Purpose Radiofrequency equipment, including invasive fractional microneedle radiofrequency (FMR) and noninvasive fractional radiofrequency (FR), has facilitated progress in the field of active acne treatment, but clinical data are limited. This study aimed to compare the efficacy and safety of FMR and FR in the treatment of moderate‐to‐severe acne vulgaris. Methods Ten patients with moderate‐to‐severe acne were enrolled in a 24‐week, prospective, single‐blind, randomized, comparative clinical trial with a split‐face design. Fractional microneedle radiofrequency treatment was provided on one side of the face and FR on the opposite side. Three consecutive treatments were performed at 4‐week interval. Objective assessment of efficacy was performed using the Acne Severity Index (ASI), and acne inflammatory lesions and noninflammatory lesions were evaluated. Results After the first treatment, ASI and both inflammatory and noninflammatory lesions of the FMR‐treated side were improved to a greater degree than the FR‐treated side. Patients' subjective satisfaction was also better regarding the FMR‐treated side. After subsequent treatments, efficacy improves for both groups, and no significant differences in the above efficacy parameters between the two groups were observed. Pain scores were significantly higher for the FR‐treated side than the FMR‐treated side. Conclusion Fractional microneedle radiofrequency is faster and more effective than FR for the treatment of moderate and severe acne after only one treatment. However, after several treatment sessions, both FMR and FR proved to be safe and effective for long‐term treatment of moderate‐to‐severe acne. Therefore, invasive radiofrequency, such as FMR, or noninvasive radiofrequency, such as FR, can successfully treat active acne.

Objective This study used deep learning for diagnosing common, benign hyperpigmentation. Method In this study, two convolutional neural networks were used to identify six pigmentary diseases, and a disease diagnosis model was established. Because the distribution of lesions in the original training picture is very complex, we cropped the image around the lesions, trained the network on the extracted lesion images, and fused the verification results of the overall picture and the extracted picture to assess the model performance in identifying hyperpigmented dermatitis pictures. Finally, we evaluated the image recognition performance of the two convolutional neural networks and the converged networks in the test set through a comparison of the converged network and the physicians’ assessments. Results The AUC of DenseNet‐96 for the overall picture was 0.98, whereas the AUC of ResNet‐152 was 0.96; therefore, we concluded that DenseNet‐96 performed better than ResNet‐152. From the AUC, the converged network has the best performance. The converged network model achieved a comprehensive classification performance comparable to that of the doctors. Conclusions The diagnostic model for benign, pigmented skin lesions based on convolutional neural networks had a slightly higher overall performance than the skin specialists.

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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.

Background: We aimed to establish and validate a deep learning-based hybrid artificial intelligence (AI) model for the objective morphometric and colorimetric assessment of vitiligo lesions. Methods: Two main datasets containing curated images of vitiligo lesions from Chinese patients (Fitzpatrick skin types III or IV) were established, including one with 2,720 images for lesion localization study and the other with 1,262 images for lesion segmentation study. Besides, an additional test set containing 145 images of vitiligo lesions from other Fitzpatrick skin types (I, II, or V) was also generated. A 3-stage hybrid model was constructed. YOLO v3 (You Only Look Once, v3) architecture was trained and validated to classify and localize vitiligo lesions, with sensitivity and error rate as primary performance outcomes. Then a segmentation study comparing 3 deep convolutional neural networks (DCNNs), Pyramid Scene Parsing Network (PSPNet), UNet, and UNet++, was carried out based on the Jaccard index (JI). The architecture with the best performance was integrated into the model. Three add-on metrics, namely VAreaA, VAreaR, and VColor were finally developed to measure absolute, relative size changes and pigmentation, respectively.Agreement between the AI model and dermatologist evaluators were assessed. Results:The sensitivity of the YOLO v3 architecture to detect vitiligo lesions was 92.91% with an error rate of 14.98%. The UNet++ architecture outperformed the others in the segmentation study (JI, 0.79) and was integrated into the model. On the additional test set, however, the model achieved a lower detection sensitivity (72.41%) and a lower segmentation score (JI, 0.69). With respect to size changes, no difference was observed between the AI model, trained dermatologists (W=0.812, P<0.05), and Photoshop analysis (P=0.075, P=0.212 respectively), which all displayed good concordance. Conclusions:We developed a novel, convenient, objective, and quantitative deep learning-based hybrid model which simultaneously evaluated both morphometric and colorimetric vitiligo lesions from patients with Fitzpatrick skin types III or IV, rendering it suitable for the assessment of severity of vitiligo lesions in Asians in both clinic and research scenarios. More work is also warranted for its use in other ethnic skin groups.