Background. Application of machine learning (ML) for identification of systemic lupus erythematosus (SLE) has been recently drawing increasing attention, while there is still lack of evidence-based support. Methods. Systematic review and meta-analysis are conducted to evaluate its diagnostic accuracy and application prospect. PubMed, Embase, Cochrane Library, and Web of Science libraries are searched, in combination with manual searching and literature retrospection, for studies regarding machine learning for identifying SLE and neuropsychiatric systemic lupus erythematosus (NPSLE). Quality Assessment of Diagnostic Accuracy Studies (QUADA-2) is applied to assess the quality of included studies. Diagnostic accuracy of the SLE model and NPSLE model is assessed using the bivariate fixed-effect model, and the data are pooled. Summary receiver operator characteristic curve (SROC) is plotted, and area under the curve (AUC) is calculated. Results. Eighteen (18) studies are included, in which ten (10) focused on SLE and eight (8) on NPSLE. The AUC of SLE identification is 0.95, the sensitivity is 0.90, the specificity is 0.89, the PLR is 8.4, the NLR is 0.12, and the DOR is 73. AUC of NPSLE identification is 0.89, the sensitivity is 0.83, the specificity is 0.83, the PLR is 5.0, the NLR is 0.20, and the DOR is 25. Conclusion. Machine learning presented remarkable performance in identification of SLE and NPSLE. Based on the convenience for inclusion factor collection and non-invasiveness of detection, machine learning is expected to be widely applied in clinical practice to assist medical decision making.

Rosacea is a chronic inflammatory skin disease without clear pathophysiology. Many factors may lead to its development, including genetic factors, immune dysregulation, neurovascular dysregulation, microorganisms, and environmental factors. 1 This disorder primarily affects the centrofacial region with various manifestations, from facial erythema, papules, pustules, phymatous changes to ocular symptoms.Due to the limitations of subtyping, rosacea classification has turned to a phenotype-led approach. The phenotype approach has been gradually accepted in rosacea diagnosis and treatment.Although there exist different treatment options for rosacea, none of them could cure all patients. When making treatment choices, clinicians should take both specific phenotypes and patient expectations into consideration. The remission and progression may appear during the natural history of rosacea, so the severity and efficacy should be supervised timely and precisely, and the treatment plan