Diseases that develop necrotizing vasculitis of cutaneous muscular arteries include cutaneous arteritis (CA) and polyarteritis nodosa (PAN). It is difficult to distinguish them based on skin biopsy findings alone. This study demonstrated that artificial intelligence (AI) can discriminate them based on skin biopsy findings and revealed where AI focuses on the image. Ninety-three hematoxylin-and-eosin images of CA and 19 PAN images were used. Among them, 85 CA and 17 PAN images were used to train AI
thereafter, AI was challenged to classify the remaining images. The same test images were evaluated by 26 pathologists with different years of experience. AI accuracy was 75.2 %, whereas that of pathologists was 42.8 %. Gradient-weighted class activation mapping (Grad-CAM) indicated that AI focused on connective tissues around the affected vessels rather than the affected vessels. Twenty-two of the 26 pathologists were randomly divided into two groups of 11 each, one of which referred to Grad-CAM images and was challenged in the second-round test of images different from the first round. The accuracy significantly improved after referring to Grad-CAM images, whereas it was equivalent to the first round without referring to Grad-CAM images. In the survey after the second-round test, pathologists who referred to Grad-CAM images suggested that inflammation and fibrosis in the surrounding connective tissues in PAN might be abundant compared to CA. AI may be useful for histological differentiation between CA and PAN and can help pathologists improve the ability of discriminating CA and PAN based on histological findings of skin biopsy specimens.