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Use of U-Net Convolutional Neural Networks for Automated Segmentation of Fecal Material for Objective Evaluation of Bowel Preparation Quality in Colonoscopy
Background: Adequate bowel cleansing is important for colonoscopy performance evaluation. Current bowel cleansing evaluation scales are subjective, with a wide variation in consistency among physicians and low reported rates of accuracy. We aim to use machine learning to develop a fully automatic se...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8947406/ https://www.ncbi.nlm.nih.gov/pubmed/35328166 http://dx.doi.org/10.3390/diagnostics12030613 |
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author | Wang, Yen-Po Jheng, Ying-Chun Sung, Kuang-Yi Lin, Hung-En Hsin, I-Fang Chen, Ping-Hsien Chu, Yuan-Chia Lu, David Wang, Yuan-Jen Hou, Ming-Chih Lee, Fa-Yauh Lu, Ching-Liang |
author_facet | Wang, Yen-Po Jheng, Ying-Chun Sung, Kuang-Yi Lin, Hung-En Hsin, I-Fang Chen, Ping-Hsien Chu, Yuan-Chia Lu, David Wang, Yuan-Jen Hou, Ming-Chih Lee, Fa-Yauh Lu, Ching-Liang |
author_sort | Wang, Yen-Po |
collection | PubMed |
description | Background: Adequate bowel cleansing is important for colonoscopy performance evaluation. Current bowel cleansing evaluation scales are subjective, with a wide variation in consistency among physicians and low reported rates of accuracy. We aim to use machine learning to develop a fully automatic segmentation method for the objective evaluation of the adequacy of colon preparation. Methods: Colonoscopy videos were retrieved from a video data cohort and transferred to qualified images, which were randomly divided into training, validation, and verification datasets. The fecal residue was manually segmented. A deep learning model based on the U-Net convolutional network architecture was developed to perform automatic segmentation. The performance of the automatic segmentation was evaluated on the overlap area with the manual segmentation. Results: A total of 10,118 qualified images from 119 videos were obtained. The model averaged 0.3634 s to segmentate one image automatically. The models produced a strong high-overlap area with manual segmentation, with 94.7% ± 0.67% of that area predicted by our AI model, which correlated well with the area measured manually (r = 0.915, p < 0.001). The AI system can be applied in real-time qualitatively and quantitatively. Conclusions: We established a fully automatic segmentation method to rapidly and accurately mark the fecal residue-coated mucosa for the objective evaluation of colon preparation. |
format | Online Article Text |
id | pubmed-8947406 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-89474062022-03-25 Use of U-Net Convolutional Neural Networks for Automated Segmentation of Fecal Material for Objective Evaluation of Bowel Preparation Quality in Colonoscopy Wang, Yen-Po Jheng, Ying-Chun Sung, Kuang-Yi Lin, Hung-En Hsin, I-Fang Chen, Ping-Hsien Chu, Yuan-Chia Lu, David Wang, Yuan-Jen Hou, Ming-Chih Lee, Fa-Yauh Lu, Ching-Liang Diagnostics (Basel) Article Background: Adequate bowel cleansing is important for colonoscopy performance evaluation. Current bowel cleansing evaluation scales are subjective, with a wide variation in consistency among physicians and low reported rates of accuracy. We aim to use machine learning to develop a fully automatic segmentation method for the objective evaluation of the adequacy of colon preparation. Methods: Colonoscopy videos were retrieved from a video data cohort and transferred to qualified images, which were randomly divided into training, validation, and verification datasets. The fecal residue was manually segmented. A deep learning model based on the U-Net convolutional network architecture was developed to perform automatic segmentation. The performance of the automatic segmentation was evaluated on the overlap area with the manual segmentation. Results: A total of 10,118 qualified images from 119 videos were obtained. The model averaged 0.3634 s to segmentate one image automatically. The models produced a strong high-overlap area with manual segmentation, with 94.7% ± 0.67% of that area predicted by our AI model, which correlated well with the area measured manually (r = 0.915, p < 0.001). The AI system can be applied in real-time qualitatively and quantitatively. Conclusions: We established a fully automatic segmentation method to rapidly and accurately mark the fecal residue-coated mucosa for the objective evaluation of colon preparation. MDPI 2022-03-01 /pmc/articles/PMC8947406/ /pubmed/35328166 http://dx.doi.org/10.3390/diagnostics12030613 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Wang, Yen-Po Jheng, Ying-Chun Sung, Kuang-Yi Lin, Hung-En Hsin, I-Fang Chen, Ping-Hsien Chu, Yuan-Chia Lu, David Wang, Yuan-Jen Hou, Ming-Chih Lee, Fa-Yauh Lu, Ching-Liang Use of U-Net Convolutional Neural Networks for Automated Segmentation of Fecal Material for Objective Evaluation of Bowel Preparation Quality in Colonoscopy |
title | Use of U-Net Convolutional Neural Networks for Automated Segmentation of Fecal Material for Objective Evaluation of Bowel Preparation Quality in Colonoscopy |
title_full | Use of U-Net Convolutional Neural Networks for Automated Segmentation of Fecal Material for Objective Evaluation of Bowel Preparation Quality in Colonoscopy |
title_fullStr | Use of U-Net Convolutional Neural Networks for Automated Segmentation of Fecal Material for Objective Evaluation of Bowel Preparation Quality in Colonoscopy |
title_full_unstemmed | Use of U-Net Convolutional Neural Networks for Automated Segmentation of Fecal Material for Objective Evaluation of Bowel Preparation Quality in Colonoscopy |
title_short | Use of U-Net Convolutional Neural Networks for Automated Segmentation of Fecal Material for Objective Evaluation of Bowel Preparation Quality in Colonoscopy |
title_sort | use of u-net convolutional neural networks for automated segmentation of fecal material for objective evaluation of bowel preparation quality in colonoscopy |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8947406/ https://www.ncbi.nlm.nih.gov/pubmed/35328166 http://dx.doi.org/10.3390/diagnostics12030613 |
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