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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
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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

 

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