ArticleClinical, cosmetic and investigational dermatology2022
Differential Diagnosis of Rosacea Using Machine Learning and Dermoscopy.
Article in Clinical, cosmetic and investigational dermatology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.
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Who cites it
8 citing papers in PubMed, 2 syntheses or guidelines pooled it, 18 citations in OpenAlex.
- Advancements and challenges of artificial intelligence in dermatology: a review of applications and perspectives in China.Frontiers in digital health · 2025Pooled it
- Analysis of the use of digital technologies in the preliminary diagnosis of dermatological diseases: a systematic review.Archives of dermatological research · 2024Pooled it
- Artificial intelligence-enabled precision medicine for inflammatory skin diseases.The Journal of investigative dermatology · 2026Review
- Predictive modeling of axillary web syndrome in Chinese postoperative breast cancer patients using interpretable machine learning.Frontiers in oncology · 2026Article
- Dermoscopy of rosacea: A cross-sectional study comparing dark and light phototypes.JAAD international · 2025Article
- Construction of a molecular diagnostic system for neurogenic rosacea by combining transcriptome sequencing and machine learning.BMC medical genomics · 2024Article
- Exploring the efficacy of laser speckle contrast imaging in the stratified diagnosis of rosacea: a quantitative analysis of facial blood flow dynamics across varied regions.Frontiers in immunology · 2024Article
- Dermoscopic Features of Cutaneous Vasculitis.Dermatology practical & conceptual · 2024Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Rosacea is a common chronic inflammatory disease occurring on the face, whose diagnosis is mainly based on symptoms and physical signs. Due to some overlap in symptoms and signs with other inflammatory skin diseases, young and inexperienced doctors often make misdiagnoses and missed diagnoses in clinical practices. We analyze the results of skin physiology and dermatoscopy using machine learning method and identify the characteristics of acne rosacea, which differentiate it from other common facial inflammatory skin diseases so as to improve the accuracy of clinical and differential diagnosis of rosacea. Methods: A total of 495 patients who were jointly diagnosed by two experienced doctors were included. Basic data, clinical symptoms, physiological skin detection, and dermatoscopy results were collected, and the clinical characteristics of rosacea and other common facial inflammatory diseases were summarized according to the descriptive analysis results. The model was established using a machine learning method and compared with the judgment results of young and inexperienced doctors to verify whether the model can improve the accuracy of clinical diagnosis and differential diagnosis of rosacea. Results: The proportion of yellow and red halos, vascular polygons, as well as follicular pustules, showed by dermatoscopy, and the melanin index in physiological skin detection revealed statistical significance in differentiating rosacea and other common facial inflammatory diseases (all P < 0.01). After adopting the machine learning, we found that GBM (Gradient Boosting Machine) algorithm was the best, and the error rate of this model in the validation set was 5.48%. In the final man-machine comparison, the accuracy of the GBM algorithm model for the classification of skin disease was significantly higher than that of young and inexperienced doctors. Conclusion: Dermatoscopy combined with machine learning can effectively improve the diagnosis and differential diagnosis accuracy of rosacea and other facial inflammatory skin diseases.
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