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

Autor:	Haifeng Hu, Gang Hu, Yu Lin, Erxia Shen, Li-Qiong Qing, Pei Jiang, Jin Bu
Jazyk:	angličtina
Rok vydání:	2021
Předmět:	0301 basic medicine Pathology medicine.medical_specialty Test group Cytodiagnosis Science Disease Article 030207 dermatology & venereal diseases 03 medical and health sciences Deep Learning 0302 clinical medicine Artificial Intelligence Cytology Taxonomy (general) Humans Medicine Skin Multidisciplinary business.industry Computational science Skin diseases 030104 developmental biology Disease Progression Learning methods Neural Networks Computer business Kappa
Zdroj:	Scientific Reports, Vol 11, Iss 1, Pp 1-9 (2021) Scientific Reports
ISSN:	2045-2322
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ed0452196876a5ec85b815a1c37ed576 https://doaj.org/article/c993e863711e46bbac831487fe8d208b Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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