Multi-criterion, automated, high-performance, rapid tool for assessing mucosal visualization quality of still images in small bowel capsule endoscopy

Sarra Oumrani; Aymeric Histace; Einas Abou Ali; Olivia Pietri; Aymeric Becq; Guy Houist; Isabelle Nion-Larmurier; Marine Camus; Christian Florent; Xavier Dray

doi:10.1055/a-0918-5883

Endoscopy International Open, Table of Contents

CC BY-NC-ND 4.0 · Endosc Int Open 2019; 07(08): E944-E948
DOI: 10.1055/a-0918-5883

Original article

Multi-criterion, automated, high-performance, rapid tool for assessing mucosal visualization quality of still images in small bowel capsule endoscopy

Authors

Sarra Oumrani

¹Assistance Publique-Hôpitaux de Paris (APHP), Department of Hepatology and Gastroenterology, Saint Antoine Hospital, Paris, France
Aymeric Histace

³ETIS, Université de Cergy-Pontoise, ENSEA, CNRS, 95014 Cergy-Pontoise Cedex, France
Einas Abou Ali

¹Assistance Publique-Hôpitaux de Paris (APHP), Department of Hepatology and Gastroenterology, Saint Antoine Hospital, Paris, France
Olivia Pietri

¹Assistance Publique-Hôpitaux de Paris (APHP), Department of Hepatology and Gastroenterology, Saint Antoine Hospital, Paris, France
Aymeric Becq

¹Assistance Publique-Hôpitaux de Paris (APHP), Department of Hepatology and Gastroenterology, Saint Antoine Hospital, Paris, France

²Sorbonne University, Paris, France
Guy Houist

¹Assistance Publique-Hôpitaux de Paris (APHP), Department of Hepatology and Gastroenterology, Saint Antoine Hospital, Paris, France
Isabelle Nion-Larmurier

¹Assistance Publique-Hôpitaux de Paris (APHP), Department of Hepatology and Gastroenterology, Saint Antoine Hospital, Paris, France
Marine Camus

¹Assistance Publique-Hôpitaux de Paris (APHP), Department of Hepatology and Gastroenterology, Saint Antoine Hospital, Paris, France

²Sorbonne University, Paris, France
Christian Florent

¹Assistance Publique-Hôpitaux de Paris (APHP), Department of Hepatology and Gastroenterology, Saint Antoine Hospital, Paris, France

²Sorbonne University, Paris, France
Xavier Dray

¹Assistance Publique-Hôpitaux de Paris (APHP), Department of Hepatology and Gastroenterology, Saint Antoine Hospital, Paris, France

²Sorbonne University, Paris, France

³ETIS, Université de Cergy-Pontoise, ENSEA, CNRS, 95014 Cergy-Pontoise Cedex, France

Abstract

Background and study aims Capsule endoscopy (CE) is the preferred method for small bowel (SB) exploration. Its diagnostic yield can be reduced by poor mucosal visualization. We aimed to evaluate three electronic parameters – colorimetry, abundance of bubbles, and brightness – to assess the adequacy of mucosal visualization of SB-CE images.

Patients and methods Six-hundred still images were randomly extracted from 30 complete and normal SB-CEs. Three experts independently evaluated these images according to a 10-point assessment grid. Any frame with a mean score above seven was considered adequately cleansed. Each image was analyzed electronically according to the three preset parameters, individually and then combined, with the experts' score as reference. A random forests methodology was used for machine learning and testing.

Results The combination of the three electronic parameters achieved better discrimination of adequately from inadequately cleansed frames as compared to each individual parameter taken separately (sensitivity 90.0 % [95 %C. I. 84.1 – 95.9], specificity 87.7 % [95 %C. I. 81.3 – 94.2]).

Conclusion This multi-criterion score constitutes a comprehensive, reproducible, reliable, automated and rapid cleansing score for SB-CE frames. A patent is pending at the European patent office.

Full Text

References

References
1 Lai EJ, Calderwood AH, Doros G. et al. The Boston bowel preparation scale: a valid and reliable instrument for colonoscopy-oriented research. Gastrointest Endosc 2009; 69: 620-625
2 Gkolfakis P, Tziatzios G, Dimitriadis GD. et al. Meta-analysis of randomized controlled trials challenging the usefulness of purgative preparation before small-bowel video capsule endoscopy. Endoscopy 2018; 50: 671-683.3
3 Ponte A, Pinho R, Rodrigues A. et al. Review of small-bowel cleansing scales in capsule endoscopy: A panoply of choices. World J Gastrointest Endosc 2016 8: 600-609
4 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
5 Albert J, Göbel C-M, Lesske J. et al. Simethicone for small bowel preparation for capsule endoscopy: a systematic, single-blinded, controlled study. Gastrointest Endosc 2004; 59: 487-491
6 Ninomiya K, Yao K, Matsui T. et al. Effectiveness of magnesium citrate as preparation for capsule endoscopy: a randomized, prospective, open-label, inter-group trial. Digestion 2012; 86: 27-33
7 Park SC, Keum B, Hyun JJ. et al. A novel cleansing score system for capsule endoscopy. World J Gastroenterol 2010; 16: 875-880
8 Goyal J, Goel A, McGwin G. et al. Analysis of a grading system to assess the quality of small-bowel preparation for capsule endoscopy: in search of the Holy Grail. Endosc Int Open 2014; 2: E183-E186
9 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
10 Abou Ali E, Histace A, Camus M. et al. Development and validation of a computed assessment of cleansing score for evaluation of the quality of small-bowel visualization in capsule endoscopy. Endosc Int Open 2018; 6: E646-E651
11 Pietri O, Rezgui G, Histace A. et al. Development and validation of a highly sensitive and specific automated algorithm to evaluate the bubbles abundance in small bowel capsule endoscopy. Endosc Int Open 2018; 06: E462-E469
12 Haralick R, Shanmugam K, Dinstein I. Textural features for image classification. IEEE Transactions on Systems Man and Cybernetics 1973; 3: 610-621
13 Breiman L. Random forests. Mach Learn 2001; 45: 5-32
14 Kohavi R. A Study of Cross-validation and Bootstrap for Accuracy Estimation and Model Selection. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence. Volume 2. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc; 1995: 1137-1143