Background: Rosacea is a chronic inflammatory disease with variable clinical presentations, including transient flushing, fixed erythema, papules, pustules, and phymatous changes on the central face. Owing to the diversity in the clinical manifestations of rosacea, the lack of objective biochemical examinations, and nonspecificity in histopathological findings, accurate identification of rosacea is a big challenge. Artificial intelligence has emerged as a potential tool in the identification and evaluation of some skin diseases such as melanoma, basal cell carcinoma, and psoriasis. Objective: The objective of our study was to utilize a convolutional neural network (CNN) to differentiate the clinical photos of patients with rosacea (taken from 3 different angles) from those of patients with other skin diseases such as acne, seborrheic dermatitis, and eczema that could be easily confused with rosacea. Methods: In this study, 24,736 photos comprising of 18,647 photos of patients with rosacea and 6089 photos of patients with other skin diseases such as acne, facial seborrheic dermatitis, and eczema were included and analyzed by our CNN model based on ResNet-50. Results: The CNN in our study achieved an overall accuracy and precision of 0.914 and 0.898, with an area under the receiver operating characteristic curve of 0.972 for the detection of rosacea. The accuracy of classifying 3 subtypes of rosacea, that is, erythematotelangiectatic rosacea, papulopustular rosacea, and phymatous rosacea was 83.9%, 74.3%, and 80.0%, respectively. Moreover, the accuracy and precision of our CNN to distinguish rosacea from acne reached 0.931 and 0.893, respectively. For the differentiation between rosacea, seborrheic dermatitis, and eczema, the overall accuracy of our CNN was 0.757 and the precision was 0.667. Finally, by comparing the CNN diagnosis with the diagnoses by dermatologists of different expertise levels, we found that our CNN system is capable of identifying rosacea with a performance superior to that of resident doctors or attending physicians and comparable to that of experienced dermatologists. Conclusions: The findings of our study showed that by assessing clinical images, the CNN system in our study could identify rosacea with accuracy and precision comparable to that of an experienced dermatologist.

Rosacea is a common skin disease that predominantly affects individuals aged between 30 and 50 years. While the exact cause of the disease remains unclear, various factors have been shown to trigger or exacerbate its symptoms. N6-methyladenosine (m⁶A) modification is one of the most abundant epigenetic methylation modification in messenger RNA (mRNA) and non-coding RNA (ncRNA), plays a crucial role in RNA splicing, export, stability, and translation. In this study, we aimed to characterize m⁶A genes in rosacea, identify molecular subtypes based on m⁶A gene expression, characterize the immune features among subtypes, explore key molecules based on co-expression analysis, and identify potential targets and drugs. To achieve our objectives, we first compared the expression pattern and immune regulation of m⁶A genes between healthy and diseased groups. Then, we performed clustering to stratify disease samples into different subtypes and analyzed immune regulation and functional enrichment among the subtypes. Then, we conducted differential analysis between subtypes and applied weighted gene co-expression network analysis (WGCNA) in three subtypes. We found hub differential expression analysis (DEG) genes and their potential drug based on the WGCNA results and the drug-gene interaction database (DGIdb). Finally, in vivo and in vitro studies showed significant differences in m⁶A methyltransferase METTL3 levels in rosacea mice and control mice, as well as in the skin of rosacea patients and healthy people, while reducing METTL3 significantly inhibited the inflammatory response of human fibroblasts (HDFs) stimulated by LL37, suggesting that METTL3 may be associated with changes in overall m⁶A levels in rosacea. Taken together, our findings provide valuable insights into therapeutic targets and drug predictions for rosacea.

BACKGROUND Rosacea is a chronic inflammatory disease with variable clinical presentations, including transient flushing, fixed erythema, papules, pustules, and phymatous changes on the central face. Owing to the diversity in the clinical manifestations of rosacea, the lack of objective biochemical examinations, and nonspecificity in histopathological findings, accurate identification of rosacea is a big challenge. Artificial intelligence has emerged as a potential tool in the identification and evaluation of some skin diseases such as melanoma, basal cell carcinoma, and psoriasis. OBJECTIVE The objective of our study was to utilize a convolutional neural network (CNN) to differentiate the clinical photos of patients with rosacea (taken from 3 different angles) from those of patients with other skin diseases such as acne, seborrheic dermatitis, and eczema that could be easily confused with rosacea. METHODS In this study, 24,736 photos comprising of 18,647 photos of patients with rosacea and 6089 photos of patients with other skin diseases such as acne, facial seborrheic dermatitis, and eczema were included and analyzed by our CNN model based on ResNet-50. RESULTS The CNN in our study achieved an overall accuracy and precision of 0.914 and 0.898, with an area under the receiver operating characteristic curve of 0.972 for the detection of rosacea. The accuracy of classifying 3 subtypes of rosacea, that is, erythematotelangiectatic rosacea, papulopustular rosacea, and phymatous rosacea was 83.9%, 74.3%, and 80.0%, respectively. Moreover, the accuracy and precision of our CNN to distinguish rosacea from acne reached 0.931 and 0.893, respectively. For the differentiation between rosacea, seborrheic dermatitis, and eczema, the overall accuracy of our CNN was 0.757 and the precision was 0.667. Finally, by comparing the CNN diagnosis with the diagnoses by dermatologists of different expertise levels, we found that our CNN system is capable of identifying rosacea with a performance superior to that of resident doctors or attending physicians and comparable to that of experienced dermatologists. CONCLUSIONS The findings of our study showed that by assessing clinical images, the CNN system in our study could identify rosacea with accuracy and precision comparable to that of an experienced dermatologist.

[This corrects the article DOI: 10.2196/23415.].

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