Construction and Evaluation of a Deep Learning Model for Assessing Acne Vulgaris Using Clinical Images

INTRODUCTION: Accurate assessment is the basis for the effective treatment of acne vulgaris. The goal of this study was to achieve standardised diagnosis and treatment based on a deep learning model that was developed according to the current Chinese Guidelines for the Management of Acne Vulgaris. METHODS: The first step was to divide each image of acne vulgaris into four regions. Each of these four regions of the same patient was then combined to form a complete facial region. The second step was to classify the images based lesion type, in accordance with the current Chinese guidelines, and by treatment strategy adopted by experienced dermatologists. The final step was to evaluate the performance of the deep learning model in patients with acne vulgaris. RESULTS: The results showed that the average F1 value of the assessment model is 0.8 (optimum value = 1). The weighted kappa coefficient between the evaluation according to the artificial intelligence model and the evaluation by the attending dermatologists was 0.791 (95% confidence interval 0.671–0.910, P < 0.001), indicating a high degree of consistency. CONCLUSIONS: The assessment model based on deep learning and according to the Chinese guidelines had a slightly higher overall performance is comparable to that of the attending dermatologist.