NCT04811937

Development of a Computer-aided Polypectomy Decision Support

• Status: Withdrawn
• Phase: N/A
• Study type: Interventional
• Enrollment: 0 (actual)
• Sponsor: Centre hospitalier de l'Université de Montréal (CHUM) · Academic / Other
• Sex: All
• Age: 45 Years – 80 Years
• Healthy volunteers: Not accepted

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.

Conditions

• Adenomatous Polyps

Interventions

Type	Name	Description
DIAGNOSTIC_TEST	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.

Timeline

• Start date: 2021-12-01
• Primary completion: 2023-04-01
• Completion: 2023-04-01
• First posted: 2021-03-23
• Last updated: 2022-12-13

Locations

1 site across 1 country: Canada