Search Results (30)

Background: Axillary hidradenitis suppurativa (HS) often requires wide surgical excision and reconstruction. Thoracodorsal artery perforator (TDAP) flaps and split-thickness skin grafts (STSGs) are common options, but comparative long-term data are insufficient. Methods: In this single-center retrospective study, patients aged ≥ 17 years with Hurley stage II–III axillary HS underwent wide excision followed by TDAP flap or STSG reconstruction. Demographic variables, surgical characteristics, complications, recurrence, shoulder mobility, and dermatology-specific quality-of-life outcomes assessed using the Dermatology Life Quality Index (DLQI) were analyzed. Results: In total, 35 reconstructions were reviewed: TDAP (n = 15, 42.9%) and STSG (n = 20, 57.1%). Follow-up was longer for TDAP (28.53 ± 16.38 vs. 19.65 ± 28.06 months; p = 0.014). Mean defect size was 105.47 ± 26.29 cm² (TDAP) vs. 164.65 ± 77.99 cm² (STSG; p = 0.116). Both groups showed significant improvement in DLQI from preoperative to postoperative assessments (TDAP: +20.87; Graft: +18.50; both p < 0.0001), with no significant postoperative difference (p = 0.9608). Smokers had higher preoperative DLQI scores than non-smokers (+5.72; p = 0.0051), but postoperative outcomes were similar (p = 0.5908). Conclusions: Both reconstructions after wide axillary excision provided durable coverage, low complication rates, and significant improvement in quality of life. Incorporating patient-reported and functional outcomes into reconstructive planning may optimize surgical decision-making for axillary HS. Full article

Background: Parasitic skin-related conditions represent a frequent and evolving challenge in human dermatology, as they often mimic other dermatoses, and are increasingly complicated by therapeutic resistance. With this study, we aimed to provide a practical, clinician-oriented overview of our experience, contextualising it within the current literature. Materials and Methods: We conducted a retrospective, single-centre observational study, reporting a case series of 88 patients diagnosed with parasitic or arthropod-related skin infestations at the San Raffaele Hospital Dermatology Unit (Milan) between 2019 and 2024, and integrated a concise narrative review of contemporary evidence on diagnosis, non-invasive imaging and management. For each case, we documented clinical presentation, dermoscopic or reflectance confocal microscopy (RCM) findings, and treatment response. Non-invasive tools (dermoscopy, videodermoscopy, RCM) were used when appropriate. Results: The spectrum of conditions included flea bites, bed bug bites, cutaneous larva migrans, subcutaneous dirofilariasis, Dermanyssus gallinae dermatitis, pediculosis, tick bites (including Lyme disease), myiasis, scabies, and cutaneous leishmaniasis. One case of eosinophilic dermatosis of haematologic malignancy was also considered due to its possible association with arthropod bites. Non-invasive imaging was critical in confirming suspected infestations, particularly in ambiguous cases or when invasive testing was not feasible. Several cases highlighted suspected therapeutic resistance: a paediatric pediculosis and three adult scabies cases required systemic therapy after standard regimens failed, raising concerns over putative resistance to permethrin and pyrethroids. In dirofilariasis, the persistence of filarial elements visualised by RCM justified the extension of antiparasitic therapy despite prior surgical removal. Conclusions: Our findings underline that accurate diagnosis, early intervention, and tailored treatment remain essential for the effective management of cutaneous infestations. The observed vast spectrum of isolated parasites reflects broader health and ecological dynamics, including zoonotic transmission, international mobility, and changing environmental conditions. At the same time, diagnostic delays, inappropriate treatments, and neglected parasitic diseases continue to pose significant risks. To address these challenges, clinicians should remain alert to atypical presentations, and consider a multidisciplinary approach including the consultation with parasitologists and veterinarians, as well as the incorporation of high-resolution imaging and alternative therapeutic strategies into their routine practice. Full article

