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)
› Author Affiliations

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.



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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  • References

  • 1 Iddan G, Meron G, Glukhovsky A. et al. Wireless capsule endoscopy. Nature 2000; 405: 417
  • 2 Rondonotti E, Spada C, Adler S. et al. Small-bowel capsule endoscopy and device-assisted enteroscopy for diagnosis and treatment of small-bowel disorders: European Society of Gastrointestinal Endoscopy (ESGE) Technical Review. Endoscopy 2018; 50: 423-446
  • 3 Rondonotti E, Pennazio M, Toth E. et al. How to read small bowel capsule endoscopy: a practical guide for everyday use. Endosc Int Open 2020; 8: E1220-E1224
  • 4 Dray X, Iakovidis D, Houdeville C. et al. Artificial intelligence in small bowel capsule endoscopy - current status, challenges and future promise. J Gastroenterol Hepatol 2021; 36: 12-19
  • 5 Soffer S, Klang E, Shimon O. et al. Deep learning for wireless capsule endoscopy: a systematic review and meta-analysis. Gastrointestinal Endoscopy 2020; 92: 831-839
  • 6 Tsuboi A, Oka S, Aoyama K. et al. Artificial intelligence using a convolutional neural network for automatic detection of small-bowel angioectasia in capsule endoscopy images. Digestive Endoscopy 2020; 32: 382-390
  • 7 Trasolini R, Byrne MF. Artificial intelligence and deep learning for small bowel capsule endoscopy. Dig Endosc 2021; 33: 290-297
  • 8 Vieira PM, Freitas NR, Lima VB. et al. Multi-pathology detection and lesion localization in WCE videos by using the instance segmentation approach. Artif Intell Med 2021; 119: 102141
  • 9 Beg S, Card T, Sidhu R. et al. The impact of reader fatigue on the accuracy of capsule endoscopy interpretation. Dig Liver Dis 2021; 53: 1028-1033
  • 10 Messmann H, Bisschops R, Antonelli G. et al. Expected value of artificial intelligence in gastrointestinal endoscopy: European Society of Gastrointestinal Endoscopy (ESGE) Position Statement. Endoscopy 2022; 54: 1211-1231
  • 11 Xie X, Xiao Y-F, Zhao X-Y. et al. Development and validation of an artificial intelligence model for small bowel capsule endoscopy. Video review. JAMA Network Open 2022; 5: e2221992
  • 12 Korman LY. Capsule Endoscopy Structured Terminology (CEST): Proposal of a standardized and structured terminology for reporting capsule endoscopy procedures. Endoscopy 2005; 37: 951-959
  • 13 Pennazio M, Spada C, Eliakim R. et al. Small-bowel capsule endoscopy and device-assisted enteroscopy for diagnosis and treatment of small-bowel disorders: European Society of Gastrointestinal Endoscopy (ESGE) Clinical Guideline. Endoscopy 2015; 47: 352-376
  • 14 Shim KN, Moon JS, Chang DK. et al. Guideline for capsule endoscopy: obscure gastrointestinal bleeding. Clin Endosc 2013; 46: 45-53
  • 15 Brotz C, Nandi N, Conn M. et al. A validation study of 3 grading systems to evaluate small-bowel cleansing for wireless capsule endoscopy: a quantitative index, a qualitative evaluation, and an overall adequacy assessment. Gastrointest Endosc 2009; 69: 262-270
  • 16 Cotter J, Dias de Castro F, Magalhães J. et al. Validation of the Lewis score for the evaluation of small-bowel Crohn's disease activity. Endoscopy 2015; 47: 330-335
  • 17 Zheng Y, Hawkins L, Wolff J. et al. Detection of lesions during capsule endoscopy: physician performance is disappointing. Am J Gastroenterol 2012; 107: 554-560
  • 18 Beg S, Card T, Sidhu R. et al. ADWE-07 How many capsule endoscopy cases can be read before accuracy is affected?. Gut 2018; 67: A164-A164