Chinese Journal of Dermatology ›› 2021, Vol. 54 ›› Issue (7): 586-589.doi: 10.35541/cjd.20200865

• Original Articles • Previous Articles     Next Articles

Establishment and evaluation of an artificial intelligence model for the diagnosis of facial vitiligo

Guo Lifang, Ge Yiping, Yang Yin, Lin Tong   

  1. Department of Laser Surgery, Hospital of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing 210042, China
  • Received:2020-09-01 Revised:2021-04-30 Online:2021-07-15 Published:2021-07-02
  • Contact: Lin Tong

Abstract: 【Abstract】 Objective To construct an artificial intelligence model for the diagnosis of facial vitiligo, so as to realize artificial intelligence-assisted diagnosis of facial vitiligo. Methods Based on digital single-lens reflex (SLR) camera images of vitiligo skin lesions and YOLO (You Only Look Once) v3 algorithm, a skin lesion detection model Vit3 was established, and its performance was evaluated by comparing its detection results and labeling results of dermatologists. On the basis of the Vit3 model, both optical and ultraviolet images of vitiligo and non-vitiligo skin lesions were taken by using an artificial intelligence-based facial skin image collector, and the gray values of vitiligo and non-vitiligo skin lesion areas on the ultraviolet images were measured by using an image processing technique. According to the gray-value threshold between vitiligo and non-vitiligo skin lesions, a facial vitiligo diagnosis model Vit4 was established. Cochran′s Q test was used to compare the diagnostic results of the Vit4 model and dermatologists, and the diagnostic performance of the Vit4 model was evaluated. Results For 100 SLR camera images of vitiligo skin lesions (167 lesional sites) and 100 SLR camera images of normal skin, the diagnostic sensitivity of the Vit3 model was 92.81% (155/167). For 97 pairs of facial skin images (including 50 vitiligo lesions, 30 pityriasis alba lesions, 7 amelanotic nevus leisons, and 10 normal skin tissues), the diagnostic accuracy rate, sensitivity and specificity of the Vit4 model were 88.66% (86/97), 88.00% (44/50) and 89.36% (42/47) respectively, and there was no significant difference in the diagnostic accuracy rate between the Vit4 model and dermatologists (92.78% [90/97], χ2 = 2.323, P > 0.05). Conclusion The artificial intelligence model Vit4 was established for the diagnosis of facial vitiligo with favorable diagnostic performance, and could serve as an objective and convenient method for the auxiliary diagnosis of facial vitiligo.

Key words: Vitiligo, Artificial intelligence, Diagnosis, Image processing, computer-assisted