ArticleDermatology and therapy2024
Intelligent Diagnosis of Hypopigmented Dermatoses and Intelligent Evaluation of Vitiligo Severity on the Basis of Deep Learning.
Article in Dermatology and therapy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 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
- Clinically Interpretable Deep Learning for Differentiating Vitiligo and Postinflammatory Hypopigmentation: Diagnostic Accuracy Study.JMIR medical informatics · 2026Article
- Automated Measurement of Depigmentation Extent with a New AI Tool Applied to the Example of Vitiligo.Dermatology and therapy · 2026Article
- Potential of automated image analysis for the measurement of vitiligo lesions.Frontiers in medicine · 2025Article
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Authors and funding
11 authors.
Funding
Abstract
introductionThere is a lack of objective, accurate, and convenient methods for classification diagnostic hypopigmented dermatoses (HD) and severity evaluation of vitiligo. To achieve an accurate and intelligent classification diagnostic model of HD and severity evaluation model of vitiligo using a deep learning-based method.
methodsA total of 11,483 images from 4744 patients with HD were included in this study. An optimal diagnostic model was constructed by merging the squeeze-and-excitation (SE) module with the candidate model, its diagnostic efficiency was compared with that of 98 dermatologists. An objective severity evaluation indicator was proposed through weighting method and combined with a segmentation model to form a severity evaluation model, which was then compared with the assessments conducted by three experienced dermatologists using the naked eye.
resultsThe improved diagnosis model SE_ResNet-18 outperformed the other 11 classic models with an accuracy of 0.9389, macro-specificity of 0.9878, and macro-f1 score of 0.9395, and outperformed the different categories of 98 dermatologists (P < 0.001). The weighted Kappa test indicated medium consistency between the Indicator
conclusionsThis study proposes an objective, accurate, and convenient hybrid model for diagnosing HD and evaluating the severity of vitiligo, providing a method for dermatologists especially in grassroots hospitals, and provides a foundation for telemedicine.
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Registered trials
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