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.

Background: Colon capsule endoscopy (CCE) is becoming more widely available across Europe, but its uptake is slow due to the need for follow-up colonoscopy for therapeutic procedures and biopsies, which impacts its cost-effectiveness. One of the major factors driving the conversion to colonoscopy is the detection of excess polyps in CCE that cannot be matched during subsequent colonoscopy. The capsule’s rocking motion, which can lead to duplicate reporting of the same polyp when viewed from different angles, is likely a key contributor. Objectives: This review aims to explore the types of polyp matching reported in the literature, assess matching techniques and matching accuracy, and evaluate the development of machine learning models to improve polyp matching in CCE and subsequent colonoscopy. Methods: A systematic literature search was conducted in EMBASE, MEDLINE, and PubMed. Due to the scarcity of research in this area, the search encompassed clinical trials, observational studies, reviews, case series, and editorial letters. Three directly related studies were included, and ten indirectly related studies were included for review. Results: Polyp matching in colon capsule endoscopy still needs to be developed, with only one study focused on creating criteria to match polyps within the same CCE video. Another study established that experienced CCE readers have greater accuracy, reducing interobserver variability. A machine learning algorithm was developed in one study to match polyps between initial CCE and subsequent colonoscopy. Only around 50% of polyps were successfully matched, requiring further optimisation. As Artificial Intelligence (AI) algorithms advance in CCE polyp detection, the risk of duplicate reporting may increase when clinicians are presented with polyp images or timestamps, potentially complicating the transition to AI-assisted CCE reading in the future. Conclusions: Polyp matching in CCE is a developing field with considerable challenges, especially in matching polyps within the same video. Although AI shows potential for decent accuracy, more research is needed to refine these techniques and make CCE a more reliable, non-invasive alternative to complement conventional colonoscopy for lower GI investigations.

Colon capsule endoscopy (CCE) has regained popularity for lower gastrointestinal investigations since the COVID-19 pandemic. While there have been systematic reviews and meta-analyses on colonic polyp detection using CCE, there is a lack of comprehensive evidence concerning colonic inflammation. Therefore, this systematic review and meta-analysis aimed to assess the diagnostic accuracy of CCE for colonic inflammation, predominantly ulcerative colitis (UC) and Crohn’s disease (CD). Methods: We systematically searched electronic databases (EMBASE, MEDLINE, PubMed Central, and Cochrane Library) for studies comparing the diagnostic accuracy between CCE and optical endoscopy as the standard reference. A bivariate random effect model was used for the meta-analysis. Results: From 3797 publications, 23 studies involving 1353 patients were included. Nine studies focused on UC, and ten focused on CD. For UC, CCE showed a pooled sensitivity of 92% (95% CI, 88–95%), a specificity of 71% (95% CI, 35–92%), and an AUC of 0.93 (95% CI, 0.89–0.97). For CD, the pooled sensitivity was 92% (95% CI, 89–95%), and the specificity was 88% (95% CI, 84–92%), with an AUC of 0.87 (95% CI, 0.76–0.98). Overall, for inflammatory bowel disease, the pooled sensitivity, specificity, and AUC were 90% (95% CI, 85–93%), 76% (95% CI, 56–90%), and 0.92 (95% CI, 0.94–0.97), respectively. Conclusions: Despite the challenges around standardised disease scoring and the lack of histological confirmation, CCE performs well in diagnosing inflammatory bowel disease. It demonstrates high sensitivity in both UC and Crohn’s terminal ileitis and colitis and high specificity in Crohn’s disease. Further studies are needed to evaluate the diagnostic accuracy of other colonic inflammatory conditions.

Objective Colon capsule endoscopy (CCE) has emerged as a promising alternative for investigating lower gastrointestinal symptoms. However, its adoption has been limited due to concerns about cost-effectiveness, significantly influenced by follow-up endoscopy rates (FERs). Understanding CCE’s FERs is crucial for its integration into routine clinical practice. We synthesised the evidence to evaluate the overall rate of further investigation in CCE. Design A systematic review and meta-analysis were conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Data sources Medline, Embase, and PubMed were searched through 15 August 2024. Eligibility criteria Studies included reporting FERs after CCE, including subsequent endoscopic procedures and radiological imaging. There were no language restrictions or limitations in CCE referral indications, patient recruitment criteria, or pathologies investigated. Data extraction and synthesis All studies were independently screened and extracted two times by four reviewers. A random-effects model was used for meta-analysis and meta-regression to identify key contributing factors. Results 2850 participants from 19 studies were included in the analysis. Compared with the key performance indicators for FERs in colonoscopy (0.10–0.15) and CT colonography (0.25), the pooled FER for CCE was found to be 0.42 (95% CI 0.34 to 0.50). The meta-regression analysis identified complete transit rates and adequate bowel cleansing quality as factors inversely associated with FERs. Furthermore, the CCE2 capsule demonstrated a higher reinvestigation risk than CCE1, likely due to its improved diagnostic accuracy. Although CCE indications were associated with lower FERs, subgroup analysis did not reach statistical significance with high heterogeneity. Conclusion This study highlights significant FERs for CCE and identifies key contributing factors, emphasising the importance of appropriate patient selection to reduce reinvestigation needs. Future research should focus on improving completion rates, bowel preparation protocols, and refining CCE indications. This will minimise environmental impact and enhance cost-effectiveness and patient satisfaction. PROSPERO registration number CRD42024567959.