Development of a Computer-aided Polypectomy Decision Support

Centre hospitalier de l'Université de Montréal (CHUM)

Status: Withdrawn

Conditions: Adenomatous Polyps

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

Study type: Interventional

Funder types: Other

Identifiers

NCT04811937

20.382

Details and patient eligibility

About

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.

Full 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.

Sex: All

Ages: 45 to 80 years old

Volunteers: No Healthy Volunteers

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

Trial design

Primary purpose: Supportive Care

Allocation: N/A

Interventional model: Single Group Assignment

Masking: None (Open label)

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0 participants in 1 patient group

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

Experimental group

Description:

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.

Treatment:

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