Artificial intelligence (AI) and machine learning (ML) are transforming medical diagnostics, but human nail, an easily accessible and rich biological substrate, is still not fully exploited in the digital health field. Nail pathologies are easily diagnosed, non-invasive disease biomarkers, including systemic diseases such as anemia, diabetes, psoriasis, melanoma, and fungal diseases. This review presents the first big synthesis of image analysis for nail lesions incorporating AI/ML for diagnostic purposes. Where dermatological reviews to date have been more wide-ranging in scope, our review will focus specifically on diagnosis and screening related to nails. The various technological modalities involved (smartphone imaging, dermoscopy, Optical Coherence Tomography) will be presented, together with the different processing techniques for images (color corrections, segmentation, cropping of regions of interest), and models that range from classical methods to deep learning, with annotated descriptions of each. There will also be additional descriptions of AI applications related to some diseases, together with analytical discussions regarding real-world impediments to clinical application, including scarcity of data, variations in skin type, annotation errors, and other laws of clinical adoption. Some emerging solutions will also be emphasized: explainable AI (XAI), federated learning, and platform diagnostics allied with smartphones. Bridging the gap between clinical dermatology, artificial intelligence and mobile health, this review consolidates our existing knowledge and charts a path through yet others to scalable, equitable, and trustworthy nail based medically diagnostic techniques. Our findings advocate for interdisciplinary innovation to bring AI-enabled nail analysis from lab prototypes to routine healthcare and global screening initiatives. Full article

Melanoma is among the most lethal forms of skin cancer, where early and accurate diagnosis significantly improves patient survival. Traditional diagnostic pathways, including clinical inspection and dermoscopy, are constrained by interobserver variability and limited access to expertise. Between 2020 and 2025, advances in artificial intelligence (AI) and medical imaging technologies have substantially redefined melanoma diagnostics. This narrative review synthesizes key developments in AI-based approaches, emphasizing the progression from convolutional neural networks to vision transformers and multimodal architectures that incorporate both clinical and imaging data. We examine the integration of AI with non-invasive imaging techniques such as reflectance confocal microscopy, high-frequency ultrasound, optical coherence tomography, and three-dimensional total body photography. The role of AI in teledermatology and mobile applications is also addressed, with a focus on expanding diagnostic accessibility. Persistent challenges include data bias, limited generalizability across diverse skin types, and a lack of prospective clinical validation. Recent regulatory frameworks, including the European Union Artificial Intelligence Act and the United States Food and Drug Administration’s guidance on adaptive systems, are discussed in the context of clinical deployment. The review concludes with perspectives on explainable AI, federated learning, and strategies for equitable implementation in dermatological oncology. Full article

Background: In recent decades, dermatological diagnostics have undergone a profound transformation, driven by the integration of new technologies alongside traditional methods. Classic techniques such as the Tzanck smear, potassium hydroxide (KOH) preparation, and Wood’s lamp examination remain fundamental in everyday clinical practice due to their simplicity, speed, and accessibility. At the same time, the development of non-invasive imaging technologies and the application of artificial intelligence (AI) have opened new frontiers in the early detection and monitoring of both neoplastic and inflammatory skin diseases. Methods: This review aims to provide a comprehensive overview of how conventional and emerging diagnostic tools can be integrated into dermatologic practice. Results: We examined a broad spectrum of diagnostic methods currently used in dermatology, ranging from traditional techniques to advanced approaches such as digital dermoscopy, reflectance confocal microscopy (RCM), optical coherence tomography (OCT), line-field confocal OCT (LC-OCT), 3D total body imaging systems with AI integration, mobile applications, electrical impedance spectroscopy (EIS), and multispectral imaging. Each method is discussed in terms of diagnostic accuracy, clinical applications, and potential limitations. While traditional methods continue to play a crucial role—especially in resource-limited settings or for immediate bedside decision-making—modern tools significantly enhance diagnostic precision. Dermoscopy and its digital evolution have improved the accuracy of melanoma and basal cell carcinoma detection. RCM and LC-OCT allow near-histological visualization of skin structures, reducing the need for invasive procedures. AI-powered platforms support lesion tracking and risk stratification, though their routine implementation requires further clinical validation and regulatory oversight. Tools like EIS and multispectral imaging may offer additional value in diagnostically challenging cases. An effective diagnostic approach in dermatology should rely on a thoughtful combination of methods, selected based on clinical suspicion and guided by Bayesian reasoning. Conclusions: Rather than replacing traditional tools, advanced technologies should complement them—optimizing diagnostic accuracy, improving patient outcomes, and supporting more individualized, evidence-based care. Full article

