Abstract
Accurate assessment of intestinal cleanliness is essential for effective colonoscopy, but manual scoring with the Boston Bowel Preparation Scale (BBPS) remains subjective and labor-intensive. In this study, we propose a novel, fully automated multi-stage pipeline for objective BBPS-based bowel preparation assessment in colonoscopy videos. Our approach utilizes deep image classifiers, enhanced by a hierarchical sequence of binary classification tasks, to assign precise frame-level BBPS scores. From the resulting sequence of scores, we extract statistical and temporal features to train machine learning regression models for video-level BBPS prediction. Extensive experiments demonstrate that our pipeline achieves robust, reproducible, and granular video-level bowel cleanliness assessment, outperforming standard multi-class models at the frame level and offering a scalable tool for large-scale endoscopic quality analysis.