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Study of capsule endoscopy delivery at scale through enhanced artificial intelligence-enabled analysis (the CESCAIL study).
Lei, Ian Io; Tompkins, Katie; White, Elizabeth; Watson, Angus; Parsons, Nicholas; Noufaily, Angela; Segui, Santi; Wenzek, Hagen; Badreldin, Rawya; Conlin, Abby; Arasaradnam, Ramesh P.
  • Lei II; Department of Gastroenterology, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK.
  • Tompkins K; Department of Gastroenterology, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK.
  • White E; CorporateHealth International, Inverness, UK.
  • Watson A; Institute of Applied Health Sciences, University of Aberdeen, Aberdeen, UK.
  • Parsons N; Medical statistics, University of Warwick, Coventry, UK.
  • Noufaily A; Medical statistics, University of Warwick, Coventry, UK.
  • Segui S; Department of Maths and Computer Science, University of Barcelona, Barcelona, Spain.
  • Wenzek H; Institute of Applied Health Sciences, University of Aberdeen, Aberdeen, UK.
  • Badreldin R; Department of Gastroenterology, James Paget University Hospitals NHS Foundation Trust, Lowestoft, UK.
  • Conlin A; Department of Gastroenterology, Northern Care Alliance NHS Foundation Trust, Salford, UK.
  • Arasaradnam RP; Department of Gastroenterology, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK.
Colorectal Dis ; 2023 Apr 21.
Article in English | MEDLINE | ID: covidwho-2298635
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.
Keywords

Full text: Available Collection: International databases Database: MEDLINE Type of study: Cohort study / Diagnostic study / Observational study / Prognostic study Language: English Journal subject: Gastroenterology Year: 2023 Document Type: Article Affiliation country: Codi.16575

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Cohort study / Diagnostic study / Observational study / Prognostic study Language: English Journal subject: Gastroenterology Year: 2023 Document Type: Article Affiliation country: Codi.16575