ArticleJournal of medical Internet research2021
The Classification of Six Common Skin Diseases Based on Xiangya-Derm: Development of a Chinese Database for Artificial Intelligence.
Article in Journal of medical Internet research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Artificial intelligence in dermatology: A literature review of current evidence and clinical implementation.JID innovations : skin science from molecules to population health · 2026Review
- Artificial intelligence-enabled precision medicine for inflammatory skin diseases.The Journal of investigative dermatology · 2026Review
- Comparative performance of deep learning models and non-dermatologists in diagnosing psoriasis, dermatophytosis, and eczema.Scientific reports · 2025Article
- Machine Learning-based Complementary Artificial Intelligence Model for Dermoscopic Diagnosis of Pigmented Skin Lesions in Resource-limited Settings.Plastic and reconstructive surgery. Global open · 2025Article
- A Multi-model Deep Learning Architecture for Diagnosing Multi-class Skin Diseases.Journal of imaging informatics in medicine · 2025Article
- Navigating the AI tide: challenges, opportunities, and future directions for early-career dermatologists.Frontiers in medicine · 2025Review
- Artificial Intelligence-Aided Diagnosis System for the Detection and Classification of Private-Part Skin Diseases: Decision Analytical Modeling Study.Journal of medical Internet research · 2024Article
- Review
- A systematic review and meta-analysis of artificial intelligence versus clinicians for skin cancer diagnosis.NPJ digital medicine · 2024Review
- Review
- Recent Advances in Melanoma Diagnosis and Prognosis Using Machine Learning Methods.Current oncology reports · 2023Review
- Artificial Intelligence-Based Psoriasis Severity Assessment: Real-world Study and Application.Journal of medical Internet research · 2023Article
- Deep skin diseases diagnostic system with Dual-channel Image and Extracted Text.Frontiers in artificial intelligence · 2023Article
- Improving Skin Color Diversity in Cancer Detection: Deep Learning Approach.JMIR dermatology · 2022Article
- Diagnostic accuracy of artificial intelligence in the diagnosis of pemphigus and pemphigoid groups of disorders based on clinical images: A systematic review and meta-analysis.Journal of oral biology and craniofacial researchReview
- Exploring AI as a Diagnostic Tool in Medical Imaging for Dermatopathological Diseases.Indian journal of dermatologyArticle
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19 authors.
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Abstract
backgroundSkin and subcutaneous disease is the fourth-leading cause of the nonfatal disease burden worldwide and constitutes one of the most common burdens in primary care. However, there is a severe lack of dermatologists, particularly in rural Chinese areas. Furthermore, although artificial intelligence (AI) tools can assist in diagnosing skin disorders from images, the database for the Chinese population is limited.
objectiveThis study aims to establish a database for AI based on the Chinese population and presents an initial study on six common skin diseases.
methodsEach image was captured with either a digital camera or a smartphone, verified by at least three experienced dermatologists and corresponding pathology information, and finally added to the Xiangya-Derm database. Based on this database, we conducted AI-assisted classification research on six common skin diseases and then proposed a network called Xy-SkinNet. Xy-SkinNet applies a two-step strategy to identify skin diseases. First, given an input image, we segmented the regions of the skin lesion. Second, we introduced an information fusion block to combine the output of all segmented regions. We compared the performance with 31 dermatologists of varied experiences.
resultsXiangya-Derm, as a new database that consists of over 150,000 clinical images of 571 different skin diseases in the Chinese population, is the largest and most diverse dermatological data set of the Chinese population. The AI-based six-category classification achieved a top 3 accuracy of 84.77%, which exceeded the average accuracy of dermatologists (78.15%).
conclusionsXiangya-Derm, the largest database for the Chinese population, was created. The classification of six common skin conditions was conducted based on Xiangya-Derm to lay a foundation for product research.
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