Search Results (8)

Skin diseases are listed among the most frequently encountered diseases. Skin diseases such as eczema, melanoma, and others necessitate early diagnosis to avoid further complications. This study aims to enhance the diagnosis of skin disease by utilizing advanced image processing techniques and an attention-based vision approach to support dermatologists in solving classification problems. Initially, the image is being passed through various processing steps to enhance the quality of the dataset. These steps are adaptive histogram equalization, binary cross-entropy with implicit averaging, gamma correction, and contrast stretching. Afterwards, enhanced images are passed through the attention-based approach for performing classification which is based on the encoder part of the transformers and multi-head attention. Extensive experimentation is performed to collect the various results on two publicly available datasets to show the robustness of the proposed approach. The evaluation of the proposed approach on two publicly available datasets shows competitive results as compared to a state-of-the-art approach. 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

In dermatological research, 2,4-dinitrochlorbenzene (DNCB)-induced atopic dermatitis (AD) is a standard model as it displays many disease-associated characteristics of human AD. However, the reproducibility of the model is challenging due to the lack of information regarding the methodology and the description of the phenotype and endotype of the mimicked disease. In this study, a DNCB-induced mouse model was established with a detailed procedure description and classification of the AD human-like skin type. The disease was induced with 1% DNCB in the sensitization phase and repeated applications of 0.3% and 0.5% DNCB in the challenging phase which led to a mild phenotype of AD eczema. Pathophysiological changes of the dorsal skin were measured: thickening of the epidermis and dermis, altered skin barrier proteins, increased TH1 and TH2 cytokine expression, a shift in polyunsaturated fatty acids, increased pro-resolving and inflammatory mediator formation, and dysregulated inflammation-associated gene expression. A link to type I allergy reactions was evaluated by increased mast cell infiltration into the skin accompanied by elevated IgE and histamine levels in plasma. As expected for mild AD, no systemic inflammation was observed. In conclusion, this experimental setup demonstrates many features of a mild human-like extrinsic AD in murine skin. Full article

Human skin pigmentation and melanin synthesis are incredibly variable, and are impacted by genetics, UV exposure, and some drugs. Patients’ physical appearance, psychological health, and social functioning are all impacted by a sizable number of skin conditions that cause pigmentary abnormalities. Hyperpigmentation, where pigment appears to overflow, and hypopigmentation, where pigment is reduced, are the two major classifications of skin pigmentation. Albinism, melasma, vitiligo, Addison’s disease, and post-inflammatory hyperpigmentation, which can be brought on by eczema, acne vulgaris, and drug interactions, are the most common skin pigmentation disorders in clinical practice. Anti-inflammatory medications, antioxidants, and medications that inhibit tyrosinase, which prevents the production of melanin, are all possible treatments for pigmentation problems. Skin pigmentation can be treated orally and topically with medications, herbal remedies, and cosmetic products, but a doctor should always be consulted before beginning any new medicine or treatment plan. This review article explores the numerous types of pigmentation problems, their causes, and treatments, as well as the 25 plants, 4 marine species, and 17 topical and oral medications now on the market that have been clinically tested to treat skin diseases. Full article

Cutaneous manifestations are one of the most common presentations among patients with inborn errors of immunity (IEI). These skin manifestations are often among the first presenting features in the majority of patients preceding the IEI diagnosis. We studied 521 available monogenic patients with IEI listed in the Iranian IEI registry up to November 2022. We extracted each patient’s demographic information, detailed clinical history of cutaneous manifestations, and immunologic evaluations. The patients were then categorized and compared based on their phenotypical classifications provided by the International Union of Immunological Societies. Most patients were categorized into syndromic combined immunodeficiency (25.1%), non-syndromic combined immunodeficiency (24.4%), predominantly antibody deficiency (20.7%), and diseases of immune dysregulation (20.5%). In total, 227 patients developed skin manifestations at a median (IQR) age of 2.0 (0.5–5.2) years; a total of 66 (40.7%) of these patients initially presented with these manifestations. Patients with cutaneous involvement were generally older at the time of diagnosis [5.0 (1.6–8.0) vs. 3.0 (1.0–7.0) years; p = 0.022]. Consanguinity was more common among patients who developed skin disorders (81.4% vs. 65.2%, p < 0.001). The overall skin infection rate and the type of dominant pathogens were significantly different among the IEI patients in different phenotypical classifications (p < 0.001). Atopic presentation, including urticaria, was highly prevalent among patients with congenital defects of phagocytes (p = 0.020). The frequency of eczema was also significantly higher among cases with both syndromic and non-syndromic combined immunodeficiency (p = 0.009). In contrast, autoimmune cutaneous manifestations, including alopecia and psoriasis, were most common in patients with immune dysregulation (p = 0.001) and defects in intrinsic or innate immunity (p = 0.031), respectively. The presence of autoimmune cutaneous complications significantly improved the survival rate of IEI patients (p = 0.21). In conclusion, cutaneous manifestations were observed in nearly 44% of Iranian patients with monogenic IEI. A considerable number of patients with cutaneous involvements developed these disorders as their first manifestation of the disease, which was particularly noticeable in patients with non-syndromic combined immunodeficiency and phagocytic defects. The neglected skin disorders in IEI patients might delay diagnosis, which is generally established within a 3-year interval from the development of skin-related problems. Cutaneous disorders, especially autoimmune features, might indicate a mild prognosis in IEI patients. Full article

