Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 1498-1505 |
| Number of pages | 8 |
| Journal | Colorectal Disease |
| Volume | 25 |
| Issue number | 7 |
| Early online date | 21 Apr 2023 |
| DOIs | |
| Publication status | Published - Jul 2023 |
Bibliographical note
Funding Information:This study is funded by the National Institute for Health and Care Research (NIHR) (funder award NIHR AI_AWARD02440).
Data Availability Statement
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.Keywords
- artificial intelligence
- colon capsule endoscopy
- colonic polyps
- colorectal cancer
Access to Document
- Lei_etal_CD_Study_of_Capsule_VOR
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.