Prevalence of cutaneous manifestations in COVID-19: A meta-analysis.

The global epidemic of coronavirus disease 2019 (COVID-19) endangers more and more people. Many studies on cutaneous manifestations related to COVID-19 have emerged, but their prevalence has varied widely. The objective of this study was to conduct a meta-analysis estimating the prevalence of skin manifestations in COVID-19. Four databases PubMed, Web of Science, CBM, and CNKI were searched, and the results were screened by two reviewers. A random-effects model was used to evaluate the overall prevalence. Heterogeneity was assessed by I2 . Further subgroup analyses were conducted by region, sample size, sex, age, and severity of COVID-19. A funnel plot and Egger's test were performed to assess publication bias. The pooled prevalence of cutaneous manifestation of 61 089 patients in 33 studies was 5.6% (95% confidence intervals [CI] = 0.040-0.076, I2  = 98.3%). Severity of COVID-19 was probably the source of heterogeneity. Studies with sample size <200 report higher prevalence estimates (10.2%). The prevalence of detailed types was as follows: maculopapular rash 2%, livedoid lesions 1.4%, petechial lesions 1.1%, urticaria 0.8%, pernio-like lesions 0.5%, vesicular lesions 0.3%. Petechial lesions and livedoid lesions contain a higher proportion of severe patients than other skin manifestations. The prevalence rates of pernio-like lesions, urticaria and petechial lesions vary greatly in different regions.

The application research of AI image recognition and processing technology in the early diagnosis of the COVID-19.

BACKGROUND: This study intends to establish a combined prediction model that integrates the clinical symptoms,the lung lesion volume, and the radiomics features of patients with COVID-19, resulting in a new model to predict the severity of COVID-19. METHODS: The clinical data of 386 patients with COVID-19 at several hospitals, as well as images of certain patients during their hospitalization, were collected retrospectively to create a database of patients with COVID-19 pneumonia. The contour of lungs and lesion locations may be retrieved from CT scans using a CT-image-based quantitative discrimination and trend analysis method for COVID-19 and the Mask R-CNN deep neural network model to create 3D data of lung lesions. The quantitative COVID-19 factors were then determined, on which the diagnosis of the development of the patients' symptoms could be established. Then, using an artificial neural network, a prediction model of the severity of COVID-19 was constructed by combining characteristic imaging features on CT slices with clinical factors. ANN neural network was used for training, and tenfold cross-validation was used to verify the prediction model. The diagnostic performance of this model is verified by the receiver operating characteristic (ROC) curve. RESULTS: CT radiomics features extraction and analysis based on a deep neural network can detect COVID-19 patients with an 86% sensitivity and an 85% specificity. According to the ROC curve, the constructed severity prediction model indicates that the AUC of patients with severe COVID-19 is 0.761, with sensitivity and specificity of 79.1% and 73.1%, respectively. CONCLUSIONS: The combined prediction model for severe COVID-19 pneumonia, which is based on deep learning and integrates clinical aspects, pulmonary lesion volume, and radiomics features of patients, has a remarkable differential ability for predicting the course of disease in COVID-19 patients. This may assist in the early prevention of severe COVID-19 symptoms.