Atopic dermatitis (AD) is a common chronic inflammatory skin dermatosis condition due to skin barrier dysfunction that causes itchy, red, swollen, and cracked skin. Currently, AD severity clinical scores are subjected to intra- and inter-observer differences. There is a need for an objective scoring method that is sensitive to skin barrier differences. The aim of this study was to evaluate the relevant skin chemical biomarkers in AD patients. We used confocal Raman micro-spectroscopy and advanced machine learning methods as means to classify eczema patients and healthy controls with sufficient sensitivity and specificity. Raman spectra at different skin depths were acquired from subjects’ lower volar forearm location using an in-house developed handheld confocal Raman micro-spectroscopy system. The Raman spectra corresponding to the skin surface from all the subjects were further analyzed through partial least squares discriminant analysis, a binary classification model allowing the classification between eczema and healthy subjects with a sensitivity and specificity of 0.94 and 0.85, respectively, using stratified K-fold (K = 10) cross-validation. The variable importance in the projection score from the partial least squares discriminant analysis classification model further elucidated the role of important stratum corneum proteins and lipids in distinguishing two subject groups. Full article

Skin diseases cases are increasing on a daily basis and are difficult to handle due to the global imbalance between skin disease patients and dermatologists. Skin diseases are among the top 5 leading cause of the worldwide disease burden. To reduce this burden, computer-aided diagnosis systems (CAD) are highly demanded. Single disease classification is the major shortcoming in the existing work. Due to the similar characteristics of skin diseases, classification of multiple skin lesions is very challenging. This research work is an extension of our existing work where a novel classification scheme is proposed for multi-class classification. The proposed classification framework can classify an input skin image into one of the six non-overlapping classes i.e., healthy, acne, eczema, psoriasis, benign and malignant melanoma. The proposed classification framework constitutes four steps, i.e., pre-processing, segmentation, feature extraction and classification. Different image processing and machine learning techniques are used to accomplish each step. 10-fold cross-validation is utilized, and experiments are performed on 1800 images. An accuracy of 94.74% was achieved using Quadratic Support Vector Machine. The proposed classification scheme can help patients in the early classification of skin lesions. Full article

Atopic eczema (AE) in the elderly is gradually increasing and has been added to the classification of AE in recent years. This investigation retrospectively analyzed 60 patients with elderly AE. Among the clinical characteristics, a male predominance, existence of several patterns of onset and clinical course, and associations with immunoglobulin (Ig)E-allergic-status and asthmatic complication were observed. The highest positive-rate and positive-score for serum-specific IgE against Dermatophagoides farinae were 83.8% and 2.65 in patients with IgE-allergic AE, and a lower incidence of lichenified eczema in the elbow and knee folds were observed. In terms of treatments and outcomes, clinical improvement and clinical remission were observed in 80.8% and 36.5% of cases, respectively, using standard treatments and combined therapy with oral corticosteroid in severe cases. As for complications and final prognosis, most elderly AE patients reached the end of life with AE, but patients with IgE-allergic AE showed significantly lower incidences of complications of malignancy and death from malignancy. These results indicate that AE in the elderly represents a new subgroup of AE with specific features. Full article