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Prediction of skin disease using a new cytological taxonomy based on cytology and pathology with deep residual learning method
With the development of artificial intelligence, technique improvement of the classification of skin disease is addressed. However, few study concerned on the current classification system of International Classification of Diseases, Tenth Revision (ICD)-10 on Diseases of the skin and subcutaneous t...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8253798/ https://www.ncbi.nlm.nih.gov/pubmed/34215767 http://dx.doi.org/10.1038/s41598-021-92848-y |
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author | Bu, Jin Lin, Yu Qing, Li-Qiong Hu, Gang Jiang, Pei Hu, Hai-Feng Shen, Er-Xia |
author_facet | Bu, Jin Lin, Yu Qing, Li-Qiong Hu, Gang Jiang, Pei Hu, Hai-Feng Shen, Er-Xia |
author_sort | Bu, Jin |
collection | PubMed |
description | With the development of artificial intelligence, technique improvement of the classification of skin disease is addressed. However, few study concerned on the current classification system of International Classification of Diseases, Tenth Revision (ICD)-10 on Diseases of the skin and subcutaneous tissue, which is now globally used for classification of skin disease. This study was aimed to develop a new taxonomy of skin disease based on cytology and pathology, and test its predictive effect on skin disease compared to ICD-10. A new taxonomy (Taxonomy 2) containing 6 levels (Project 2–4) was developed based on skin cytology and pathology, and represents individual diseases arranged in a tree structure with three root nodes representing: (1) Keratinogenic diseases, (2) Melanogenic diseases, and (3) Diseases related to non-keratinocytes and non-melanocytes. The predictive effects of the new taxonomy including accuracy, precision, recall, F1, and Kappa were compared with those of ICD-10 on Diseases of the skin and subcutaneous tissue (Taxonomy 1, Project 1) by Deep Residual Learning method. For each project, 2/3 of the images were included as training group, and the rest 1/3 of the images acted as test group according to the category (class) as the stratification variable. Both train and test groups in the Projects (2 and 3) from Taxonomy 2 had higher F1 and Kappa scores without statistical significance on the prediction of skin disease than the corresponding groups in the Project 1 from Taxonomy 1, however both train and test groups in Project 4 had a statistically significantly higher F1-score than the corresponding groups in Project 1 (P = 0.025 and 0.005, respectively). The results showed that the new taxonomy developed based on cytology and pathology has an overall better performance on predictive effect of skin disease than the ICD-10 on Diseases of the skin and subcutaneous tissue. The level 5 (Project 4) of Taxonomy 2 is better on extension to unknown data of diagnosis system assisted by AI compared to current used classification system from ICD-10, and may have the potential application value in clinic of dermatology. |
format | Online Article Text |
id | pubmed-8253798 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-82537982021-07-06 Prediction of skin disease using a new cytological taxonomy based on cytology and pathology with deep residual learning method Bu, Jin Lin, Yu Qing, Li-Qiong Hu, Gang Jiang, Pei Hu, Hai-Feng Shen, Er-Xia Sci Rep Article With the development of artificial intelligence, technique improvement of the classification of skin disease is addressed. However, few study concerned on the current classification system of International Classification of Diseases, Tenth Revision (ICD)-10 on Diseases of the skin and subcutaneous tissue, which is now globally used for classification of skin disease. This study was aimed to develop a new taxonomy of skin disease based on cytology and pathology, and test its predictive effect on skin disease compared to ICD-10. A new taxonomy (Taxonomy 2) containing 6 levels (Project 2–4) was developed based on skin cytology and pathology, and represents individual diseases arranged in a tree structure with three root nodes representing: (1) Keratinogenic diseases, (2) Melanogenic diseases, and (3) Diseases related to non-keratinocytes and non-melanocytes. The predictive effects of the new taxonomy including accuracy, precision, recall, F1, and Kappa were compared with those of ICD-10 on Diseases of the skin and subcutaneous tissue (Taxonomy 1, Project 1) by Deep Residual Learning method. For each project, 2/3 of the images were included as training group, and the rest 1/3 of the images acted as test group according to the category (class) as the stratification variable. Both train and test groups in the Projects (2 and 3) from Taxonomy 2 had higher F1 and Kappa scores without statistical significance on the prediction of skin disease than the corresponding groups in the Project 1 from Taxonomy 1, however both train and test groups in Project 4 had a statistically significantly higher F1-score than the corresponding groups in Project 1 (P = 0.025 and 0.005, respectively). The results showed that the new taxonomy developed based on cytology and pathology has an overall better performance on predictive effect of skin disease than the ICD-10 on Diseases of the skin and subcutaneous tissue. The level 5 (Project 4) of Taxonomy 2 is better on extension to unknown data of diagnosis system assisted by AI compared to current used classification system from ICD-10, and may have the potential application value in clinic of dermatology. Nature Publishing Group UK 2021-07-02 /pmc/articles/PMC8253798/ /pubmed/34215767 http://dx.doi.org/10.1038/s41598-021-92848-y Text en © The Author(s) 2021, corrected publication 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 Bu, Jin Lin, Yu Qing, Li-Qiong Hu, Gang Jiang, Pei Hu, Hai-Feng Shen, Er-Xia Prediction of skin disease using a new cytological taxonomy based on cytology and pathology with deep residual learning method |
title | Prediction of skin disease using a new cytological taxonomy based on cytology and pathology with deep residual learning method |
title_full | Prediction of skin disease using a new cytological taxonomy based on cytology and pathology with deep residual learning method |
title_fullStr | Prediction of skin disease using a new cytological taxonomy based on cytology and pathology with deep residual learning method |
title_full_unstemmed | Prediction of skin disease using a new cytological taxonomy based on cytology and pathology with deep residual learning method |
title_short | Prediction of skin disease using a new cytological taxonomy based on cytology and pathology with deep residual learning method |
title_sort | prediction of skin disease using a new cytological taxonomy based on cytology and pathology with deep residual learning method |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8253798/ https://www.ncbi.nlm.nih.gov/pubmed/34215767 http://dx.doi.org/10.1038/s41598-021-92848-y |
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