Artificial Intelligence Aid Systems and Endocuff in Colorectal Adenoma Detection

NCT ID: NCT05141773

Last Updated: 2025-07-24

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Clinical Phase

NA

Total Enrollment

696 participants

Study Classification

INTERVENTIONAL

Study Start Date

2022-11-01

Study Completion Date

2023-04-30

Brief Summary

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The main purpose of the study to evaluate the usefulness of the Endo-AID artificial intelligence system combined with endocuff compared with endocuff in the detection of colorectal adenomas in consecutive patients for outpatient colonoscopy.

The secondary aims were:

* To evaluate the benefit of Endo-AID and endocuff in adenoma detection rate by comparing endoscopists with high and low adenoma detection rate.
* To evaluate serrated detection rate, advanced adenoma detection rate, adenoma detection rate according to the size (\<= 5mm, 6-9mm,\> = 10mm) and number of adenomas by colonoscopy. Stratification by location and morphology.

Detailed Description

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Guidelines have been established regarding artificial intelligence (AI) applied to gastrointestinal endoscopy. Regarding the priority uses for their development, there are applications that improve vision, placing computer-assisted lesion detection (CADe) as one of the most necessary priorities, given the importance of colorectal cancer screening (CRC) and post-polypectomy surveillance. The evaluation of these systems in different clinical practices and patient groups has been recommend. In this regard, studies in the western population are limited and have been carried out by expert endoscopists. It has not been evaluated comparing with other strategies such as add-on devices. In addition, there are no studies with the recent CADe Endo-AID system (Olympus Corp. Tokyo).

The main purpose of the study is to evaluate the usefulness of the Endo-AID artificial intelligence system with endocuff in the detection of colorectal adenomas in consecutive patients for outpatient colonoscopy compared with standard colonoscopy with endocuff. In addition, the benefit of the CADe system will be assessed according to the endoscopist ADR.

A randomized controlled trial will be carried out in consecutive outpatients meeting the inclusion criteria and none of the exclusion criteria. Patients with be randomized to one of the two groups: CADe system with endocuff and standard colonoscopy with endocuff.

Conditions

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Adenoma Detection Rate

Study Design

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Allocation Method

RANDOMIZED

Intervention Model

PARALLEL

Primary Study Purpose

DIAGNOSTIC

Blinding Strategy

NONE

Study Groups

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Computed adenoma detection system (CADe) and Endocuff

CADe system can detect in the screen suspicion areas of adenomatous polyps. This is an additional help for the endoscopist for the detection of lesions. Endocuff increases the colonic surface examinated

Group Type EXPERIMENTAL

Computed adenoma detection system (CADe) plus endocuff

Intervention Type DEVICE

This is a computed system that helps the endoscopist to increase the detection of colorectal polyps. An add-on device (Endocuff) is also incorporated to the tip of the colonoscope

Control group (Endocuff)

Endocuff increases the colonic surface examinated

Group Type ACTIVE_COMPARATOR

Endocuff

Intervention Type DEVICE

An add-on device (Endocuff) is also incorporated to the tip of the colonoscope

Interventions

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Computed adenoma detection system (CADe) plus endocuff

This is a computed system that helps the endoscopist to increase the detection of colorectal polyps. An add-on device (Endocuff) is also incorporated to the tip of the colonoscope

Intervention Type DEVICE

Endocuff

An add-on device (Endocuff) is also incorporated to the tip of the colonoscope

Intervention Type DEVICE

Eligibility Criteria

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Inclusion Criteria

* Age ≥ 18 years.
* Patients referred for outpatient colonoscopy

Exclusion Criteria

* Colonic resection
* Taking anticoagulants or antiaggregants that contraindicate the performance of therapy
* Patients with a recent colonoscopy (\<6 months) of good quality (e.g. cited for endoscopic therapy)
* IBD
* Patients with incomplete colonoscopy
* Patients with inadequate preparation using the Boston Colonic Preparation Scale (BBPS). A cleaning quality of less than 2 points in any of the 3 colonic sections will be considered inadequate.
* Patients with polyposis syndromes
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Hospital Universitario de Canarias

OTHER

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Principal Investigators

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Antonio Z García, MD, PhD

Role: PRINCIPAL_INVESTIGATOR

Hospital Universitario de Canarias

Locations

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Hospital Universitario de Canarias

San Cristóbal de La Laguna, SANTA CRUZ DE TENERIFE, Spain

Site Status

Countries

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Spain

References

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Ngu WS, Bevan R, Tsiamoulos ZP, Bassett P, Hoare Z, Rutter MD, Clifford G, Totton N, Lee TJ, Ramadas A, Silcock JG, Painter J, Neilson LJ, Saunders BP, Rees CJ. Improved adenoma detection with Endocuff Vision: the ADENOMA randomised controlled trial. Gut. 2019 Feb;68(2):280-288. doi: 10.1136/gutjnl-2017-314889. Epub 2018 Jan 23.

Reference Type BACKGROUND
PMID: 29363535 (View on PubMed)

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.

Reference Type BACKGROUND
PMID: 32371116 (View on PubMed)

Aziz M, Haghbin H, Gangwani MK, Sharma S, Nawras Y, Khan Z, Chandan S, Mohan BP, Lee-Smith W, Nawras A. Efficacy of Endocuff Vision compared to first-generation Endocuff in adenoma detection rate and polyp detection rate in high-definition colonoscopy: a systematic review and network meta-analysis. Endosc Int Open. 2021 Jan;9(1):E41-E50. doi: 10.1055/a-1293-7327. Epub 2021 Jan 1.

Reference Type BACKGROUND
PMID: 33403235 (View on PubMed)

Ashat M, Klair JS, Singh D, Murali AR, Krishnamoorthi R. Impact of real-time use of artificial intelligence in improving adenoma detection during colonoscopy: A systematic review and meta-analysis. Endosc Int Open. 2021 Apr;9(4):E513-E521. doi: 10.1055/a-1341-0457. Epub 2021 Mar 17.

Reference Type BACKGROUND
PMID: 33816771 (View on PubMed)

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.

Reference Type BACKGROUND
PMID: 32557490 (View on PubMed)

Hassan C, Spadaccini M, Iannone A, Maselli R, Jovani M, Chandrasekar VT, Antonelli G, Yu H, Areia M, Dinis-Ribeiro M, Bhandari P, Sharma P, Rex DK, Rosch T, Wallace M, Repici A. Performance of artificial intelligence in colonoscopy for adenoma and polyp detection: a systematic review and meta-analysis. Gastrointest Endosc. 2021 Jan;93(1):77-85.e6. doi: 10.1016/j.gie.2020.06.059. Epub 2020 Jun 26.

Reference Type BACKGROUND
PMID: 32598963 (View on PubMed)

Other Identifiers

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Computer aid colonoscopy

Identifier Type: -

Identifier Source: org_study_id

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