[Recurrent livedo-like skin lesions following a vector-based COVID-19 vaccine]. / Rezidivierende Livedo-artige Hautveränderungen nach einer Vektor-basierten COVID-19-Impfung.

Starting in 2020, the global health system faced unprecedent challenges due to the coronavirus disease 2019 (COVID-19) pandemic and the consequences are still felt. All the more fascinating and of particular importance for health policy was the development of potent vaccines within about one year by several research groups after the first reports of COVID-19 infections. To date, three types of COVID-19 vaccines are available, i.e., messenger RNA-based vaccines, adenoviral vector vaccines, and inactivated whole-virus vaccines. We report a woman who developed reddish, partially urticarial skin lesions on her right arm and flank shortly after the first dose with the corona vaccination from AstraZeneca/Oxford (ChAdOx1). The lesions were transient, however reoccurred in loco and at other locations over several days. The clinical presentation was unusual and was correctly assigned due to the clinical course.

New AI-algorithms on smartphones to detect skin cancer in a clinical setting-A validation study.

BACKGROUND AND OBJECTIVES: The incidence of skin cancer is rising worldwide and there is medical need to optimize its early detection. This study was conducted to determine the diagnostic and risk-assessment accuracy of two new diagnosis-based neural networks (analyze and detect), which comply with the CE-criteria, in evaluating the malignant potential of various skin lesions on a smartphone. Of note, the intention of our study was to evaluate the performance of these medical products in a clinical setting for the first time. METHODS: This was a prospective, single-center clinical study at one tertiary referral center in Graz, Austria. Patients, who were either scheduled for preventive skin examination or removal of at least one skin lesion were eligible for participation. Patients were assessed by at least two dermatologists and by the integrated algorithms on different mobile phones. The lesions to be recorded were randomly selected by the dermatologists. The diagnosis of the algorithm was stated as correct if it matched the diagnosis of the two dermatologists or the histology (if available). The histology was the reference standard, however, if both clinicians considered a lesion as being benign no histology was performed and the dermatologists were stated as reference standard. RESULTS: A total of 238 patients with 1171 lesions (86 female; 36.13%) with an average age of 66.19 (SD = 17.05) was included. Sensitivity and specificity of the detect algorithm were 96.4% (CI 93.94-98.85) and 94.85% (CI 92.46-97.23); for the analyze algorithm a sensitivity of 95.35% (CI 93.45-97.25) and a specificity of 90.32% (CI 88.1-92.54) were achieved. DISCUSSION: The studied neural networks succeeded analyzing the risk of skin lesions with a high diagnostic accuracy showing that they are sufficient tools in calculating the probability of a skin lesion being malignant. In conjunction with the wide spread use of smartphones this new AI approach opens the opportunity for a higher early detection rate of skin cancer with consecutive lower epidemiological burden of metastatic cancer and reducing health care costs. This neural network moreover facilitates the empowerment of patients, especially in regions with a low density of medical doctors. REGISTRATION: Approved and registered at the ethics committee of the Medical University of Graz, Austria (Approval number: 30-199 ex 17/18).