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Article Dans Une Revue Endoscopy Année : 2020

A Neural Network-based Algorithm for Assessing the Cleanliness of Small Bowel during Capsule Endoscopy

Résumé

Background and Aims. Cleanliness scores in small bowel (SB) capsule endoscopy (CE) have poor reproducibility. The aim of this study was to evaluate a neural network (NN)-based algorithm for automated assessment of the SB cleanliness during CE. Methods: First, 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. Results: Using a threshold of 79% adequate still frames per video to achieve the best performance, the algorithm yielded a sensitivity of 90.3%, a specificity of 83.3%, and an accuracy of 89.7%. The reproducibility was perfect. The mean calculation time per video was 3 ± 1 minutes. Conclusion: This NN-based algorithm allowing automatic assessment of SB cleanliness during CE was highly sensitive and paves the way for automated, standardized SBCE reports.
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Dates et versions

hal-02984556 , version 1 (31-10-2020)

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Citer

Romain Leenhardt, Marc Souchaud, Guy Houist, J.L. Lemouel, J.C. Saurin, et al.. A Neural Network-based Algorithm for Assessing the Cleanliness of Small Bowel during Capsule Endoscopy. Endoscopy, 2020, 53 (09), pp.932-936. ⟨10.1055/a-1301-3841⟩. ⟨hal-02984556⟩
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