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Automatic evaluation of degree of cleanliness in capsule endoscopy based on a novel CNN architecture
Capsule endoscopy (CE) is a widely used, minimally invasive alternative to traditional endoscopy that allows visualisation of the entire small intestine. Patient preparation can help to obtain a cleaner intestine and thus better visibility in the resulting videos. However, studies on the most effect...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7573687/ https://www.ncbi.nlm.nih.gov/pubmed/33077755 http://dx.doi.org/10.1038/s41598-020-74668-8 |
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author | Noorda, Reinier Nevárez, Andrea Colomer, Adrián Pons Beltrán, Vicente Naranjo, Valery |
author_facet | Noorda, Reinier Nevárez, Andrea Colomer, Adrián Pons Beltrán, Vicente Naranjo, Valery |
author_sort | Noorda, Reinier |
collection | PubMed |
description | Capsule endoscopy (CE) is a widely used, minimally invasive alternative to traditional endoscopy that allows visualisation of the entire small intestine. Patient preparation can help to obtain a cleaner intestine and thus better visibility in the resulting videos. However, studies on the most effective preparation method are conflicting due to the absence of objective, automatic cleanliness evaluation methods. In this work, we aim to provide such a method capable of presenting results on an intuitive scale, with a relatively light-weight novel convolutional neural network architecture at its core. We trained our model using 5-fold cross-validation on an extensive data set of over 50,000 image patches, collected from 35 different CE procedures, and compared it with state-of-the-art classification methods. From the patch classification results, we developed a method to automatically estimate pixel-level probabilities and deduce cleanliness evaluation scores through automatically learnt thresholds. We then validated our method in a clinical setting on 30 newly collected CE videos, comparing the resulting scores to those independently assigned by human specialists. We obtained the highest classification accuracy for the proposed method (95.23%), with significantly lower average prediction times than for the second-best method. In the validation of our method, we found acceptable agreement with two human specialists compared to interhuman agreement, showing its validity as an objective evaluation method. |
format | Online Article Text |
id | pubmed-7573687 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-75736872020-10-21 Automatic evaluation of degree of cleanliness in capsule endoscopy based on a novel CNN architecture Noorda, Reinier Nevárez, Andrea Colomer, Adrián Pons Beltrán, Vicente Naranjo, Valery Sci Rep Article Capsule endoscopy (CE) is a widely used, minimally invasive alternative to traditional endoscopy that allows visualisation of the entire small intestine. Patient preparation can help to obtain a cleaner intestine and thus better visibility in the resulting videos. However, studies on the most effective preparation method are conflicting due to the absence of objective, automatic cleanliness evaluation methods. In this work, we aim to provide such a method capable of presenting results on an intuitive scale, with a relatively light-weight novel convolutional neural network architecture at its core. We trained our model using 5-fold cross-validation on an extensive data set of over 50,000 image patches, collected from 35 different CE procedures, and compared it with state-of-the-art classification methods. From the patch classification results, we developed a method to automatically estimate pixel-level probabilities and deduce cleanliness evaluation scores through automatically learnt thresholds. We then validated our method in a clinical setting on 30 newly collected CE videos, comparing the resulting scores to those independently assigned by human specialists. We obtained the highest classification accuracy for the proposed method (95.23%), with significantly lower average prediction times than for the second-best method. In the validation of our method, we found acceptable agreement with two human specialists compared to interhuman agreement, showing its validity as an objective evaluation method. Nature Publishing Group UK 2020-10-19 /pmc/articles/PMC7573687/ /pubmed/33077755 http://dx.doi.org/10.1038/s41598-020-74668-8 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Noorda, Reinier Nevárez, Andrea Colomer, Adrián Pons Beltrán, Vicente Naranjo, Valery Automatic evaluation of degree of cleanliness in capsule endoscopy based on a novel CNN architecture |
title | Automatic evaluation of degree of cleanliness in capsule endoscopy based on a novel CNN architecture |
title_full | Automatic evaluation of degree of cleanliness in capsule endoscopy based on a novel CNN architecture |
title_fullStr | Automatic evaluation of degree of cleanliness in capsule endoscopy based on a novel CNN architecture |
title_full_unstemmed | Automatic evaluation of degree of cleanliness in capsule endoscopy based on a novel CNN architecture |
title_short | Automatic evaluation of degree of cleanliness in capsule endoscopy based on a novel CNN architecture |
title_sort | automatic evaluation of degree of cleanliness in capsule endoscopy based on a novel cnn architecture |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7573687/ https://www.ncbi.nlm.nih.gov/pubmed/33077755 http://dx.doi.org/10.1038/s41598-020-74668-8 |
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