نبذة مختصرة : 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 centers 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 centers 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.
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