Clinical trial NCT04811937: Development of a Computer-aided Polypectomy Decision Support
Official title: Development of a Computer-aided Polypectomy Decision Support
Study type: INTERVENTIONAL
Overall status: WITHDRAWN
Phase: NA
Conditions: Adenomatous Polyps
Keywords: Polyps detection
Artificial Intelligence
Adenoma detection
Polyps classification
Computer decision support
Lead sponsor: Centre hospitalier de l'Université de Montréal (CHUM)
Sponsor class: OTHER
First posted: 2021-03-23
Enrollment: 0
Sex: ALL
Minimum age (years): 45
Maximum age (years): 80
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: NA
Intervention model: SINGLE_GROUP
Intervention model description: prospective, multi-endoscopist, single center, clinical study at tertiary referral center (CHUM)
Primary purpose: SUPPORTIVE_CARE

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.

Detailed description: 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.

Eligibility criteria: 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

Primary outcomes:
measure: Accuracy of the CADp system
timeFrame: 3 weeks
measure: Completeness of polypectomy
timeFrame: 1 month
measure: Training CADp
timeFrame: 1 month
measure: Validity of the choice of primary outcome
timeFrame: 1 month
Secondary outcomes:
Other outcomes:
Locations:
city: Montréal
country: Canada
facility: Centre Hospitalier Universitaire de Montréal
state: Quebec
Study officials:
affiliation: Centre hospitalier de l'Université de Montréal (CHUM)
name: Daniel von Renteln
role: PRINCIPAL_INVESTIGATOR
References: