Development of a Computer-aided Polypectomy Decision Support

NCT04811937 | Withdrawn

• 1 other identifier: 20.382
• Study Type: interventional
• Target: N/A
• Locations: 1 country
• Sites: 1

Brief Summary

Quality components of colonoscopy include the detection and complete removal of colorectal polyps, which are precursors to CRC. However, endoscopic ablation may be incomplete, posing a risk for the development of "interval cancers". The investigators propose to develop a solution based on artificial intelligence (AI) (CADp computer-aided decision support polypectomy) to solve this problem.This research project aims to develop CADp, a computer decision support solution (CDS) for the ablation of colorectal polyps from 1 to 20 mm.

Conditions

Adenomatous Polyps

Keywords

Polyps detection; Artificial Intelligence; Adenoma detection; Polyps classification; Computer decision support

Outcome Measures

Primary Outcomes (4)

• Accuracy of the CADp system

  accuracy with which the CADp system predicts completeness of polypectomy in the test set with the reference standard for completeness being determined by the histology of post-polypectomy margin biopsies; if free from any polyp tissue (adenomatous, serrated or hyperplastic), the resection will be considered complete. If remnant polyp tissue is detected in any one or more of the margin biopsies the resection is deemed incomplete

  3 weeks

• Completeness of polypectomy

  We will evaluate the agreement between the different subjective and objective ways of assessing the completeness of the polypectomy : evaluation of margins (presence or not, measurement of margins) by endoscopists self-assessment, and by expert consensus.

  1 month

• Training CADp

  Evaluation of the concordance of data on polyp size, extension of margins around the polyp, quality of resection between clinical data (endoscopists' self-assessment and experts' assessments) and CADp prediction.

  1 month

• Validity of the choice of primary outcome

  Based on the results and comparison of the different assessment methods, we will perform sensitivity analyses to assess the validity and robustness of the choice of primary outcome.

  1 month

Study Arms (1)

Artificial intelligence for real-time Computer decision support of resection of colorectal polyps

EXPERIMENTAL

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 about polypectomy procedures.

Diagnostic Test: Computer-aided polypectomy decision support by Artificial Intelligence

Interventions

Computer-aided polypectomy decision support by Artificial Intelligence DIAGNOSTIC_TEST

The AI system will capture the live video of the procedure and the AI feedbackwill 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 and the information to help the polypectomy.

Artificial intelligence for real-time Computer decision support of resection of colorectal polyps

Eligibility Criteria

• Age: 45 Years - 80 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Signed informed consent
• Age 45-80 years
• Indication to undergo a lower GI endoscopy.

You may not qualify if:

• Known inflammatory bowel disease
• Active colitis
• Coagulopathy
• Familial polyposis syndrome;
• Poor general health, defined as an American Society of Anesthesiologists (ASA) physical status class \>3
• Emergency colonoscopies

Sponsors & Collaborators

• Centre hospitalier de l'Université de Montréal (CHUM): lead

Study Sites (1)

Centre Hospitalier Universitaire de Montréal

Montreal, Quebec, Canada

Location

MeSH Terms

Conditions

Adenomatous Polyps

Condition Hierarchy (Ancestors)

Adenoma; Neoplasms, Glandular and Epithelial; Neoplasms by Histologic Type; Neoplasms

Study Officials

• Daniel von Renteln

  Centre hospitalier de l'Université de Montréal (CHUM)

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: SUPPORTIVE CARE
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Model Details: prospective, multi-endoscopist, single center, clinical study at tertiary referral center (CHUM)

Study Record Dates

• First Submitted: February 16, 2021
• First Posted: March 23, 2021
• Study Start: December 1, 2021
• Primary Completion: April 1, 2023
• Study Completion: April 1, 2023
• Last Updated: December 13, 2022

Record last verified: 2022-12

Data Sharing

• IPD Sharing: Will not share

Locations

CA (1)