Abstract
Background: Adequate bowel preparation is crucial for high-quality colonoscopy; however, assessing preparation adequacy can be burdensome for both healthcare providers and patients. In this study, we aimed to develop artificial intelligence (AI) models for the automated identification of bowel PREParation for colonoscopy (AI-PREPOO).
Methods: On the day of colonoscopy, participants were instructed to use smartphones to photograph their stool in the toilet after each bowel movement following initiation of polyethylene glycol solution and upload the images to a secure web server. All images were labeled as “ready” or “not ready” for colonoscopy based on clarity and the absence of solid content. Using these labeled images, four image-recognition models based on different deep learning architectures (AI-PREPOO 1-4) were developed using transfer learning to classify stool status as “ready” or “not ready.”