Capsule endoscopy with artificial intelligence-assisted technology: Real-world usage of a validated AI model for capsule image review

Abstract

Background and study aims Capsule endoscopy is a time-consuming procedure with a significance error rate. Artificial intelligence (AI) can potentially reduce reading time significantly by reducing the number of images that need human review. An OMOM Artificial Intelligence-enabled small bowel capsule has been recently trained and validated for small bowel capsule endoscopy video review. This study aimed to assess its performance in a real-world setting in comparison with standard reading methods.

Patients and methods In this single-center retrospective study, 40 patient studies performed using the OMOM capsule were analyzed first with standard reading methods and later using AI-assisted reading. Reading time, pathology identified, intestinal landmark identification and bowel preparation assessment (Brotz Score) were compared.

Results Overall diagnosis correlated 100% between the two reading methods. In a per-lesion analysis, 1293 images of significant lesions were identified combining standard and AI-assisted reading methods. AI-assisted reading captured 1268 (98.1%, 95% CI 97.15–98.7) of these findings while standard reading mode captured 1114 (86.2%, 95% confidence interval 84.2–87.9), P < 0.001. Mean reading time went from 29.7 minutes with standard reading to 2.3 minutes with AI-assisted reading (P < 0.001), for an average time saving of 27.4 minutes per study. Time of first cecal image showed a wide discrepancy between AI and standard reading of 99.2 minutes (r = 0.085, P = 0.68). Bowel cleansing evaluation agreed in 97.4% (r = 0.805 P < 0.001).

Conclusions AI-assisted reading has shown significant time savings without reducing sensitivity in this study. Limitations remain in the evaluation of other indicators.

Keywords

Endoscopy Small Bowel - Capsule endoscopy - Quality and logistical aspects - Image and data processing, documentatiton

Publikationsverlauf

Eingereicht: 31. Januar 2023

Angenommen nach Revision: 25. August 2023

Accepted Manuscript online:
28. August 2023

Artikel online veröffentlicht:
11. Oktober 2023

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