Hospital for Skin Disease (Institute of Dermatology), Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, 210042, Jiangsu, China.
Guangzhou South China Biomedical Research Institute, Co., Ltd, Guangzhou, 510275, Guangdong, China.
Sci Rep. 2021 Jul 2;11(1):13764. doi: 10.1038/s41598-021-92848-y.
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.
随着人工智能技术的发展，皮肤病的分类技术得到了改进。然而，目前很少有研究关注全球用于皮肤病分类的国际疾病分类第十版（ICD-10）中关于皮肤和皮下组织疾病的现行分类系统。本研究旨在基于细胞学和病理学开发一种新的皮肤病分类法，并测试其对皮肤病的预测效果与 ICD-10 的比较。基于皮肤细胞学和病理学，开发了一个包含 6 个层次（项目 2-4）的新分类法（分类法 2），代表了按树状结构排列的个体疾病，有 3 个根节点代表：（1）角质形成疾病，（2）黑素生成疾病，和（3）与非角质形成细胞和非黑素细胞相关的疾病。新分类法（分类法 2）的预测效果，包括准确性、精确性、召回率、F1 和 Kappa，与 ICD-10 皮肤病和皮下组织疾病（分类法 1，项目 1）通过深度学习方法进行了比较。对于每个项目，将 2/3 的图像纳入训练组，其余 1/3 的图像根据类别（类别）作为分层变量作为测试组。来自分类法 2 的项目（2 和 3）的训练组和测试组在皮肤病的预测方面具有更高的 F1 和 Kappa 评分，且没有统计学意义，但来自分类法 2 的项目 4 的训练组和测试组在皮肤病的预测方面具有统计学意义更高的 F1 评分（分别为 P=0.025 和 0.005）。结果表明，基于细胞学和病理学开发的新分类法在皮肤病的预测效果方面总体优于 ICD-10 皮肤病和皮下组织疾病的分类法。与目前使用的 ICD-10 分类系统相比，分类法 2 的第 5 级（项目 4）在 AI 辅助诊断系统的未知数据扩展方面表现更好，可能具有皮肤科临床应用价值。