Background/Objectives: The accurate identification of insect bites from images of skin is daunting due to the fine gradations among diverse bite types, variability in human skin response, and inconsistencies in image quality. Methods: For this work, we introduce DeepBiteNet, a new ensemble-based deep learning model designed to perform robust multiclass classification of insect bites from RGB images. Our model aggregates three semantically diverse convolutional neural networks—DenseNet121, EfficientNet-B0, and MobileNetV3-Small—using a stacked meta-classifier designed to aggregate their predicted outcomes into an integrated, discriminatively strong output. Our technique balances heterogeneous feature representation with suppression of individual model biases. Our model was trained and evaluated on a hand-collected set of 1932 labeled images representing eight classes, consisting of common bites such as mosquito, flea, and tick bites, and unaffected skin. Our domain-specific augmentation pipeline imputed practical variability in lighting, occlusion, and skin tone, thereby boosting generalizability. Results: Our model, DeepBiteNet, achieved a training accuracy of 89.7%, validation accuracy of 85.1%, and test accuracy of 84.6%, and surpassed fifteen benchmark CNN architectures on all key indicators, viz., precision (0.880), recall (0.870), and F1-score (0.875). Our model, optimized for mobile deployment with quantization and TensorFlow Lite, enables rapid on-client computation and eliminates reliance on cloud-based processing. Conclusions: Our work shows how ensemble learning, when carefully designed and combined with realistic data augmentation, can boost the reliability and usability of automatic insect bite diagnosis. Our model, DeepBiteNet, forms a promising foundation for future integration with mobile health (mHealth) solutions and may complement early diagnosis and triage in dermatologically underserved regions. Full article

Acne vulgaris is a common dermatological condition, particularly affecting adolescents during critical developmental stages, which may have lasting psychosocial impacts. Traditional assessments, including global severity grading and lesion counting, are limited by subjectivity and time constraints. Background/Objectives: This review aims to systematically assess the recent advancements in artificial intelligence (AI) applications for acne diagnosis, lesion segmentation/counting, and severity grading, highlighting the potential of AI-driven methods to improve objectivity, reproducibility, and clinical efficiency. Methods: A comprehensive literature search was conducted across PubMed, Scopus, arXiv, Embase, and Web of Science for studies published between 1 January 2017 and 1 March 2025. The search strategy incorporated terms related to “acne” and various AI methodologies (e.g., “neural network”, “deep learning”, “convolutional neural network”). Two independent reviewers screened 345 articles, with 29 studies ultimately meeting inclusion criteria. Data were extracted on study design, dataset characteristics (including internal and publicly available resources such as ACNE04 and AcneSCU), AI architectures (predominantly CNN-based models), and performance metrics. Results: While AI-driven models demonstrated promising accuracy, as high as 97.6% in controlled settings, the limited availability of large public datasets, the predominance of data from specific ethnic groups, and the lack of extensive external validation underscore critical barriers to clinical implementation. Conclusions: The findings indicate that although AI has the potential to standardize acne assessments, reduce observer variability, and enable self-monitoring via mobile platforms, significant challenges remain in achieving robust, real-world applicability. Future research should prioritize the development of large, diverse, and publicly accessible datasets and undertake prospective clinical validations to ensure equitable and effective dermatological care. 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

