Artificial Intelligence Aid Systems in Colorectal Adenoma Detection

NCT04945044 | Completed

• 1 other identifier: Computer aid adenoma detection
• Study Type: interventional
• Target: 370
• Locations: 1 country
• Sites: 1

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.

Conditions

Adenoma Detection Rate

Outcome Measures

Primary Outcomes (1)

• Adenoma detection rate

  Number of colonoscopies with colorectal adenoma/Number of total colonoscopies

  [Time frame: 1 years][Designated as safety issue: No]

Secondary Outcomes (2)

• Serrated detection rate

  [Time Frame: 1 years][Designated as safety issue: No]

• Advanced adenoma detection rate

  [Time Frame: 1 years][Designated as safety issue: No]

Study Arms (2)

Computed adenoma detection system (CADe)

EXPERIMENTAL

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

Device: Computed adenoma detection system (CADe)

Control group (absence of CADe)

ACTIVE COMPARATOR

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

Behavioral: Control group (regular colonoscopy)

Interventions

Computed adenoma detection system (CADe) DEVICE

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

Computed adenoma detection system (CADe)

Control group (regular colonoscopy) BEHAVIORAL

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

Control group (absence of CADe)

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

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

You may not qualify if:

• 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.

Sponsors & Collaborators

• Hospital Universitario de Canarias: lead

Study Sites (1)

Department of Gastroenterology

San Cristóbal de La Laguna, S/C de Tenerife, 38320, Spain

Location

Related Publications (5)

• 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.

  PMID: 32598963 BACKGROUND

• 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.

  PMID: 32565188 RESULT

• 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.

  PMID: 31981517 RESULT

• 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.

  PMID: 32562721 RESULT

• 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.

  PMID: 36228695 DERIVED

MeSH Terms

Interventions

Control Groups

Intervention Hierarchy (Ancestors)

Epidemiologic Research Design; Epidemiologic Methods; Investigative Techniques; Research Design; Methods

Study Officials

• Antonio Gimeno Garcia, MD, PhD

  Hospital Universitario de Canarias

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: June 22, 2021
• First Posted: June 30, 2021
• Study Start: November 15, 2021
• Primary Completion: January 31, 2022
• Study Completion: January 31, 2022
• Last Updated: September 21, 2022

Record last verified: 2022-09

Data Sharing

• IPD Sharing: Will not share

Locations

ES (1)