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CC BY-NC-ND 4.0 · Endosc Int Open 2023; 11(10): E970-E975
DOI: 10.1055/a-2161-1816
Original article

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

Fintan John O'Hara
1   Gastroenterology, Tallaght University Hospital, Dublin, Ireland (Ringgold ID: RIN57976)
2   Medicine, Trinity College Dublin School of Medicine, Dublin, Ireland (Ringgold ID: RIN155276)
,
Deirdre Mc Namara
1   Gastroenterology, Tallaght University Hospital, Dublin, Ireland (Ringgold ID: RIN57976)
2   Medicine, Trinity College Dublin School of Medicine, Dublin, Ireland (Ringgold ID: RIN155276)
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Referred to by:

Reading capsule endoscopy: Why not AI alone?Endosc Int Open 2023; 11(12): E1175-E1176
DOI: 10.1055/a-2202-8301

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


Publication History

Received: 31 January 2023

Accepted after revision: 25 August 2023

Accepted Manuscript online:
28 August 2023

Article published online:
11 October 2023

© 2023. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial-License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).

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