{'fraction_non_alphanumeric': 0.02338586680223691, 'fraction_numerical': 0.008896797153024912, 'mean_word_length': 5.591289782244556, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Artificial intelligence for grading in acne vulgaris: current situation and prospect

To the Editor, Acne vulgaris is a common inflammatory cutaneous disease affecting appearance and mental health, most commonly occurring in adolescents and adults.1 What's worse, scars and hyperpigmentation are always left behind after acne due to untimely or substandard treatment, which needs a lot of time and money to treat. Therefore, severity evaluation of acne and appropriate therapy are important. Multiple acnegrading systems have been developed to standardize severity and indicate therapy for patients, approximately divided into qualitative and quantitative acnegrading systems. However, estimating types and quantity of lesions is subjective and timeconsuming. Fortunately, artificial intelligence (AI) has gradually been used in the severity grading of acne vulgaris. Shen and colleagues2 constructed a model to extract effective features in facial images and achieved acne seven classifications, including blackhead, whitehead, papule, pustule, cyst, nodule, and normal skin. The model can distinguish skin areas from nonskin areas and analyze the proportions of every type of lesions. Even though their model was weaker than the pretrained VGG16 model, the accuracy of any class exceeded 81%. The model had the possibility of applying in diagnosis and qualitative grading systems but was unsuitable for quantitative grading systems. Except for this model, others were trained in terms of one specific acnegrading system. Bang and colleagues trained ResNet, GoogLeNet, and VGGNet based on the Korea Acne Severity Rating System (KAGS) to compare their accuracy by testing. As a consequence, GoogLeNet's Inceptionv3 performed best with 86.7% accuracy. Lim and colleagues3 trained ResNet, Inceptionv4, and DenseNet18 on different sizes of images based on the Investigator's Global Assessment (IGA). The best classification accuracy was 67% from the Inceptionv4 and DenseNet18 on image size 1200 × 1600 pixels. Yang and colleagues4 trained Inceptionv3 according to the current Chinese Guidelines for the Management of Acne Vulgaris. The average F1 value was 0.8, and the model has a high consistency with attending dermatologists. Seité and colleagues5 developed an artificial intelligence algorithm (AIA) for smartphones based on Investigator Global Assessment (IGA) with an accuracy of 68%. The incidence of acne is very high, but the shortage of dermatologists causes lots of patients to have no standardized, systematic, and individualized treatment. Additionally, acne as a chronic inflammatory disease needs longtime management, which increases pressure on healthcare organizations. AI makes acne grading more objective, precise, and efficient, not only reducing time for doctors on assessing severity but also let patients have access to know more about their conditions. Till now, researchers have applied AI to smartphones, which makes it possible to achieve online patient management. The aforementioned models perform well with relatively high accuracy and consistency with dermatologists, whereas they also have limitations. These models are developed in light of one specific grading system and ethnic populations, so they are not universal. Besides, even though models indicate the severity grade, they have not been connected to a specific treatment or not compared with practical therapy of dermatologists. In conclusion, existing models have practicability for acne severity grading and the potential for applying to online assessment and instruction. Nevertheless, they cannot be utilized in all acnegrading systems, and the guiding value of treatment is undefined. In the future, a more accurate and universal model may be developed to alleviate the shortage of dermatologists and assist doctors with evaluating the severity, making clinical treatment strategies, and clinical followup for patients with acne vulgaris.