Artificial Intelligence Aid Systems in Colorectal Adenoma Detection

NCT ID: NCT04945044

Last Updated: 2022-09-21

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

370 participants

Study Classification

INTERVENTIONAL

Study Start Date

2021-11-15

Study Completion Date

2022-01-31

Brief Summary

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

The secondary aims were:

* To evaluate the benefit of Endo-AID 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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Priority guidelines have been established regarding IA 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 in endoscopists with different adenoma detection rates. In addition, there are no studies with the recent CADe Endo-AID system (Olympus Corp. Tokyo).

The main purpose of the study to evaluate the usefulness of the Endo-AID artificial intelligence system in the detection of colorectal adenomas in consecutive patients for outpatient colonoscopy. 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 four groups: CADe system and high ADR endoscopist; CADe system and low ADR endoscopist; Control and high ADR endoscopist; Control and low ADR endoscopist.

For the sample size calculation a 14.4 of difference in favor of the CADe system was considered. Taking onto account an alpha error of 0.05 in a unilateral contrast, a power of 80% and a loss of 10%, 165 patients per group would be required.

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)

Tis system can detect in the screen suspicion areas of adenomatous polyps. This is an additional help for the endoscopist for the detection of lesions

Group Type EXPERIMENTAL

Computed adenoma detection system (CADe)

Intervention Type DEVICE

This is a computed system that helps the endoscopist to increase the detection of colorectal polyps

Control group (absence of CADe)

This is the control group. As in the routine colonoscopy the endoscopist is in charge of the detection of the lesions.

Group Type ACTIVE_COMPARATOR

Control group (regular colonoscopy)

Intervention Type BEHAVIORAL

It is exclusively the endoscopist in charge of the detection of the polyps (usual practice)

Interventions

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Computed adenoma detection system (CADe)

This is a computed system that helps the endoscopist to increase the detection of colorectal polyps

Intervention Type DEVICE

Control group (regular colonoscopy)

It is exclusively the endoscopist in charge of the detection of the polyps (usual practice)

Intervention Type BEHAVIORAL

Eligibility Criteria

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Inclusion Criteria

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

Exclusion Criteria

* Colonic resection
* Taking anticoagulants or antiagregants that contraindicate the performance of therapy
* Patients with a recent colonoscopy (\<6 months) of good quality (e.g. cited for endoscopic therapy)
* Inflammatory bowel disease
* 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
* Refusal to participate in the study.
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 Gimeno Garcia, MD, PhD

Role: PRINCIPAL_INVESTIGATOR

Hospital Universitario de Canarias

Locations

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Department of Gastroenterology

San Cristóbal de La Laguna, S/C de Tenerife, Spain

Site Status

Countries

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Spain

References

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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)

Berzin TM, Parasa S, Wallace MB, Gross SA, Repici A, Sharma P. Position statement on priorities for artificial intelligence in GI endoscopy: a report by the ASGE Task Force. Gastrointest Endosc. 2020 Oct;92(4):951-959. doi: 10.1016/j.gie.2020.06.035. Epub 2020 Jun 19.

Reference Type RESULT
PMID: 32565188 (View on PubMed)

Wang P, Liu X, Berzin TM, Glissen Brown JR, Liu P, Zhou C, Lei L, Li L, Guo Z, Lei S, Xiong F, Wang H, Song Y, Pan Y, Zhou G. Effect of a deep-learning computer-aided detection system on adenoma detection during colonoscopy (CADe-DB trial): a double-blind randomised study. Lancet Gastroenterol Hepatol. 2020 Apr;5(4):343-351. doi: 10.1016/S2468-1253(19)30411-X. Epub 2020 Jan 22.

Reference Type RESULT
PMID: 31981517 (View on PubMed)

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.

Reference Type RESULT
PMID: 32562721 (View on PubMed)

Gimeno-Garcia AZ, Hernandez Negrin D, Hernandez A, Nicolas-Perez D, Rodriguez E, Montesdeoca C, Alarcon O, Romero R, Baute Dorta JL, Cedres Y, Castillo RD, Jimenez A, Felipe V, Morales D, Ortega J, Reygosa C, Quintero E, Hernandez-Guerra M. Usefulness of a novel computer-aided detection system for colorectal neoplasia: a randomized controlled trial. Gastrointest Endosc. 2023 Mar;97(3):528-536.e1. doi: 10.1016/j.gie.2022.09.029. Epub 2022 Oct 11.

Reference Type DERIVED
PMID: 36228695 (View on PubMed)

Other Identifiers

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Computer aid adenoma detection

Identifier Type: -

Identifier Source: org_study_id

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