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Development of a Computer-aided Polypectomy Decision Support

NCT ID: NCT04811937

Last Updated: 2022-12-13

Study Results

Results pending

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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Recruitment Status

WITHDRAWN

Clinical Phase

NA

Study Classification

INTERVENTIONAL

Study Start Date

2021-12-31

Study Completion Date

2023-04-30

Brief Summary

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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.

Detailed Description

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This research project aims to develop CADp, a computer-based decision support (CDS) solution for the removal of colorectal polyps ranging from 1-20 mm. The investigators will use a video and image dataset of polypectomy procedures to train the CADp model; thus, it can provide real-time overlaid video feedback for polypectomy procedures based on five specific metrics: 1) estimation of polyp size; 2) prediction of morphology and histology; 3) suggestion of an appropriate resection accessory and technical approach based on the characteristics, size, and histology of the polyp according to current guidelines; 4) image overlay, based on semantic image segmentation technology, showing the extent of the lesion and suggestion of an appropriate resection margin contour around the polyp to ensure its complete removal; 5) post-resection analysis to identify any remnant polyp tissue or insufficient resection margin that may increase this risk.

The investigators will collect a set of images and video data from live polypectomy procedures to leverage recent advances in AI technology to train deep learning models. This dataset will be obtained prospectively from a cohort of adults (ages 45-80) undergoing screening, diagnostic, or surveillance colonoscopies. To train the CADp solution, the investigators will obtain the corresponding completeness of resection status using the yield of post-resection margin biopsies. The dataset will be divided into two groups, the training, and the CADp test, respectively.

Conditions

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Adenomatous Polyps

Keywords

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Polyps detection Artificial Intelligence Adenoma detection Polyps classification Computer decision support

Study Design

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Allocation Method

NA

Intervention Model

SINGLE_GROUP

prospective, multi-endoscopist, single center, clinical study at tertiary referral center (CHUM)
Primary Study Purpose

SUPPORTIVE_CARE

Blinding Strategy

NONE

Study Groups

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Artificial intelligence for real-time Computer decision support of resection 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 about polypectomy procedures.

Group Type EXPERIMENTAL

Computer-aided polypectomy decision support by Artificial Intelligence

Intervention Type 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.

Interventions

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Computer-aided polypectomy decision support by Artificial Intelligence

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.

Intervention Type DIAGNOSTIC_TEST

Eligibility Criteria

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Inclusion Criteria

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

Exclusion Criteria

* 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
Minimum Eligible Age

45 Years

Maximum Eligible Age

80 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Centre hospitalier de l'Université de Montréal (CHUM)

OTHER

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Principal Investigators

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Daniel von Renteln

Role: PRINCIPAL_INVESTIGATOR

Centre hospitalier de l'Université de Montréal (CHUM)

Locations

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Centre Hospitalier Universitaire de Montréal

Montreal, Quebec, Canada

Site Status

Countries

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Canada

Other Identifiers

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20.382

Identifier Type: -

Identifier Source: org_study_id