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Artificial Intelligence Aid Systems in Colorectal Adenoma Detection (INTELAID)

Primary Purpose

Adenoma Detection Rate

Status
Completed
Phase
Not Applicable
Locations
Spain
Study Type
Interventional
Intervention
Computed adenoma detection system (CADe)
Control group (regular colonoscopy)
Sponsored by
Hospital Universitario de Canarias
About
Eligibility
Locations
Arms
Outcomes
Full info

About this trial

This is an interventional diagnostic trial for Adenoma Detection Rate

Eligibility Criteria

18 Years - undefined (Adult, Older Adult)All SexesDoes not accept healthy volunteers

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.

Sites / Locations

  • Department of Gastroenterology

Arms of the Study

Arm 1

Arm 2

Arm Type

Experimental

Active Comparator

Arm Label

Computed adenoma detection system (CADe)

Control group (absence of CADe)

Arm Description

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

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

Outcomes

Primary Outcome Measures

Adenoma detection rate
Number of colonoscopies with colorectal adenoma/Number of total colonoscopies

Secondary Outcome Measures

Serrated detection rate
Number of colonoscopies with serrated adenoma/Number of total colonoscopies
Advanced adenoma detection rate
Number of colonoscopies with advanced adenoma/Number of total colonoscopies

Full Information

First Posted
June 22, 2021
Last Updated
September 19, 2022
Sponsor
Hospital Universitario de Canarias
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1. Study Identification

Unique Protocol Identification Number
NCT04945044
Brief Title
Artificial Intelligence Aid Systems in Colorectal Adenoma Detection
Acronym
INTELAID
Official Title
Usefulness of the Endo-AID Artificial Intelligence System in the Detection of Colorectal Adenomas. a Randomized Controlled Trial
Study Type
Interventional

2. Study Status

Record Verification Date
September 2022
Overall Recruitment Status
Completed
Study Start Date
November 15, 2021 (Actual)
Primary Completion Date
January 31, 2022 (Actual)
Study Completion Date
January 31, 2022 (Actual)

3. Sponsor/Collaborators

Responsible Party, by Official Title
Sponsor
Name of the Sponsor
Hospital Universitario de Canarias

4. Oversight

Studies a U.S. FDA-regulated Drug Product
No
Studies a U.S. FDA-regulated Device Product
No
Data Monitoring Committee
No

5. Study Description

Brief Summary
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
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.

6. Conditions and Keywords

Primary Disease or Condition Being Studied in the Trial, or the Focus of the Study
Adenoma Detection Rate

7. Study Design

Primary Purpose
Diagnostic
Study Phase
Not Applicable
Interventional Study Model
Parallel Assignment
Masking
None (Open Label)
Allocation
Randomized
Enrollment
370 (Actual)

8. Arms, Groups, and Interventions

Arm Title
Computed adenoma detection system (CADe)
Arm Type
Experimental
Arm Description
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
Arm Title
Control group (absence of CADe)
Arm Type
Active Comparator
Arm Description
This is the control group. As in the routine colonoscopy the endoscopist is in charge of the detection of the lesions.
Intervention Type
Device
Intervention Name(s)
Computed adenoma detection system (CADe)
Intervention Description
This is a computed system that helps the endoscopist to increase the detection of colorectal polyps
Intervention Type
Behavioral
Intervention Name(s)
Control group (regular colonoscopy)
Intervention Description
It is exclusively the endoscopist in charge of the detection of the polyps (usual practice)
Primary Outcome Measure Information:
Title
Adenoma detection rate
Description
Number of colonoscopies with colorectal adenoma/Number of total colonoscopies
Time Frame
[Time frame: 1 years][Designated as safety issue: No]
Secondary Outcome Measure Information:
Title
Serrated detection rate
Description
Number of colonoscopies with serrated adenoma/Number of total colonoscopies
Time Frame
[Time Frame: 1 years][Designated as safety issue: No]
Title
Advanced adenoma detection rate
Description
Number of colonoscopies with advanced adenoma/Number of total colonoscopies
Time Frame
[Time Frame: 1 years][Designated as safety issue: No]

10. Eligibility

Sex
All
Minimum Age & Unit of Time
18 Years
Accepts Healthy Volunteers
No
Eligibility Criteria
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.
Overall Study Officials:
First Name & Middle Initial & Last Name & Degree
Antonio Gimeno Garcia, MD, PhD
Organizational Affiliation
Hospital Universitario de Canarias
Official's Role
Principal Investigator
Facility Information:
Facility Name
Department of Gastroenterology
City
La Laguna
State/Province
S/C De Tenerife
ZIP/Postal Code
38320
Country
Spain

12. IPD Sharing Statement

Plan to Share IPD
No
Citations:
PubMed Identifier
32598963
Citation
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.
Results Reference
background
PubMed Identifier
32565188
Citation
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.
Results Reference
result
PubMed Identifier
31981517
Citation
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. Erratum In: Lancet Gastroenterol Hepatol. 2020 Apr;5(4):e3.
Results Reference
result
PubMed Identifier
32562721
Citation
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.
Results Reference
result

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Artificial Intelligence Aid Systems in Colorectal Adenoma Detection

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