Video/Image Library of Endoscopy Procedures for the Development of AI-empowered Endoscopy Quality Reporting and Educational Modules
Creation of a Video/Image Library of Annotated Full-length Endoscopy Procedures for the Development of Artificial Intelligence-empowered Endoscopy Quality Reporting and Educational Modules
Centre hospitalier de l'Université de Montréal (CHUM)
10,000 participants
Jun 10, 2022
OBSERVATIONAL
Conditions
Summary
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.
Eligibility
Inclusion Criteria2
- ≥ 18 y.o.
- indication of undergoing a screening, surveillance, diagnostic, or therapeutic upper (EGD) or lower (colonoscopy) endoscopy
Exclusion Criteria4
- 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
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Locations(1)
View Full Details on ClinicalTrials.gov
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NCT06822816