The rising incidence of skin cancer, particularly melanoma, underscores the need for improved diagnostic tools in dermatology. Accurate imaging plays a crucial role in early detection, yet challenges related to color accuracy, image distortion, and resolution persist, leading to diagnostic errors. This study addresses these issues by evaluating color reproduction accuracy across various imaging devices and lighting conditions. Using a ColorChecker test chart, color deviations were measured through Euclidean distances (ΔE*, ΔC*), and nonlinear color differences (ΔE00, ΔC00), while the color rendering index (CRI) and television lighting consistency index (TLCI) were used to evaluate the influence of light sources on image accuracy. Significant color discrepancies were identified among mobile phones, DSLRs, and mirrorless cameras, with inadequate dermatoscope lighting systems contributing to further inaccuracies. We demonstrate practical applications, including manual camera adjustments, grayscale reference cards, post-processing techniques, and optimized lighting conditions, to improve color accuracy. This study provides applicable solutions for enhancing color accuracy in dermatological imaging, emphasizing the need for standardized calibration techniques and imaging protocols to improve diagnostic reliability, support AI-assisted skin cancer detection, and contribute to high-quality image databases for clinical and automated analysis. Full article

Liposomal preparations play an important role as formulations for transdermal drug delivery; however, the electrical conductivity of these systems is sparingly evaluated. The aim of the study was to outline the range of the values of electrical conductivity values that may be recorded in the future pharmaceutical systems in the context of their viscosity. The electrical conductivity, measured by a conductivity probe of k = 1.0 cm⁻¹, and the dynamic viscosity of liposomal and non-liposomal systems with naproxen sodium, embedded into a methylcellulose hydrophilic gel (0.25%), were compared with data from preparations without naproxen sodium in a range reflecting the naproxen sodium concentrations 0.1·10⁻²–9.5·10⁻² mol/L. The specific conductivity covered a 1.5 μS·cm⁻¹–5616.0 μS·cm⁻¹ range, whereas the viscosity ranged from 0.9 to 9.4 mPa·s. The naproxen sodium highly influenced the electrical conductivity, whereas the dynamic viscosity was a moderate factor. The observed phenomena may be ascribed to the high mobility of sodium ions recruited from naproxen sodium and the relatively low concentrations of applied methylcellulose. The assembly of lecithin in liposomes may have lowered the specific conductivity of the systems with naproxen sodium. These measurements will be further developed for implementation as simple assays of the concentrations of active pharmaceutical ingredient in release experiments of preparations proposed for dermatological applications. Full article

Skin cancer represents a significant global public health concern, with over five million new cases diagnosed annually. If not diagnosed at an early stage, skin diseases have the potential to pose a significant threat to human life. In recent years, deep learning has increasingly been used in dermatological diagnosis. In this paper, a multiclassification model based on the Inception-v2 network and the focal loss function is proposed on the basis of deep learning, and the ISIC 2019 dataset is optimised using data augmentation and hair removal to achieve seven classifications of dermatological images and generate heat maps to visualise the predictions of the model. The results show that the model has an average accuracy of 89.04%, a precision of 87.37%, recall of 90.15%, and an F1-score of 88.76%, The accuracy rates of ResNext101, MobileNetv2, Vgg19, and ConvNet are 88.50%, 85.30%, 88.57%, and 86.90%, respectively. These results show that our proposed model performs better than the above models and performs well in classifying dermatological images, which has significant application value. Full article

Artificial intelligence (AI) based on convolutional neural networks (CNNs) has recently made great advances in dermatology with respect to the classification and malignancy prediction of skin diseases. In this article, we demonstrate how we have used a similar technique to build a mobile application to classify skin diseases captured by patients with their personal smartphone cameras. We used a CNN classifier to distinguish four subtypes of dermatological diseases the patients might have (“pigmentation changes and superficial infections”, “inflammatory diseases and eczemas”, “benign tumors, cysts, scars and callous”, and “suspected lesions”) and their severity in terms of morbidity and mortality risks, as well as the kind of medical consultation the patient should seek. The dataset used in this research was collected by the Department of Telemedicine of Albert Einstein Hospital in Sao Paulo and consisted of 146.277 skin images. In this paper, we show that our CNN models with an overall average classification accuracy of 79% and a sensibility of above 80% implemented in personal smartphones have the potential to lower the frequency of skin diseases and serve as an advanced tracking tool for a patient’s skin-lesion history. Full article

