Background:Colon capsule endoscopy (CCE) has seen increased adoption since the COVID-19 pandemic, offering a non-invasive alternative for lower gastrointestinal investigations. However, inadequate bowel preparation remains a key limitation, often leading to higher conversion rates to colonoscopy. Manual assessment of bowel cleanliness is inherently subjective and marked by high interobserver variability. Recent advances in artificial intelligence (AI) have enabled automated cleansing scores that not only standardise assessment and reduce variability but also align with the emerging semi-automated AI reading workflow, which highlights only clinically significant frames. As full video review becomes less routine, reliable, and consistent, cleansing evaluation is essential, positioning bowel preparation AI as a critical enabler of diagnostic accuracy and scalable CCE deployment.Objective:This CESCAIL sub-study aimed to (1) evaluate interobserver agreement in CCE bowel cleansing assessment using two established scoring systems, and (2) determine the impact of AI-assisted scoring, specifically a TransUNet-based segmentation model with a custom Patch Loss function, on both interobserver and intraobserver agreement compared to manual assessment.Methods:As part of the CESCAIL study, twenty-five CCE videos were randomly selected from 673 participants. Nine readers with varying CCE experience scored bowel cleanliness using the Leighton–Rex and CC-CLEAR scales. After a minimum 8-week washout, the same readers reassessed the videos using AI-assisted CC-CLEAR scores. Interobserver variability was evaluated using bootstrapped intraclass correlation coefficients (ICC) and Fleiss’ Kappa; intraobserver variability was assessed with weighted Cohen’s Kappa, paired t-tests, and Two One-Sided Tests (TOSTs).Results:Leighton–Rex showed poor to fair agreement (Fleiss = 0.14; ICC = 0.55), while CC-CLEAR demonstrated fair to excellent agreement (Fleiss = 0.27; ICC = 0.90). AI-assisted CC-CLEAR achieved only moderate agreement overall (Fleiss = 0.27; ICC = 0.69), with weaker performance among less experienced readers (Fleiss = 0.15; ICC = 0.56). Intraobserver agreement was excellent (ICC > 0.75) for experienced readers but variable in others (ICC 0.03–0.80). AI-assisted scores were significantly lower than manual reads by 1.46 points (p< 0.001), potentially increasing conversion to colonoscopy.Conclusions:AI-assisted scoring did not improve interobserver agreement and may even reduce consistency amongst less experienced readers. The maintained agreement observed in experienced readers highlights its current value in experienced hands only. Further refinement, including spatial analysis integration, is needed for robust overall AI implementation in CCE.
背景：自COVID-19大流行以来，结肠胶囊内镜（CCE）的应用日益广泛，为下消化道检查提供了一种非侵入性替代方案。然而，肠道准备不充分仍是其主要局限，常导致较高的结肠镜转诊率。人工评估肠道清洁度存在固有的主观性，观察者间差异显著。人工智能（AI）的最新进展实现了自动化清洁度评分，不仅可标准化评估、减少差异性，还能与新兴的半自动化AI阅片工作流程（仅突出临床意义显著的图像帧）相衔接。随着全视频复核逐渐减少常规应用，可靠且一致的清洁度评估变得至关重要，这使得肠道准备AI成为提升诊断准确性和推动CCE规模化部署的关键赋能工具。 目的：本CESCAIL子研究旨在（1）使用两种成熟评分系统评估CCE肠道清洁度判读的观察者间一致性；（2）与人工评估相比，确定AI辅助评分（特别是基于TransUNet分割模型结合定制Patch Loss函数的方法）对观察者间及观察者内一致性的影响。 方法：作为CESCAIL研究的一部分，从673名参与者中随机选取25段CCE视频。9名具有不同CCE经验的阅片者使用Leighton–Rex量表和CC-CLEAR量表评估肠道清洁度。经过至少8周洗脱期后，同一批阅片者使用AI辅助的CC-CLEAR评分对视频进行重新评估。观察者间差异性通过自助法组内相关系数（ICC）和弗莱斯Kappa系数评估；观察者内差异性通过加权科恩Kappa系数、配对t检验及双单侧检验（TOST）进行评估。 结果：Leighton–Rex量表显示一致性程度为差至一般（弗莱斯Kappa=0.14；ICC=0.55），而CC-CLEAR量表显示一致性为一般至优秀（弗莱斯Kappa=0.27；ICC=0.90）。AI辅助的CC-CLEAR评分总体仅达到中等一致性（弗莱斯Kappa=0.27；ICC=0.69），在经验较少的阅片者中表现更弱（弗莱斯Kappa=0.15；ICC=0.56）。经验丰富阅片者的观察者内一致性优秀（ICC>0.75），但其他阅片者差异较大（ICC 0.03–0.80）。AI辅助评分较人工评分显著降低1.46分（p<0.001），可能增加结肠镜转诊率。 结论：AI辅助评分未能改善观察者间一致性，甚至可能降低经验不足阅片者的一致性。经验丰富阅片者保持的一致性凸显了当前该技术仅在有经验者手中的应用价值。未来需通过整合空间分析等技术进一步优化，以实现AI在CCE领域的稳健全面应用。
肿瘤(癌症)患者之家