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Deep learning model for distinguishing Mayo endoscopic subscore 0 and 1 in patients with ulcerative colitis
The aim of this study was to address the issue of differentiating between Mayo endoscopic subscore (MES) 0 and MES 1 using a deep learning model. A dataset of 492 ulcerative colitis (UC) patients who demonstrated MES improvement between January 2018 and December 2019 at Samsung Medical Center was ut...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10344868/ https://www.ncbi.nlm.nih.gov/pubmed/37443370 http://dx.doi.org/10.1038/s41598-023-38206-6 |
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author | Kim, Ji Eun Choi, Yoon Ho Lee, Yeong Chan Seong, Gyeol Song, Joo Hye Kim, Tae Jun Kim, Eun Ran Hong, Sung Noh Chang, Dong Kyung Kim, Young-Ho Shin, Soo-Yong |
author_facet | Kim, Ji Eun Choi, Yoon Ho Lee, Yeong Chan Seong, Gyeol Song, Joo Hye Kim, Tae Jun Kim, Eun Ran Hong, Sung Noh Chang, Dong Kyung Kim, Young-Ho Shin, Soo-Yong |
author_sort | Kim, Ji Eun |
collection | PubMed |
description | The aim of this study was to address the issue of differentiating between Mayo endoscopic subscore (MES) 0 and MES 1 using a deep learning model. A dataset of 492 ulcerative colitis (UC) patients who demonstrated MES improvement between January 2018 and December 2019 at Samsung Medical Center was utilized. Specifically, two representative images of the colon and rectum were selected from each patient, resulting in a total of 984 images for analysis. The deep learning model utilized in this study consisted of a convolutional neural network (CNN)-based encoder, with two auxiliary classifiers for the colon and rectum, as well as a final MES classifier that combined image features from both inputs. In the internal test, the model achieved an F1-score of 0.92, surpassing the performance of seven novice classifiers by an average margin of 0.11, and outperforming their consensus by 0.02. The area under the receiver operating characteristic curve (AUROC) was calculated to be 0.97 when considering MES 1 as positive, with an area under the precision-recall curve (AUPRC) of 0.98. In the external test using the Hyperkvasir dataset, the model achieved an F1-score of 0.89, AUROC of 0.86, and AUPRC of 0.97. The results demonstrate that the proposed CNN-based model, which integrates image features from both the colon and rectum, exhibits superior performance in accurately discriminating between MES 0 and MES 1 in patients with UC. |
format | Online Article Text |
id | pubmed-10344868 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-103448682023-07-15 Deep learning model for distinguishing Mayo endoscopic subscore 0 and 1 in patients with ulcerative colitis Kim, Ji Eun Choi, Yoon Ho Lee, Yeong Chan Seong, Gyeol Song, Joo Hye Kim, Tae Jun Kim, Eun Ran Hong, Sung Noh Chang, Dong Kyung Kim, Young-Ho Shin, Soo-Yong Sci Rep Article The aim of this study was to address the issue of differentiating between Mayo endoscopic subscore (MES) 0 and MES 1 using a deep learning model. A dataset of 492 ulcerative colitis (UC) patients who demonstrated MES improvement between January 2018 and December 2019 at Samsung Medical Center was utilized. Specifically, two representative images of the colon and rectum were selected from each patient, resulting in a total of 984 images for analysis. The deep learning model utilized in this study consisted of a convolutional neural network (CNN)-based encoder, with two auxiliary classifiers for the colon and rectum, as well as a final MES classifier that combined image features from both inputs. In the internal test, the model achieved an F1-score of 0.92, surpassing the performance of seven novice classifiers by an average margin of 0.11, and outperforming their consensus by 0.02. The area under the receiver operating characteristic curve (AUROC) was calculated to be 0.97 when considering MES 1 as positive, with an area under the precision-recall curve (AUPRC) of 0.98. In the external test using the Hyperkvasir dataset, the model achieved an F1-score of 0.89, AUROC of 0.86, and AUPRC of 0.97. The results demonstrate that the proposed CNN-based model, which integrates image features from both the colon and rectum, exhibits superior performance in accurately discriminating between MES 0 and MES 1 in patients with UC. Nature Publishing Group UK 2023-07-13 /pmc/articles/PMC10344868/ /pubmed/37443370 http://dx.doi.org/10.1038/s41598-023-38206-6 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Kim, Ji Eun Choi, Yoon Ho Lee, Yeong Chan Seong, Gyeol Song, Joo Hye Kim, Tae Jun Kim, Eun Ran Hong, Sung Noh Chang, Dong Kyung Kim, Young-Ho Shin, Soo-Yong Deep learning model for distinguishing Mayo endoscopic subscore 0 and 1 in patients with ulcerative colitis |
title | Deep learning model for distinguishing Mayo endoscopic subscore 0 and 1 in patients with ulcerative colitis |
title_full | Deep learning model for distinguishing Mayo endoscopic subscore 0 and 1 in patients with ulcerative colitis |
title_fullStr | Deep learning model for distinguishing Mayo endoscopic subscore 0 and 1 in patients with ulcerative colitis |
title_full_unstemmed | Deep learning model for distinguishing Mayo endoscopic subscore 0 and 1 in patients with ulcerative colitis |
title_short | Deep learning model for distinguishing Mayo endoscopic subscore 0 and 1 in patients with ulcerative colitis |
title_sort | deep learning model for distinguishing mayo endoscopic subscore 0 and 1 in patients with ulcerative colitis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10344868/ https://www.ncbi.nlm.nih.gov/pubmed/37443370 http://dx.doi.org/10.1038/s41598-023-38206-6 |
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