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A knowledge-enhanced transform-based multimodal classifier for microbial keratitis identification
Microbial keratitis, a nonviral corneal infection caused by bacteria, fungi, and protozoa, is an urgent condition in ophthalmology requiring prompt treatment in order to prevent severe complications of corneal perforation and vision loss. It is difficult to distinguish between bacterial and fungal k...
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/PMC10238367/ https://www.ncbi.nlm.nih.gov/pubmed/37268729 http://dx.doi.org/10.1038/s41598-023-36024-4 |
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author | Wu, Jianfeng Yuan, Zhouhang Fang, Zhengqing Huang, Zhengxing Xu, Yesheng Xie, Wenjia Wu, Fei Yao, Yu-Feng |
author_facet | Wu, Jianfeng Yuan, Zhouhang Fang, Zhengqing Huang, Zhengxing Xu, Yesheng Xie, Wenjia Wu, Fei Yao, Yu-Feng |
author_sort | Wu, Jianfeng |
collection | PubMed |
description | Microbial keratitis, a nonviral corneal infection caused by bacteria, fungi, and protozoa, is an urgent condition in ophthalmology requiring prompt treatment in order to prevent severe complications of corneal perforation and vision loss. It is difficult to distinguish between bacterial and fungal keratitis from image unimodal alone, as the characteristics of the sample images themselves are very close. Therefore, this study aims to develop a new deep learning model called knowledge-enhanced transform-based multimodal classifier that exploited the potential of slit-lamp images along with treatment texts to identify bacterial keratitis (BK) and fungal keratitis (FK). The model performance was evaluated in terms of the accuracy, specificity, sensitivity and the area under the curve (AUC). 704 images from 352 patients were divided into training, validation and testing set. In the testing set, our model reached the best accuracy was 93%, sensitivity was 0.97(95% CI [0.84,1]), specificity was 0.92(95% CI [0.76,0.98]) and AUC was 0.94(95% CI [0.92,0.96]), exceeding the benchmark accuracy of 0.86. The diagnostic average accuracies of BK ranged from 81 to 92%, respectively and those for FK were 89–97%. It is the first study to focus on the influence of disease changes and medication interventions on infectious keratitis and our model outperformed the benchmark models and reaching the state-of-the-art performance. |
format | Online Article Text |
id | pubmed-10238367 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-102383672023-06-04 A knowledge-enhanced transform-based multimodal classifier for microbial keratitis identification Wu, Jianfeng Yuan, Zhouhang Fang, Zhengqing Huang, Zhengxing Xu, Yesheng Xie, Wenjia Wu, Fei Yao, Yu-Feng Sci Rep Article Microbial keratitis, a nonviral corneal infection caused by bacteria, fungi, and protozoa, is an urgent condition in ophthalmology requiring prompt treatment in order to prevent severe complications of corneal perforation and vision loss. It is difficult to distinguish between bacterial and fungal keratitis from image unimodal alone, as the characteristics of the sample images themselves are very close. Therefore, this study aims to develop a new deep learning model called knowledge-enhanced transform-based multimodal classifier that exploited the potential of slit-lamp images along with treatment texts to identify bacterial keratitis (BK) and fungal keratitis (FK). The model performance was evaluated in terms of the accuracy, specificity, sensitivity and the area under the curve (AUC). 704 images from 352 patients were divided into training, validation and testing set. In the testing set, our model reached the best accuracy was 93%, sensitivity was 0.97(95% CI [0.84,1]), specificity was 0.92(95% CI [0.76,0.98]) and AUC was 0.94(95% CI [0.92,0.96]), exceeding the benchmark accuracy of 0.86. The diagnostic average accuracies of BK ranged from 81 to 92%, respectively and those for FK were 89–97%. It is the first study to focus on the influence of disease changes and medication interventions on infectious keratitis and our model outperformed the benchmark models and reaching the state-of-the-art performance. Nature Publishing Group UK 2023-06-02 /pmc/articles/PMC10238367/ /pubmed/37268729 http://dx.doi.org/10.1038/s41598-023-36024-4 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 Wu, Jianfeng Yuan, Zhouhang Fang, Zhengqing Huang, Zhengxing Xu, Yesheng Xie, Wenjia Wu, Fei Yao, Yu-Feng A knowledge-enhanced transform-based multimodal classifier for microbial keratitis identification |
title | A knowledge-enhanced transform-based multimodal classifier for microbial keratitis identification |
title_full | A knowledge-enhanced transform-based multimodal classifier for microbial keratitis identification |
title_fullStr | A knowledge-enhanced transform-based multimodal classifier for microbial keratitis identification |
title_full_unstemmed | A knowledge-enhanced transform-based multimodal classifier for microbial keratitis identification |
title_short | A knowledge-enhanced transform-based multimodal classifier for microbial keratitis identification |
title_sort | knowledge-enhanced transform-based multimodal classifier for microbial keratitis identification |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10238367/ https://www.ncbi.nlm.nih.gov/pubmed/37268729 http://dx.doi.org/10.1038/s41598-023-36024-4 |
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