Download PDF
Endoscopy 2021; 53(09): 932-936
DOI: 10.1055/a-1301-3841
Innovations and brief communications

A neural network-based algorithm for assessing the cleanliness of small bowel during capsule endoscopy

Romain Leenhardt 
1   Sorbonne University, Center for Digestive Endoscopy, Saint Antoine Hospital, APHP, Paris, France
2   ETIS, Université de Cergy-Pontoise, ENSEA, CNRS, Cergy-Pontoise, France
,
Marc Souchaud
2   ETIS, Université de Cergy-Pontoise, ENSEA, CNRS, Cergy-Pontoise, France
,
Guy Houist
3   Gastroenterology Department, Centre Hospitalier Sud Francilien, Corbeil-Essonnes, France
,
Jean-Philippe Le Mouel
4   Gastroenterology, Amiens University Hospital, Université de Picardie Jules Verne, Amiens, France
,
Jean-Christophe Saurin
5   Gastroenterology and Endoscopy Unit, Edouard Herriot Hospital, Lyon, France
,
Franck Cholet
6   Endoscopy Unit, CHU La Cavale Blanche, Brest, France
,
Gabriel Rahmi
7   Department of Gastroenterology and Digestive Endoscopy, Georges-Pompidou European Hospital, APHP, Paris, France
,
Chloé Leandri
8   Gastroenterology Department, Cochin Hospital, APHP, Paris, France
,
Aymeric Histace  ‡   *
2   ETIS, Université de Cergy-Pontoise, ENSEA, CNRS, Cergy-Pontoise, France
,
Xavier Dray‡   *
1   Sorbonne University, Center for Digestive Endoscopy, Saint Antoine Hospital, APHP, Paris, France
2   ETIS, Université de Cergy-Pontoise, ENSEA, CNRS, Cergy-Pontoise, France
› Author Affiliations
› Further Information
Also available at
    Permissions and Reprints

Abstract

Background Cleanliness scores in small-bowel capsule endoscopy (SBCE) have poor reproducibility. The aim of this study was to evaluate a neural network-based algorithm for automated assessment of small-bowel cleanliness during capsule endoscopy.

Methods 600 normal third-generation SBCE still frames were categorized as “adequate” or “inadequate” in terms of cleanliness by three expert readers, according to a 10-point scale, and served as a training database. Then, 156 third-generation SBCE recordings were categorized in a consensual manner as “adequate” or “inadequate” in terms of cleanliness; this testing database was split into two independent 78-video subsets for the tuning and evaluation of the algorithm, respectively.

Results Using a threshold of 79 % “adequate” still frames per video to achieve the best performance, the algorithm yielded a sensitivity of 90.3 %, specificity of 83.3 %, and accuracy of 89.7 %. The reproducibility was perfect. The mean calculation time per video was 3 (standard deviation 1) minutes.

Conclusion This neural network-based algorithm allowing automatic assessment of small-bowel cleanliness during capsule endoscopy was highly sensitive and paves the way for automated, standardized SBCE reports.

* These authors contributed equally.




Publication History

Received: 14 June 2020

Accepted: 30 October 2020

Accepted Manuscript online:
30 October 2020

Article published online:
12 January 2021

© 2020. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • References

  • 1 Niv Y. Efficiency of bowel preparation for capsule endoscopy examination: a meta-analysis. World J Gastroenterol 2008; 14: 1313-1317
    CrossrefPubMedSearch in Google Scholar
  • 2 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
    CrossrefPubMedSearch in Google Scholar
  • 3 Dray X, Houist G, Le Mouel JP. et al. Prospective evaluation of third-generation small bowel capsule endoscopy videos by independent readers demonstrates poor reproducibility of cleanliness classifications. Clin Res Hepatol Gastroenterol. (in press)
  • 4 Spada C, McNamara D, Despott EJ. et al. Performance measures for small-bowel endoscopy: a European Society of Gastrointestinal Endoscopy (ESGE) Quality Improvement Initiative. United Eur Gastroenterol J 2019; 7: 614-641
    CrossrefPubMedSearch in Google Scholar
  • 5 Enns RA, Hookey L, Armstrong D. et al. Clinical practice guidelines for the use of video capsule endoscopy. Gastroenterology 2017; 152: 497-514
    CrossrefPubMedSearch in Google Scholar
  • 6 Le Berre C, Sandborn WJ, Aridhi S. et al. Application of artificial intelligence to gastroenterology and hepatology. Gastroenterology 2020; 158: 76-94
    CrossrefPubMedSearch in Google Scholar
  • 7 Ding Z, Shi H, Zhang H. et al. Gastroenterologist-level identification of small bowel diseases and normal variants by capsule endoscopy using a deep-learning model. Gastroenterology 2019; 157: 1044-1054
    CrossrefPubMedSearch in Google Scholar
  • 8 Leenhardt R, Vasseur P, Li C. et al. A neural network algorithm for detection of GI angiectasia during small-bowel capsule endoscopy. Gastrointest Endosc 2019; 89: 189-194
    CrossrefPubMedSearch in Google Scholar
  • 9 Aoki T, Yamada A, Aoyama K. et al. Automatic detection of erosions and ulcerations in wireless capsule endoscopy images based on a deep convolutional neural network. Gastrointest Endosc 2019; 89: 357-363
    CrossrefPubMedSearch in Google Scholar
  • 10 Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. Presented at the International Conference on Learning Representations; 2015 May 7–9; San Diego, California. https://arxiv.org/pdf/1409.1556.pdf
  • 11 Oumrani S, Histace A, Abou Ali E. et al. Multi-criterion, automated, high-performance, rapid tool for assessing mucosal visualization quality of still images in small bowel capsule endoscopy. Endosc Int Open 2019; 7: E944-E948
    Thieme ConnectPubMedSearch in Google Scholar
  • 12 Cholet F, Rahmi G, Gaudric M. et al. Does polyethylene glycol cleansing purge improve video capsule endoscopy diagnostic yield in obscure gastrointestinal bleeding?. Endoscopy 2018; 50: S18
    Thieme ConnectPubMedSearch in Google Scholar
  • 13 Van Weyenberg SJB, De Leest HTJI, Mulder CJJ. Description of a novel grading system to assess the quality of bowel preparation in video capsule endoscopy. Endoscopy 2011; 43: 406-411
    Thieme ConnectPubMedSearch in Google Scholar
  • 14 Saito H, Aoki T, Aoyama K. et al. Automatic detection and classification of protruding lesions in wireless capsule endoscopy images based on a deep convolutional neural network. Gastrointest Endosc 2020; 92: 144-151
    CrossrefPubMedSearch in Google Scholar
  • 15 Lapalus M-G, Ben Soussan E, Saurin J-C. et al. Capsule endoscopy and bowel preparation with oral sodium phosphate: a prospective randomized controlled trial. Gastrointest Endosc 2008; 67: 1091-1096
    CrossrefPubMedSearch in Google Scholar