Dermato, Volume 5, Issue 2 (June 2025) – 2 articles

Background/Objectives: Skin lesions, ranging from benign to malignant diseases, are a difficult dermatological condition due to their great diversity and variable severity. Their detection at an early stage and proper classification, particularly between benign Nevus (NV), precancerous Actinic Keratosis (AK), and Squamous Cell Carcinoma (SCC), are crucial for improving the effectiveness of treatment and patient prognosis. The goal of this study was to test deep learning (DL) models to determine the best architecture to use in classifying lesions and create a web-based platform for improved diagnostic and educational availability. Methods: Various DL models, like Xception, DenseNet169, ResNet152V2, InceptionV3, MobileNetV2, EfficientNetV2 Small, and NASNetMobile, were compared for classification accuracy. The top model was incorporated into a web application, allowing users to upload images for automatic classification, thereby offering confidence scores as a measure of the reliability of predictions. The tool also has enhanced visualization capabilities, which allow users to investigate feature maps derived from convolutional layers, enhancing interpretability. Web scraping and summarization techniques were also employed to offer concise, evidence-based dermatological information from established sources. Results: Of the models evaluated, DenseNet169 achieved the best classification accuracy of 85% and was, therefore, chosen as the base architecture for the web application. The application enhances diagnostic clarity by visualizing features and promotes access to trustworthy medical information on dermatological disorders. Conclusions: The developed web application serves as both a diagnostic support system for dermatologists and an educational system for the general public. By using DL-based classification, interpretability techniques, and automatic medical information extraction, it facilitates early intervention and increases awareness regarding skin health. Full article

Background/objectives: Stimulator of Interferon Genes (STING)-associated vasculopathy with onset in infancy (SAVI) is a rare autoinflammatory disorder caused by gain-of-function mutations in the TMEM173 gene. These mutations result in chronic activation of the STING pathway and excessive type I interferon production, leading to systemic inflammation, vascular abnormalities, interstitial lung disease, and skin ulcerations. Janus kinase (JAK) inhibitors, including baricitinib, have shown promise in mitigating systemic and organ-specific manifestations. However, these inhibitors broadly suppress immune pathways, potentially increasing vulnerability to infections. Case presentation: This case report describes a 21-year-old woman with SAVI (due to a heterozygous TMEM173 mutation) who developed disseminated molluscum contagiosum (MC) while receiving baricitinib therapy. Laboratory results revealed lymphopenia, low CD4/CD8 ratio, and impaired immune cell activity, suggesting compromised antiviral immunity. Discussion: Despite SAVI’s association with excessive type I interferon signaling, this chronic hyperactivation may cause immune dysregulation, exhausting T cells and natural killer cells vital for viral defense. Furthermore, baricitinib suppresses interferon signaling via the JAK-STAT pathway, reducing inflammatory damage in SAVI but also impairing antiviral responses. Moreover, MC viruses evade host immune defenses by antagonizing STING and TANK-binding kinase 1-mediated interferon activation, further contributing to infection risk. This report is the first to document MC in a SAVI patient and highlights the rare complication of disseminated MC due to impaired type I interferon signaling and immune suppression from baricitinib therapy. This case underscores the need for vigilance regarding viral infections in SAVI patients treated with JAK inhibitors. Full article