Artificial Intelligence for Real-time Detection and Monitoring of Colorectal Polyps
|The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Know the risks and potential benefits of clinical studies and talk to your health care provider before participating. Read our disclaimer for details.|
|ClinicalTrials.gov Identifier: NCT04586556|
Recruitment Status : Recruiting
First Posted : October 14, 2020
Last Update Posted : March 24, 2021
|Condition or disease||Intervention/treatment||Phase|
|Adenomatous Polyps||Diagnostic Test: Polyps detection by Artificial Intelligence||Not Applicable|
|Study Type :||Interventional (Clinical Trial)|
|Estimated Enrollment :||600 participants|
|Intervention Model:||Single Group Assignment|
|Intervention Model Description:||prospective, multi-endoscopist, single center, clinical study at tertiary referral center (CHUM)|
|Masking:||None (Open Label)|
|Official Title:||Artificial Intelligence for Real-time Detection and Monitoring of Colorectal Polyps|
|Actual Study Start Date :||December 18, 2020|
|Estimated Primary Completion Date :||November 2021|
|Estimated Study Completion Date :||November 2021|
Experimental: Artificial intelligence for real-time detection and monitoring of colorectal polyps
A standard colonoscopy will be performed according to the standard of routine care. All optically diagnosed polyps will be removed and sent to the CHUM pathology laboratory for histopathological evaluation according to institutional standards. The AI system will capture video of the procedure in real time, and provide additional information on the detection of polyps, follow-up and prediction of pathology.
Diagnostic Test: Polyps detection by Artificial Intelligence
The AI system will capture the live video of the procedure and the AI feedback (polyp detection, tracking, and pathology prediction) will be shown on a second screen installed next to the regular endoscopy screen. Screen A will show the regular endoscopy image and screen B will show the regular endoscopy image together with the areas that might harbor a polyp or the information to predict pathology
- Efficacy of detection of adenomas [ Time Frame: Day 1 ]Efficacy of AI assisted colonoscopy to detect the proportion of patients with at least 1 neoplastic polyp. Adenoma detection rate with an AI.
- Incomplete resection rate (IRR) [ Time Frame: Day 1 ]proportion of incomplete polyp resections proportion of patients with either adenoma or carcinoma rate and type of adverse and serious adverse events occurring during the procedure.
- prediction of polyp histology [ Time Frame: Day 14 ]evaluate the agreement of the optical diagnosis-based surveillance interval with the pathology-based recommendation assessing the prediction accuracy of advanced neoplasia proportion of patients for whom an immediate surveillance recommendation following the colonoscopy can be directly provided when using AI-assisted optical diagnosis and how often histopathology polyp examination would have been avoided by using this strategy.
To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.
Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT04586556
|Université de Montréal||Active, not recruiting|
|Montréal, Quebec, Canada, QC H3T 1J4|
|Centre Hospitalier Universitaire de Montréal||Recruiting|
|Montréal, Quebec, Canada|
|Contact: Maureen Fontaine (514) 890-8000 Ext. 30655 firstname.lastname@example.org|
|Principal Investigator: Daniel von Renteln, MD|
|Sub-Investigator: Simon Bouchard, MD|
|Sub-Investigator: Erik Deslandres, MD|
|Sub-Investigator: Mickael Bouin, MD|
|Sub-Investigator: Audrey Weber, MD|
|Sub-Investigator: Sacha Sidani, MD|
|IHU Strasbourg||Active, not recruiting|
|Strasbourg, France, 67000|