Psoriasis is a chronic autoimmune inflammatory skin disorder that affects approximately 2–3% of the global population due to significant genetic predisposition. It is characterized by an uncontrolled growth and differentiation of keratinocytes, leading to the formation of scaly erythematous plaques. Psoriasis extends beyond dermatological manifestations to impact joints and nails and is often associated with systemic disorders. Although traditional treatments provide relief, their use is limited by potential side effects and the chronic nature of the disease. This review aims to discuss the therapeutic potential of keratinocyte-targeting natural products in psoriasis and highlight their efficacy and safety in comparison with conventional treatments. This review comprehensively examines psoriasis pathogenesis within keratinocytes and the various related signaling pathways (such as JAK-STAT and NF-κB) and cytokines. It presents molecular targets such as high-mobility group box-1 (HMGB1), dual-specificity phosphatase-1 (DUSP1), and the aryl hydrocarbon receptor (AhR) for treating psoriasis. It evaluates the ability of natural compounds such as luteolin, piperine, and glycyrrhizin to modulate psoriasis-related pathways. Finally, it offers insights into alternative and sustainable treatment options with fewer side effects. Full article

Background: The prevalence of skin diseases such as leprosy, and limited dermatological knowledge among frontline health workers (FHWs) in rural areas of Sub-Saharan Africa, led to the development of the NLR SkinApp: a mobile application (app) that supports FHWs to promptly diagnose and treat, or suspect and refer patients with skin diseases. The app includes common skin diseases, neglected tropical skin diseases (skin NTDs) such as leprosy, and HIV/AIDS-related skin conditions. This study aimed to test the supporting role of the NLR SkinApp by examining the diagnostic accuracy of its third edition. Methods: A cross-sectional study was conducted in East Hararghe, Ethiopia, as well as the Mwanza and Morogoro region, Tanzania, in 2018–2019. Diagnostic accuracy was measured against a diagnosis confirmed by two dermatologists/dermatological medical experts (reference standard) in terms of sensitivity, specificity, positive predictive value, and negative predictive value. The potential negative effect of an incorrect management recommendation was expressed on a scale of one to four. Results: A total of 443 patients with suspected skin conditions were included. The FHWs using the NLR SkinApp diagnosed 45% of the patients accurately. The values of the sensitivity of the FHWs using the NLR SkinApp in determining the correct diagnosis ranged from 23% for HIV/AIDS-related skin conditions to 76.9% for eczema, and the specificity from 69.6% for eczema to 99.3% for tinea capitis/corporis. The inter-rater reliability among the FHWs for the diagnoses made, expressed as the percent agreement, was 58% compared to 96% among the dermatologists. Of the management recommendations given on the basis of incorrect diagnoses, around one-third could have a potential negative effect. Conclusions: The results for diagnosing eczema are encouraging, demonstrating the potential contribution of the NLR SkinApp to dermatological and leprosy care by FHWs. Further studies with a bigger sample size and comparing FHWs with and without using the NLR SkinApp are needed to obtain a better understanding of the added value of the NLR SkinApp as a mobile health (mHealth) tool in supporting FHWs to diagnose and treat skin diseases. Full article

Excessive epidermal hyperkeratosis in acral areas is a common occurrence in dermatology practice, with a notable prevalence of approximately 65% in the elderly, especially in plantar lesions. Hyperkeratosis, characterized by thickening of the stratum corneum, can have various causes, including chronic physical or chemical factors, genetic predispositions, immunological disorders, and pharmaceutical compounds. This condition can significantly impact mobility, increase the risk of falls, and reduce the overall quality of life, particularly in older individuals. Management often involves creams containing urea to soften hyperkeratotic areas. Currently, subjective visual evaluation is the gold standard for assessing hyperkeratosis severity, lacking precision and consistency. Therefore, our research group proposes a novel 6-point keratinization scale based on dermatoscopy with cross-polarization and parallel-polarization techniques. This scale provides a structured framework for objective assessment, aiding in treatment selection, duration determination, and monitoring disease progression. Its clinical utility extends to various dermatological conditions involving hyperkeratosis, making it a valuable tool in dermatology practice. This standardized approach enhances communication among healthcare professionals, ultimately improving patient care and research comparability in dermatology. Full article