Development and validation of an automated algorithm to evaluate the abundance of bubbles in small bowel capsule endoscopy

• Main Authors: Olivia Pietri, Gada Rezgui, Aymeric Histace, Marine Camus, Isabelle Nion-Larmurier, Cynthia Li, Aymeric Becq, Einas Abou Ali, Olivier Romain, Ulriikka Chaput, Philippe Marteau, Christian Florent, Xavier Dray
• Format: Article
• Language: English
• Published: Georg Thieme Verlag KG 2018-03-01
• Series: Endoscopy International Open
• Online Access: http://www.thieme-connect.de/DOI/DOI?10.1055/a-0573-1044

Background and study aims Bubbles can impair visualization of the small bowel (SB) mucosa during capsule endoscopy (CE). We aimed to develop and validate a computed algorithm that would allow evaluation of the abundance of bubbles in SB-CE still frames. Patients and methods Two sets of 200 SB-CE normal still frames were created. Two experienced SB-CE readers analyzed both sets of images twice, in a random order. Each still frame was categorized as presenting with < 10 % or ≥ 10 % of bubbles. Reproducibility (κ), sensitivity (Se), specificity (Sp), receiver operating characteristic curve, and calculation time were measured for different algorithms (Grey-level of co-occurrence matrix [GLCM], fractal dimension, Hough transform, and speeded-up robust features [SURF]) using the experts’ analysis as reference. Algorithms with highest reproducibility, Se and Sp were then selected for a validation step on the second set of frames. Criteria for validation were κ = 1, Se ≥ 90 %, Sp ≥ 85 %, and a calculation time < 1 second. Results Both SURF and GLCM algorithms had high operating points (Se and Sp over 90 %) and a perfect reproducibility (κ = 1). The validation step showed the GLCM detector strategy had the best diagnostic performances, with a Se of 95.79 %, a Sp of 95.19 %, and a calculation time of 0.037 seconds per frame. Conclusion A computed algorithm based on a GLCM detector strategy had high diagnostic performance allowing assessment of the abundance of bubbles in SB-CE still frames. This algorithm could be of interest for clinical use (quality reporting) and for research purposes (objective comparison tool of different preparations).