Search Results (19)

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

Retrotransposons have played an important role in evolution through their transposable activity. The largest and the only currently active human group of mobile DNAs are the LINE-1 retrotransposons. The ectopic expression of LINE-1 has been correlated with genomic instability. Narrow-band ultraviolet B (NB-UVB) and broad-band ultraviolet B (BB-UVB) phototherapy is commonly used for the treatment of dermatological diseases. UVB exposure is carcinogenic and can lead, in keratinocytes, to genomic instability. We hypothesize that LINE-1 reactivation occurs at a high rate in response to UVB exposure on the skin, which significantly contributes to genomic instability and DNA damage leading to cellular senescence and photoaging. Immortalized N/TERT1 and HaCaT human keratinocyte cell lines were irradiated in vitro with either NB-UVB or BB-UVB. Using immunofluorescence and Western blotting, we confirmed UVB-induced protein expression of LINE-1. Using RT-qPCR, we measured the mRNA expression of LINE-1 and senescence markers that were upregulated after several NB-UVB exposures. Selected miRNAs that are known to bind LINE-1 mRNA were measured using RT-qPCR, and the expression of miR-16 was downregulated with UVB exposure. Our findings demonstrate that UVB irradiation induces LINE-1 reactivation and DNA damage in normal keratinocytes along with the associated upregulation of cellular senescence markers and change in miR-16 expression. Full article

This study presents an enhanced deep learning approach for the accurate detection of eczema and psoriasis skin conditions. Eczema and psoriasis are significant public health concerns that profoundly impact individuals’ quality of life. Early detection and diagnosis play a crucial role in improving treatment outcomes and reducing healthcare costs. Leveraging the potential of deep learning techniques, our proposed model, named “Derma Care,” addresses challenges faced by previous methods, including limited datasets and the need for the simultaneous detection of multiple skin diseases. We extensively evaluated “Derma Care” using a large and diverse dataset of skin images. Our approach achieves remarkable results with an accuracy of 96.20%, precision of 96%, recall of 95.70%, and F1-score of 95.80%. These outcomes outperform existing state-of-the-art methods, underscoring the effectiveness of our novel deep learning approach. Furthermore, our model demonstrates the capability to detect multiple skin diseases simultaneously, enhancing the efficiency and accuracy of dermatological diagnosis. To facilitate practical usage, we present a user-friendly mobile phone application based on our model. The findings of this study hold significant implications for dermatological diagnosis and the early detection of skin diseases, contributing to improved healthcare outcomes for individuals affected by eczema and psoriasis. Full article

Mobile health (mHealth) has recently had significant advances in tele-dermatology (TD) thanks to the developments following the COVID-19 pandemic. This topic is very important, as telemedicine and mHealth, when applied to dermatology, could improve both the quality of healthcare for citizens and the workflow in the health domain. The proposed study was centered on the last three years. We conducted an overview on the opportunities, the perspectives, and the problems involved in TD integration with mHealth. The methodology of the narrative review was based on: (I) a search of PubMed and Scopus and (II) an eligibility assessment, using properly proposed parameters. The outcome of the study showed that during the COVID-19 pandemic, TD integration with mHealth advanced rapidly. This integration enabled the monitoring of dermatological problems and facilitated remote specialist visits, reducing face-to-face interactions. AI and mobile apps have empowered citizens to take an active role in their healthcare. This differs from other imaging sectors where information exchange is limited to professionals. The opportunities for TD in mHealth include improving service quality, streamlining healthcare processes, reducing costs, and providing more accessible care. It can be applied to various conditions, such as (but not limited to) acne, vitiligo, psoriasis, and skin cancers. Integration with AI and augmented reality (AR), as well as the use of wearable sensors, are anticipated as future developments. However, integrating TD with mHealth also brings about problems and challenges related to regulations, ethics, cybersecurity, data privacy, and device management. Scholars and policymakers need to address these issues while involving citizens in the process. Full article

Skin cancer is the most common cancer diagnosis in the United States, with approximately one in five Americans expected to be diagnosed within their lifetime. Non-melanoma skin cancer is the most prevalent type of skin cancer, and as cases rise globally, physicians need reliable tools for early detection. Artificial intelligence has gained substantial interest as a decision support tool in medicine, particularly in image analysis, where deep learning has proven to be an effective tool. Because specialties such as dermatology rely primarily on visual diagnoses, deep learning could have many diagnostic applications, including the diagnosis of skin cancer. Furthermore, with the advancement of mobile smartphones and their increasingly powerful cameras, deep learning technology could also be utilized in remote skin cancer screening applications. Ultimately, the available data for the detection and diagnosis of skin cancer using deep learning technology are promising, revealing sensitivity and specificity that are not inferior to those of trained dermatologists. Work is still needed to increase the clinical use of AI-based tools, but based on the current data and the attitudes of patients and physicians, deep learning technology could be used effectively as a clinical decision-making tool in collaboration with physicians to improve diagnostic efficiency and accuracy. Full article

