Background/Objectives: Colon capsule endoscopy (CCE) is a non-invasive method for visualising the colon, but its clinical adoption has been slow. Although the COVID-19 pandemic reignited interest in CCE, its role in conventional gastrointestinal investigations remains unclear, leading to varied practices across Europe. This highlights the need for a comprehensive understanding of diverse approaches to CCE in clinical practice. Method: A web-based survey was conducted from January to July 2024, targeting European gastroenterologists and colorectal surgeons interested in capsule endoscopy through the International Capsule Endoscopy Research (iCARE) Group. The survey aimed to understand CCE application across Europe and investigate factors influencing its uptake. Results: Thirty-eight (n = 38) valid responses were received from 19 European countries. While 88% reported access to CCE, only 45% had local services readily available, and just 7% included CCE in national guidelines. The most common indication for CCE was for patients who declined or could not tolerate colonoscopy (30%), with 77% of CE specialists preferring its use in fit patients. Ease of access was significantly associated with service availability (p = 0.0358). Barriers to uptake included lack of reimbursement, insufficient knowledge, and limited use in research settings. Only 27% of specialists viewed CCE positively, while 57% had a negative perception. Conclusions: This study reveals the wide variation in CCE practices and critical factors influencing its uptake. Understanding common indications and patient groups is the key to guiding its future development, particularly as AI and telemedicine enhance its potential for rapid full digestive tract visualisation.

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.