Medical image recognition is a cross-disciplinary field involving clinical medicine, mathematical image processing, pattern recognition, machine learning, among other areas of knowledge. The main research aspects include medical image classification, lesion location and segmentation and three-dimensional reconstruction and visualisation. Traditional medical image recognition techniques include pixel-level image processing and mathematical modelling, both of which are based on specific recognition rules. Some studies have evaluated the severity of acne. Chang and Liao [11] used the support vector machine (SVM) classifier and feature extraction to divide acne vulgaris into comedones, papules, pustules, etc. Malik et al. [12] used a similar method to divide acne vulgaris into comedones, papules, etc., and then classified acne into mild, mild to moderate, moderate to severe and severe according to the established scoring rules. Patwardhan et al. [13] extracted features from VISIA-CR images of acne to calculate the number of inflammatory lesions, noninflammatory skin lesions, erythema, and post-acne pigmentation. These studies were based on specific acquisition equipment or traditional image recognition methods. It is necessary to design image features according to the color or texture of acne and then select the classifier for training and classification. These kinds of artificially designed image features are easily affected by the illumination environment and imaging quality and cannot effectively describe the appearance characteristics of acne, which limits the clinical application.

In recent years, thanks to the development of computers and the expansion of datasets, deep learning models have been widely used in the field of image classification and detection. With the development of machine learning, scholars are beginning to use convolutional neural networks to train medical images, such as magnetic resonance imaging (MRI) images, computed tomography (CT) images, microscopic images and clinical photographs, to achieve higher recognition rates. For acne research, Shen et al. [14] used the 16-layer Visual Geometry Group (VGG16) model to classify acne patients into seven categories: those with normal skin, whiteheads, blackheads, pimples, pustules, cysts and nodules. The same patient can be output to more than one class of results. Zhao and Spoelstra [15] used the 152-layer residual neural network (ResNet-152) to classify acne into five levels: 1-clear, 2-almost clear, 3-mild, 4-moderate, and 5-severe, which was compared with 11 dermatologists on the same acne patients. Seité [16] developed an artificial intelligence algorithm for smartphones to determine the severity of facial acne using the Global Acne Severity Scale for Europe.

In view of previous studies and the practical application of the evaluation of the acne severity, the model presented in this study was designed with following characteristics:

1.

A grading standard with operability. Not only the quantity and type of skin lesions but also the standard treatment plan were used as the grading standard. Each severity level corresponded to an established standard treatment regimen, which could assist the physician to formulate a clinical treatment plan and provide patients with standardised and targeted precision treatment. In this study, the assessment of acne severity and the corresponding treatment recommendations were in accordance with current Chinese guidelines [10], which were formulated by 30 expert dermatologists based on user feedback of the previous guidelines, research progress on acne in China and abroad, as well as experts’ experience. These guidelines have a practical guiding and normative role in diagnosis and treatment. In addition, this model can re-evaluate the severity of patients after treatment to determine whether the current treatment regimen should be changed. For instance, when a patient’s grading is downgraded from Grade III to Grade I after treatment, his/her treatment can thus be changed from the original oral antibiotics to topical retinoids.

2.

Full-section photos of multi-angle stitching. The overall degree of disease cannot be fully described by a single facial photograph. In this study, we used a multi-angled face to remove the area near the eyes, nostrils, and lips that did not have skin lesions. Splicing the dataset not only reflects the skin lesions in the entire face of the patient but also minimises the privacy exposure of patient data.

3.

Voting method to process data. The average classification was not processed by orderly classification data, such as severity classification, but by a more accurate voting method.

4.

Training using the Inception-v3 model. Inception-v3 is a widely used image recognition model that achieves better performance in image recognition. Compared with VGGNet and ResNet, it is more suitable to our task because of its unique Inception architecture, in which multiscale convolutions are performed in parallel and the convolution results for each branch are further concatenated. Additionally, VGGNet requires more computational cost and ResNet performs poorly in identifying subtle object differences.

This study performed a statistical analysis of the evaluation results of three attending dermatologists and three dermatology residents, confirming that the dermatologists at the same clinical level of treatment had more consistency. The evaluations by our model have a strong consistency with the evaluations of the attending dermatologists, indicating that the model can achieve the same level of assessment of acne severity as an attending dermatologist with more experience in diagnosis and treatment.

The model is highly practical since it is based on photographs taken by SLR cameras, which are commonly used in everyday practice. It can be applied to the teaching of dermatology and can assist primary dermatologists, general practitioners and physicians in developing treatment plans and help acne patients understand the severity of their disease. Of course, the treatment and management regimen of the disease is not fixed. It should be noted that:

1.

The purpose of this research model is to provide an objective assessment of the severity of acne vulgaris in patients and to propose basic treatment recommendations. However, there is a far way to go before such an AI system could actually make clinical decisions in the place/absence of human input.

2.

In order to fully embody the principle of individualised treatment, dermatologists should make choices according to the actual conditions of patients based on, for example, medical history, contraindications of drugs, etc.

There are a number of shortcomings to this study. First, since only East Asian people with Fitzpatrick skin type III and IV were included in the dataset, we adopted the Chinese Guidelines for the Management of Acne Vulgaris as the sole grading standard. In future studies, we are going to use guidelines [17,18,19,20], datasets and evaluation methods [21] from different ethnic groups to train the model, with the aim to expand the application range of the model. Secondly, the team will conduct a retrospective or prospective clinical study of the model's graded treatment plan to optimise present the treatment regimen of the study model. Furthermore, patients’ metadata, such as patients’ medical history and other clinical information in addition to image data will be added to the model. Later, a standardised acne vulgaris severity assessment model applied to smart-phones and other modes will be developed to promote the implementation of precise, accessible, individualised medical treatment. Thirdly, this study focused on the overall evaluation of the disease without classifying and quantifying specific skin lesions, which is an aim of our future research. Moreover, due to the advantages of deep learning, we will continue to obtain new data based on clinical practical application, so as to constantly update the generalisation performance and optimise the model, making it a more valuable tool in clinical practice.