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

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.

• References

• 1 Niv Y. Efficiency of bowel preparation for capsule endoscopy examination: a meta-analysis. World J Gastroenterol 2008; 14: 1313-1317
• 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
• 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
• 5 Enns RA, Hookey L, Armstrong D. et al. Clinical practice guidelines for the use of video capsule endoscopy. Gastroenterology 2017; 152: 497-514
• 6 Le Berre C, Sandborn WJ, Aridhi S. et al. Application of artificial intelligence to gastroenterology and hepatology. Gastroenterology 2020; 158: 76-94
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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