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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Conditions de participation
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Sexe:
ALL -
Âges admissibles:
18 and up
Critères de participation
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
Lieu de l'étude
Centre Hospitalier de l'Université de Montréal
Centre Hospitalier de l'Université de MontréalMontréal, Quebec
Canada
Contactez l'équipe d'étude
Daniel von Renteln, MD
- Étude parrainée par
- Centre hospitalier de l'Université de Montréal (CHUM)
- Participants recherchés
- Plus d'informations
- ID de l'étude:
NCT06822816