Differential Diagnosis of Rosacea Using Machine Learning and Dermoscopy - Ge L; Li Y; Wu Y; Fan Z; Song Z (Clinical, Cosmetic and Investigational Dermatology, 2022)
Lan Ge,1 Yaoying Li,1 Yaguang Wu,1 Ziwei Fan,2 Zhiqiang Song1 1Department of Dermatology, The First Affiliated Hospital of Army Medical University, Chongqing, People’s Republic of China; 2Lianren Digital Health Technology Co., LTD, Shanghai, People’s Republic of ChinaCorrespondence: Zhiqiang Song, Department of Dermatology, The First Affiliated Hospital of Army Medical University, No. 30, Gaotanyanzheng Street, Shapingba District, Chongqing, People’s Republic of China, Email songzhiq_cq@163.comIntroduction: 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.Keywords: rosacea, dermatoscope, machine learning