Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Bu, Jin, Lin, Yu, Qing, Li-Qiong, Hu, Gang, Jiang, Pei, Hu, Hai-Feng, Shen, Er-Xia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
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
_version_ 1783717592146378752
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
work_keys_str_mv AT bujin predictionofskindiseaseusinganewcytologicaltaxonomybasedoncytologyandpathologywithdeepresiduallearningmethod
AT linyu predictionofskindiseaseusinganewcytologicaltaxonomybasedoncytologyandpathologywithdeepresiduallearningmethod
AT qingliqiong predictionofskindiseaseusinganewcytologicaltaxonomybasedoncytologyandpathologywithdeepresiduallearningmethod
AT hugang predictionofskindiseaseusinganewcytologicaltaxonomybasedoncytologyandpathologywithdeepresiduallearningmethod
AT jiangpei predictionofskindiseaseusinganewcytologicaltaxonomybasedoncytologyandpathologywithdeepresiduallearningmethod
AT huhaifeng predictionofskindiseaseusinganewcytologicaltaxonomybasedoncytologyandpathologywithdeepresiduallearningmethod
AT shenerxia predictionofskindiseaseusinganewcytologicaltaxonomybasedoncytologyandpathologywithdeepresiduallearningmethod