Colonoscopy is the standard of care technique for detecting and removing polyps for the prevention of colorectal cancer. Nevertheless, gastroenterologists (GI) routinely miss approximately 25% of polyps during colonoscopies. These misses are highly operator dependent, influenced by the physician skills, experience, vigilance, and fatigue. Standard quality metrics, such as Withdrawal Time or Cecal Intubation Rate, have been shown to be well correlated with Adenoma Detection Rate (ADR). However, those metrics are limited in their ability to assess the quality of a specific procedure, and they do not address quality aspects related to the style or technique of the examination. In this work we design novel online and offline quality metrics, based on visual appearance quality criteria learned by an ML model in an unsupervised way. Furthermore, we evaluate the likelihood of detecting an existing polyp as a function of quality and use it to demonstrate high correlation of the proposed metric to polyp detection sensitivity. The proposed online quality metric can be used to provide real time quality feedback to the performing GI. By integrating the local metric over the withdrawal phase, we build a global, offline quality metric, which is shown to be highly correlated to the standard Polyp Per Colonoscopy (PPC) quality metric.

翻译：结肠镜检查是检测和切除息肉以预防结直肠癌的标准诊疗技术。然而，消化科医生在结肠镜检查中常规遗漏约25%的息肉。这些遗漏高度依赖操作者，受医生技能、经验、警觉性和疲劳程度的影响。标准质量指标（如退镜时间或盲肠插管率）已被证实与腺瘤检出率高度相关，但这些指标在评估具体操作质量方面存在局限，且未涉及检查风格或技术相关的质量维度。本研究基于无监督方式训练的机器学习模型所学习的视觉外观质量准则，设计了新型在线与离线质量指标。进一步地，我们评估了现有息肉检出概率与质量指标的函数关系，并证明所提指标与息肉检测灵敏度具有高相关性。提出的在线质量指标可为执行操作的消化科医生提供实时质量反馈。通过将局部指标在退镜阶段进行积分，我们构建了全局离线质量指标，该指标与标准每例结肠镜息肉数质量指标呈现高度相关性。