Ignite Creation Date:
2024-05-06 @ 3:57 PM
Last Modification Date:
2024-10-26 @ 2:00 PM
Study NCT ID:
NCT04811937
Status:
WITHDRAWN
Last Update Posted:
2022-12-13
First Post:
2021-02-16
Brief Title:
Development of a Computer-aided Polypectomy Decision Support
Sponsor:
Centre hospitalier de lUniversité de Montréal CHUM
Organization:
Centre hospitalier de lUniversité de Montréal CHUM
Study Overview
Official Title:
Development of a Computer-aided Polypectomy Decision Support
Status:
WITHDRAWN
Status Verified Date:
2022-12
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
The study was abandoned due to the Covid pandemic which prevented recruitment
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
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 problemThis 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
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
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None