Aim: Lower gastrointestinal (GI) diagnostics have been facing relentless capacity constraints for many years, even before the COVID‐19 era. Restrictions from the COVID pandemic have resulted in a significant backlog in lower GI diagnostics. Given recent developments in deep neural networks (DNNs) and the application of artificial intelligence (AI) in endoscopy, automating capsule video analysis is now within reach. Comparable to the efficiency and accuracy of AI applications in small bowel capsule endoscopy, AI in colon capsule analysis will also improve the efficiency of video reading and address the relentless demand on lower GI services. The aim of the CESCAIL study is to determine the feasibility, accuracy and productivity of AI‐enabled analysis tools (AiSPEED) for polyp detection compared with the ‘gold standard’: a conventional care pathway with clinician analysis.Method: This multi‐centre, diagnostic accuracy study aims to recruit 674 participants retrospectively and prospectively from centres conducting colon capsule endoscopy (CCE) as part of their standard care pathway. After the study participants have undergone CCE, the colon capsule videos will be uploaded onto two different pathways: AI‐enabled video analysis and the gold standard conventional clinician analysis pathway. The reports generated from both pathways will be compared for accuracy (sensitivity and specificity). The reading time can only be compared in the prospective cohort. In addition to validating the AI tool, this study will also provide observational data concerning its use to assess the pathway execution in real‐world performance.Results: The study is currently recruiting participants at multiple centres within the United Kingdom and is at the stage of collecting data.Conclusion: This standard diagnostic accuracy study carries no additional risk to patients as it does not affect the standard care pathway, and hence patient care remains unaffected.

Introduction Colorectal cancer (CRC) poses a significant global health threat, necessitating early detection. Traditional diagnostic tools like optical colonoscopy have limitations prompting our ‘5G-SUCCEEDS’ initiative to explore a novel approach involving remote colon capsule endoscopy (CCE). Methods This prospective feasibility study was conducted at a single hospital in England. Between December 2022 and September 2023, we introduced a remote CCE service within the 5G-SUCCEEDS framework. We undertook a feasibility study of CCE in patients with low-risk/moderate-risk CRC stratified by faecal haemoglobin. Outcomes included carbon footprint analysis (outlined through three potential clinical pathways) and patient-reported outcomes through structured questionnaires and interviews. Results Among 25 participants, 88% expressed satisfaction with remote CCE. 82% were willing to have remote CCE if clinically indicated in future. CCE findings included adenomatous polyps (58%), normal results (17%) and diverticulosis (21%), with no cancers identified in this pilot. Notably, we found that the carbon footprint associated with delivery of CCE at home (pathway 3) was lower compared with CCE delivered in a clinical setting (pathway 2). A fully optimised, automated scaled-up pathway would combine the delivery and collection of CCE equipment within a local area to reduce the carbon footprint of the travel element by 75%. Moreover, the conversion rate into a colonoscopy pathway is not static and clinicians acknowledge that this could be as low as 28%. Carbon footprint is more favourable for home-delivered CCE in the optimised scenario, while less so when considering the need for additional procedures (colonoscopy conversion). Conclusion The 5G-SUCCEEDS initiative highlights the feasibility and advantages of home-based diagnostics using CCE.

Artificial intelligence (AI) applications have become widely popular across the healthcare ecosystem. Colon capsule endoscopy (CCE) was adopted in the NHS England pilot project following the recent COVID pandemic’s impact. It demonstrated its capability to relieve the national backlog in endoscopy. As a result, AI-assisted colon capsule video analysis has become gastroenterology’s most active research area. However, with rapid AI advances, mastering these complex machine learning concepts remains challenging for healthcare professionals. This forms a barrier for clinicians to take on this new technology and embrace the new era of big data. This paper aims to bridge the knowledge gap between the current CCE system and the future, fully integrated AI system. The primary focus is on simplifying the technical terms and concepts in machine learning. This will hopefully address the general “fear of the unknown in AI” by helping healthcare professionals understand the basic principle of machine learning in capsule endoscopy and apply this knowledge in their future interactions and adaptation to AI technology. It also summarises the evidence of AI in CCE and its impact on diagnostic pathways. Finally, it discusses the unintended consequences of using AI, ethical challenges, potential flaws, and bias within clinical settings.

Lower gastrointestinal diagnostics have been facing significant capacity constraints, which the COVID-19 pandemic has exacerbated due to significant reductions in endoscopy procedures. Colon Capsule Endoscopy (CCE) provides a safe, viable solution to offset ongoing demand and could be a valuable tool for the recovery of endoscopy services post-COVID. NHS Scotland has already begun a country-wide rollout of CCE as a managed service, and NHS England have committed to a pilot scheme of 11,000 capsules via hospital-based delivery. Here, we outline a proven method of CCE delivery that ensures the CCE and results are delivered in an efficient, clinically robust manner with high patient acceptability levels through a managed service. Delivering CCE without a managed service is likely to be slower, more costly, and less effective, limiting the many benefits of CCE as an addition to the standard diagnostic pathway for bowel cancer.

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.

(1) Background: Accurate evaluation of colon capsule endoscopy videos plays a pivotal role in diagnosing gastrointestinal disorders. A primary step in this process is assessing the cleanliness of the area of interest to determine its admissibility. This study introduces a system designed to assist physicians in evaluating the colon cleanliness score of capsule endoscopy videos. (2) Methods: The system uses a TransUNet architecture, a customized loss function, and a low-effort labeling method to propose cleanliness scores for previously unseen videos. The proposed model was evaluated on a dataset of 52 capsule endoscopy videos. Agreement with physicians was measured using Cohen’s kappa statistic. (3) Results: The system achieved a Cohen’s kappa agreement of 0.586 with physicians, which is notably higher than the intra-observer variability observed, measured at 0.546. Additionally, the system can show the cleanliness evolution throughout the entire video, helping justify the proposed score. (4) Conclusions: The proposed system demonstrates improved agreement with physicians compared to inter-physician agreement, showing its potential to support the cleanliness evaluation process in colon capsule endoscopy. The ability to visualize the cleanliness evolution across the video enhances the transparency and interpretability of the suggested score.