Artificial Intelligence Aid Systems in Colorectal Adenoma Detection
NCT ID: NCT04945044
Last Updated: 2022-09-21
Study Results
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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COMPLETED
NA
370 participants
INTERVENTIONAL
2021-11-15
2022-01-31
Brief Summary
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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.
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Detailed Description
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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. 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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Study Design
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RANDOMIZED
PARALLEL
DIAGNOSTIC
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
Computed adenoma detection system (CADe)
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.
Control group (regular colonoscopy)
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
Control group (regular colonoscopy)
It is exclusively the endoscopist in charge of the detection of the polyps (usual practice)
Eligibility Criteria
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Inclusion Criteria
* Patients referred for outpatient colonoscopy
Exclusion Criteria
* 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.
18 Years
ALL
No
Sponsors
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Hospital Universitario de Canarias
OTHER
Responsible Party
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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
Countries
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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.
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.
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.
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.
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.
Other Identifiers
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Computer aid adenoma detection
Identifier Type: -
Identifier Source: org_study_id
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