To address the limitations of current AI-based systems that rely on the assumption of a "constant withdrawal speed," this study proposes the integration of the UPD-3 endoscopic positioning system. By using colonoscope withdrawal videos in combination with UPD-3 imaging data as training samples, we aim to develop an AI-powered bowel cleanliness assessment system that incorporates "withdrawal distance" as a weighting factor. This approach is expected to yield a more reliable, objective, and clinically applicable intelligent assessment system that better aligns with real-world clinical practice and endoscopists' operational habits.
Clear colonoscopy images suitable for BBPS scoring
Complete and clear colonoscopy videos suitable for BBPS scoring
Clear colonoscopy videos with a stable UPD-3 positioning system, without signal drift, disappearance, or other disruptions
Blurred colonoscopy images
Colonoscopy images taken from the small intestine or outside the patient's body
Colonoscopy images captured during irrigation or instrument manipulation
Colonoscopy images obtained during chromoendoscopy
Colonoscopy images, colonoscopy videos, and colonoscopy videos with the UPD-3 positioning system from the electronic database of our hospital
Cohort
Retrospective
No Intervention: Observational Cohort
Other interventionNo Intervention: Observational Cohort
Prediction accuracy of the models
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