The objective of our study was to evaluate the sensitivity and specificity of rapid antigen detection tests versus those of reverse transcriptase PCR (RT-PCR) using oral, anterior nasal, and nasopharyngeal swabs. The underlying prospective, diagnostic case-control-type accuracy study included 87 hospitalized and nonhospi- talized participants in a positive and a negative sample cohort between 16 March and 14 May 2021 in two hospitals in Vienna. SARS-CoV-2 infection status was con- firmed by RT-PCR. Participants self-performed one oral and one anterior nasal swab for the rapid antigen test, immediately followed by two nasopharyngeal swabs for the rapid antigen test and RT-PCR by the investigator. Test results were read after 15 min, and participants completed a questionnaire in the meantime. Test parameters were calculated based on the evaluation of 87 participants. The overall sensitivity of rapid antigen detection tests versus that of RT-PCR with oral, anterior nasal, and nasopharyn- geal samples was 18.18% (95% confidence interval [CI] 8.19% to 32.71%), 63.04% (95% CI 47.55% to 76.79%), and 73.33% (95% CI 58.06% to 85.4%), respectively. All sampling methods had a test specificity of 100% regardless of the cycle threshold (CT) value. Rapid antigen detection tests using self-collected anterior nasal swabs proved to be as sensitive as and more tolerable than professionally collected nasopharyngeal swabs for CT values up to 30 determined by RT-PCR. This finding illustrates the reliability of tests obtained by adequate self-collected anterior nasal specimen. Sensitivity was depend- ent upon the CT value for each sampling method. While the main advantage of rapid antigen detection tests is the immediate availability of results, PCR should be preferred in crucial settings wherever possible.

Although significant advancements in computer-aided diagnostics using artificial intelligence (AI) have been made, to date, no viable method for radiation-induced skin reaction (RISR) analysis and classification is available. The objective of this single-center study was to develop machine learning and deep learning approaches using deep convolutional neural networks (CNNs) for automatic classification of RISRs according to the Common Terminology Criteria for Adverse Events (CTCAE) grading system. Scarletred(R)Vision, a novel and state-of-the-art digital skin imaging method capable of remote monitoring and objective assessment of acute RISRs was used to convert 2D digital skin images using the CIELAB color space and conduct SEV* measurements. A set of different machine learning and deep convolutional neural network-based algorithms has been explored for the automatic classification of RISRs. A total of 2263 distinct images from 209 patients were analyzed for training and testing the machine learning and CNN algorithms. For a 2-class problem of healthy skin (grade 0) versus erythema (grade ≥ 1), all machine learning models produced an accuracy of above 70%, and the sensitivity and specificity of erythema recognition were 67–72% and 72–83%, respectively. The CNN produced a test accuracy of 74%, sensitivity of 66%, and specificity of 83% for predicting healthy and erythema cases. For the severity grade prediction of a 3-class problem (grade 0 versus 1 versus 2), the overall test accuracy was 60–67%, and the sensitivities were 56–82%, 35–59%, and 65–72%, respectively. For estimating the severity grade of each class, the CNN obtained an accuracy of 73%, 66%, and 82%, respectively. Ensemble learning combines several individual predictions to obtain a better generalization performance. Furthermore, we exploited ensemble learning by deploying a CNN model as a meta-learner. The ensemble CNN based on bagging and majority voting shows an accuracy, sensitivity and specificity of 87%, 90%, and 82% for a 2-class problem, respectively. For a 3-class problem, the ensemble CNN shows an overall accuracy of 66%, while for each grade (0, 1, and 2) accuracies were 76%, 69%, and 87%, sensitivities were 70%, 57%, and 71%, and specificities were 78%, 75%, and 95%, respectively. This study is the first to focus on erythema in radiation-dermatitis and produces benchmark results using machine learning models. The outcome of this study validates that the proposed system can act as a pre-screening and decision support tool for oncologists or patients to provide fast, reliable, and efficient assessment of erythema grading.

Injection site reactions (ISRs) are a constellation of symptoms, such as erythema, swelling, pain, and induration, occurring at the site of the injection. Documenting and monitoring ISRs are integral components of the clinical trial process for any injectable drug or treatment. During clinical development, local reactions at the injection site must be tracked to complete the safety assessment of the investigational product. ISRs can occur right after the substance has been administered and subsequently evolve over time after the individual has left the health care provider, highlighting the need for a solution that can accommodate real-time, remote, home-based monitoring. Medical professionals agree that traditional methods of ISR monitoring in the product development phases are lacking, leading to a loss of time, precision, and data. The global digitalization trend and the impact of the current SARS-CoV-2 pandemic are the main driving forces behind the development of new injectables, revealing the urgent requirement for novel state-of-the-art tools that can be more efficiently implemented. In the present global market and research study, we (SCARLETRED) give an overview of the applicable industries and our technological solution, Scarletred®Vision, which outperforms conventional methods and is on its way to becoming the modern standard for remote ISR Monitoring and objective quantification in a broad range of application fields.

Hidradenitis suppurativa (HS) is a chronic inflammatory skin disease affecting up to 2% of European adults. Disease activity is commonly assessed by counting of inflammatory nodules, abscesses and fistulas.

Radiation-induced dermatitis (RID) is routinely graded by visual inspection. Inter-observer variability makes this approach inadequate for an objective assessment of the efficacy of different topical treatments. In this study we report on the first clinical application of a new image-analysis tool developed to measure the relevant effects quantitatively and to compare the effects of two different topical preparations used to treat RID.

The present case report shows that an innovative technology -Scarletred® Vision - can predict the response of the topical application of Enstilar® foam already by day 3.

The purpose of our investigation was to develop a novel and state of the art digital skin imaging method capable for remote monitoring and objective assessment of Radiation Induced Dermatitis (RID). Therefore, radiation therapy related side effects were assessed by medical experts according to Common Terminology Criteria for Adverse Events (CTCAE) grade of severity in 20 female breast cancer patients in a clinical trial over the treatment time frame of 25-28 radiation cycles, 50.0 – 50.4 Gy each. Furthermore the intensity of developed skin erythema was documented by using conventional spectrophotometry plus digital skin imaging. Thereby we could derive the Standardized Erythema Value (SEV), a novel objective parameter, which in contrast to single parametric L* and a* delivers a long dynamic measurement range for analyzing RID from bright to very dark skin tones. Methodical superiority of the SEV could be proven over spectrophotometer measurements in terms of a higher sensitivity and by enabling signal intensity mapping in analyzed skin images. Our thereupon-derived patent enables novel objective dermatologic eHealth applications in a broad range of medical and industrial use by opening likewise the window for augmented dermatology. The first of its kind system is now already further developed in form of the medical device product Scarletred®Vision. It is available on the market for primary usage in clinical trials and in medical routine.