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