Dermato, Volume 5, Issue 2 (June 2025) – 4 articles

Background: Artificial intelligence (AI) has emerged as a transformative tool in modern medicine, particularly in dermatology, where it supports the diagnosis and management of various skin diseases, including skin cancer. Through machine learning and deep learning techniques, AI enables accurate analysis of clinical and dermoscopic images, improving early detection and clinical outcomes. Objective: This systematic review aimed to evaluate the clinical applications of AI in dermatology, focusing on its impact on diagnostic accuracy, workflow efficiency, and access to specialized care. Methods: The review was conducted according to PRISMA guidelines. Peer-reviewed studies published between January 2020 and March 2025 in English or Spanish were included if they evaluated AI-based tools for dermatological diagnosis, classification, or treatment. Animal studies, editorials, non-peer-reviewed articles, and studies with an unclear methodology were excluded. A comprehensive search was performed in PubMed, Scopus, IEEE Xplore, and Google Scholar between December 2024 and March 2025. The risk of bias was assessed qualitatively, using a tailored framework based on study design, dataset transparency, and clinical applicability. Results: A total of 29 studies met the inclusion criteria. AI tools demonstrated high performance in melanoma detection, achieving up to 90% accuracy and 85% sensitivity. In clinical settings, AI support reduced mismanagement of malignant lesions from 58.8% to 4.1% and avoided 27% of unnecessary procedures in benign cases. Additional tools such as convolutional neural networks and imaging systems like FotoFinder also showed promising results. Limitations: Limitations of the evidence include the heterogeneity of AI models, lack of external validation, and a moderate-to-high risk of bias. Conclusions: AI has demonstrated robust clinical potential in dermatology, particularly in cancer detection and workflow optimization. However, further studies are required to address challenges such as algorithmic bias, data privacy, and regulatory oversight. Funding and registration: This review received no external funding and was not registered in a systematic review registry. Full article

Background/objectives: According to the Chapel Hill Consensus Conference nomenclature, single-organ cutaneous small vessel vasculitis (SOCV) is defined histopathologically by immune complex-mediated vasculitis of the dermal capillaries/venules without systemic involvement. There is a lack of investigations studying predictors of SOCV outcomes. This multicenter retrospective study aimed to assess whether baseline serum liver scores could predict SOCV recurrence. Methods: Data from 204 inpatients with histopathologically confirmed idiopathic SOCV treated between 2000 and 2022 were analyzed. All patients had baseline blood tests for platelets and liver parameters; those with systemic diseases were excluded. The study evaluated the AST to Platelet Ratio Index (APRI), Fibrosis-4 (FIB-4) index, and De Ritis ratio (DRR). Results: Recurrence was observed in 17.6% of patients. Univariable analysis identified hospital stay length, DRR, cutaneous extent, and cardiovascular comorbidities as predictors, but logistic regression confirmed only cutaneous extent and DRR as independent predictors of disease recurrence. Conclusions: A higher DRR (cut-off > 1.13) may indicate necrosis from reduced blood flow, making it a potential predictor of SOCV recurrence. Full article

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