Abstract
Accurate assessment of bowel cleanliness is essential for effective colonoscopy procedures. The Boston Bowel Preparation Scale (BBPS) offers a standardized scoring system but suffers from subjectivity and inter-observer variability when performed manually. In this paper, to support robust training and evaluation, we construct a high-quality colonoscopy dataset comprising 2,240 images from 517 subjects, annotated with expert-agreed BBPS scores. We propose a novel automated BBPS scoring framework that leverages the CLIP model with adapter-based transfer learning and a dedicated fecal-feature extraction branch. Our method fuses global visual features with stool-related textual priors to improve the accuracy of bowel cleanliness evaluation without requiring explicit segmentation. Extensive experiments on both our dataset and the public NERTHU dataset demonstrate the superiority of our approach over existing baselines, highlighting its potential for clinical deployment in computer-aided colonoscopy analysis.
Abstract (translated)
准确的肠道清洁度评估对于有效的结肠镜检查至关重要。波士顿肠道准备评分量表（BBPS）提供了一种标准化评分系统，但在手动操作时容易受到主观性和观察者间差异的影响。在本文中，为了支持稳健的训练和评估，我们构建了一个高质量的结肠镜图像数据集，该数据集包含来自517名受试者的2,240张图像，并且这些图像已经由专家商定并标注了BBPS评分。我们提出了一种新的自动化BBPS评分框架，利用CLIP模型结合基于适配器的迁移学习和专门的粪便特征提取分支。我们的方法将全局视觉特征与与粪便相关的文本先验知识相结合，以提高肠道清洁度评估的准确性，并且无需显式分割操作。在我们自己的数据集以及公开的NERTHU数据集上的广泛实验表明，我们的方法优于现有的基线方法，突显了其在计算机辅助结肠镜检查分析中的临床应用潜力。
URL
https://arxiv.org/abs/2512.20374
