Prediction of skin disease using a new cytological taxonomy based on cytology and pathology with deep residual learning method

Abstract 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 subcu...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Jin Bu, Yu Lin, Li-Qiong Qing, Gang Hu, Pei Jiang, Hai-Feng Hu, Er-Xia Shen
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c993e863711e46bbac831487fe8d208b
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:c993e863711e46bbac831487fe8d208b
record_format	dspace
spelling	oai:doaj.org-article:c993e863711e46bbac831487fe8d208b2021-12-02T14:33:51ZPrediction of skin disease using a new cytological taxonomy based on cytology and pathology with deep residual learning method10.1038/s41598-021-92848-y2045-2322https://doaj.org/article/c993e863711e46bbac831487fe8d208b2021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-92848-yhttps://doaj.org/toc/2045-2322Abstract 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.Jin BuYu LinLi-Qiong QingGang HuPei JiangHai-Feng HuEr-Xia ShenNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jin Bu Yu Lin Li-Qiong Qing Gang Hu Pei Jiang Hai-Feng Hu Er-Xia Shen Prediction of skin disease using a new cytological taxonomy based on cytology and pathology with deep residual learning method
description	Abstract 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	article
author	Jin Bu Yu Lin Li-Qiong Qing Gang Hu Pei Jiang Hai-Feng Hu Er-Xia Shen
author_facet	Jin Bu Yu Lin Li-Qiong Qing Gang Hu Pei Jiang Hai-Feng Hu Er-Xia Shen
author_sort	Jin Bu
title	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_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_sort	prediction of skin disease using a new cytological taxonomy based on cytology and pathology with deep residual learning method
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/c993e863711e46bbac831487fe8d208b
work_keys_str_mv	AT jinbu predictionofskindiseaseusinganewcytologicaltaxonomybasedoncytologyandpathologywithdeepresiduallearningmethod AT yulin predictionofskindiseaseusinganewcytologicaltaxonomybasedoncytologyandpathologywithdeepresiduallearningmethod AT liqiongqing predictionofskindiseaseusinganewcytologicaltaxonomybasedoncytologyandpathologywithdeepresiduallearningmethod AT ganghu predictionofskindiseaseusinganewcytologicaltaxonomybasedoncytologyandpathologywithdeepresiduallearningmethod AT peijiang predictionofskindiseaseusinganewcytologicaltaxonomybasedoncytologyandpathologywithdeepresiduallearningmethod AT haifenghu predictionofskindiseaseusinganewcytologicaltaxonomybasedoncytologyandpathologywithdeepresiduallearningmethod AT erxiashen predictionofskindiseaseusinganewcytologicaltaxonomybasedoncytologyandpathologywithdeepresiduallearningmethod
_version_	1718391158430760960

Prediction of skin disease using a new cytological taxonomy based on cytology and pathology with deep residual learning method

Ejemplares similares