the yield obtained by the standard colonoscopy (SC). As the risk of progression is higher for large than for small adenomas the specific contribution of the new technique in reducing the miss rate of large neoplasms represents an important outcome to be assessed in the study. In addition, given the suggested association of a higher miss-rate of serrated and flat lesions with an increased risk of early post-colonoscopy CRC, the benefit of the new technique in reducing the miss rate of these lesions will be assessed.

Conditions

Colonic Adenoma; Adenoma; Polyp of Colon

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: DIAGNOSTIC
• Blinding Strategy: NONE

Study Groups

Artificial intelligence arm Patients undergoing colonoscopy with artificial intelligence.

Patients undergoing colonoscopy with artificial intelligence.

Group Type: EXPERIMENTAL

CADe colonoscopy using GI Genius device

Intervention Type: DEVICE

We wanted to compare the diagnostic yield obtained by using CADe colonoscopy to the yield obtained by the standard colonoscopy (SC) in a follow-up pateints in the screening population.

Conventional Colonoscopy

Standard Colonoscopy with white light

Group Type: ACTIVE_COMPARATOR

White light

Intervention Type: DEVICE

White light endoscopy

Interventions

CADe colonoscopy using GI Genius device

We wanted to compare the diagnostic yield obtained by using CADe colonoscopy to the yield obtained by the standard colonoscopy (SC) in a follow-up pateints in the screening population.

Intervention Type: DEVICE

White light

White light endoscopy

Intervention Type: DEVICE

Eligibility Criteria

Inclusion Criteria

• Patients aged 50 to 74 undergoing colonoscopy examination following a prior colonsocopy were polyps were found (follow-up) performed in the context of a regional mass-screening program.

Exclusion Criteria

• Patients unwilling or unable to give informed consent.
• Patients reporting use of anti-platelet agents or anticoagulants precluding removal of polyps.

• Minimum Eligible Age: 50 Years
• Maximum Eligible Age: 74 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Fondazione Poliambulanza Istituto Ospedaliero

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Cristiano Spada

Role: PRINCIPAL_INVESTIGATOR

Fondazione Poliambulanza

Locations

Fondazione Poliambulanza

Brescia, Bs, Italy

Countries

Italy

Other Identifiers

NP4451

Identifier Type: -

Identifier Source: org_study_id