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Video/image Library of Endoscopy Procedures for the Development of AI-empowered Endoscopy Quality Reporting and Educational Modules

Polyp of Colon | Artificial Intelligence (AI)

The goal of this observational study is to establish a video/image library dataset of complete endoscopy or partial colonoscopy procedures for patients with rectal cancer or inflammatory bowel disease (IBD). With this video/image library, the aims are:

* to develop and validate novel AI-empowered solutions to automatically detect and report endoscopy quality metrics
* to develop automated endoscopy reporting solutions, auditing, and educational tools for residents and fellows to enhance their endoscopy skills.

The hypothesis is that a heterogeneous video/image library will provide:

* comprehensive and robust source material to develop AI models
* real-time quality feedback at the end of an endoscopy procedure.

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Participation Requirements

  • Sex:

    ALL

  • Eligible Ages:

    18 and up

Participation Criteria

Inclusion Criteria:

* ≥ 18 y.o.
* indication of undergoing a screening, surveillance, diagnostic, or therapeutic upper (EGD) or lower (colonoscopy) endoscopy

Exclusion Criteria:

* Coagulopathy defined as an elevated INR ≥ 2.5
* Platelet count ≤ 50,000/mm3
* Emergency endoscopy
* Poor general health defined as the American Society of Anesthesiologists physical status class \>3

Study Location

Centre Hospitalier de l'Université de Montréal
Centre Hospitalier de l'Université de Montréal
Montréal, Quebec
Canada

Contact Study Team

Primary Contact

Samira Hanin

[email protected]
514-890-8000
Backup Contact

Daniel von Renteln, MD

Study Sponsored By
Centre hospitalier de l'Université de Montréal (CHUM)
Participants Required
More Information
Study ID: NCT06822816