Artificial intelligence (AI) is recently seeing significant advances in teledermatology (TD), also thanks to the developments that have taken place during the COVID-19 pandemic. In the last two years, there was an important development of studies that focused on opportunities, perspectives, and problems in this field. The topic is very important because the telemedicine and AI applied to dermatology have the opportunity to improve both the quality of healthcare for citizens and the workflow of healthcare professionals. This study conducted an overview on the opportunities, the perspectives, and the problems related to the integration of TD with AI. The methodology of this review, following a standardized checklist, was based on: (I) a search of PubMed and Scopus and (II) an eligibility assessment, using parameters with five levels of score. The outcome highlighted that applications of this integration have been identified in various skin pathologies and in quality control, both in eHealth and mHealth. Many of these applications are based on Apps used by citizens in mHealth for self-care with new opportunities but also open questions. A generalized enthusiasm has been registered regarding the opportunities and general perspectives on improving the quality of care, optimizing the healthcare processes, minimizing costs, reducing the stress in the healthcare facilities, and in making citizens, now at the center, more satisfied. However, critical issues have emerged related to: (a) the need to improve the process of diffusion of the Apps in the hands of citizens, with better design, validation, standardization, and cybersecurity; (b) the need for better attention paid to medico-legal and ethical issues; and (c) the need for the stabilization of international and national regulations. Targeted agreement initiatives, such as position statements, guidelines, and/or consensus initiatives, are needed to ensure a better result for all, along with the design of both specific plans and shared workflows. Full article

Skin problems are among the most common ailments on Earth. Despite its popularity, assessing it is not easy because of the complexities in skin tones, hair colors, and hairstyles. Skin disorders provide a significant public health risk across the globe. They become dangerous when they enter the invasive phase. Dermatological illnesses are a significant concern for the medical community. Because of increased pollution and poor diet, the number of individuals with skin disorders is on the rise at an alarming rate. People often overlook the early signs of skin illness. The current approach for diagnosing and treating skin conditions relies on a biopsy process examined and administered by physicians. Human assessment can be avoided with a hybrid technique, thus providing hopeful findings on time. Approaches to a thorough investigation indicate that deep learning methods might be used to construct frameworks capable of identifying diverse skin conditions. Skin and non-skin tissue must be distinguished to detect skin diseases. This research developed a skin disease classification system using MobileNetV2 and LSTM. For this system, accuracy in skin disease forecasting is the primary aim while ensuring excellent efficiency in storing complete state information for exact forecasts. Full article

Pelotherapy and electrotherapy are therapeutic methodologies with proven success in physical medicine and rehabilitation (PMR) and dermatology fields. The main purpose of these therapeutic modalities is to reduce pain, accelerate wound healing, alleviate muscle spasms, and improve mobility, and muscle tone. Their main challenge is in the passage of some ionic species through the skin barrier. The use of drugs, such as diclofenac, corticosteroids or steroids, has gained widespread efficacy recognition in physical therapy and the therapeutic action of these drugs is widely studied in experimental and clinical trials. Unlike pharmaceutical and cosmetic clays, peloids are not subject to any prior quality control or subject to any specific European regulation. The dermal absorption values are an integral part of the risk assessment process for peloids. This work explores the converging points between these two transdermal drug delivery systems (TDDS) and the presentation of methodologies to achieve peloid safety compliance, especially concerning the potential and degree of toxicity arising from ion exchange and trace elements. TDDS is applied to the pharmaceuticals industry and drug is the generic term for the active substances released into skin tissues. The transdermal delivery of drugs or clay components with therapeutic properties is limited due to the excellent barrier function of the stratum corneum. The transdermal drug delivery of pelotherapy is enhanced by temperature and electrically by iontophoresis. The low voltage of iontophoresis and sweat phenomena with pore dilation driven by pelotherapy allows the use of the same pathways: hair follicles and sweat pore. The therapeutic integration of iontophoresis and pelotherapy focused on patient benefits and low safety-related risk may contribute to the outstanding physiological performance of pelotherapy, specifically, in the way the essential elements and exchange cations pass through the skin barrier. The validation of an innovative iontophoretic systems applied to pelotherapy can also promote future challenges in the obtaining of the ideal therapeutic control of peloids and the clinical validation of results with physiological efficacy recognition. Full article

With the increasing adoption of teledermatology, there is a need to improve the automatic organization of medical records, being dermatological image modality a key filter in this process. Although there has been considerable effort in the classification of medical imaging modalities, this has not been in the field of dermatology. Moreover, as various devices are used in teledermatological consultations, image acquisition conditions may differ. In this work, two models (VGG-16 and MobileNetV2) were used to classify dermatological images from the Portuguese National Health System according to their modality. Afterwards, four incremental learning strategies were applied to these models, namely naive, elastic weight consolidation, averaged gradient episodic memory, and experience replay, enabling their adaptation to new conditions while preserving previously acquired knowledge. The evaluation considered catastrophic forgetting, accuracy, and computational cost. The MobileNetV2 trained with the experience replay strategy, with 500 images in memory, achieved a global accuracy of 86.04% with only 0.0344 of forgetting, which is 6.98% less than the second-best strategy. Regarding efficiency, this strategy took 56 s per epoch longer than the baseline and required, on average, 4554 megabytes of RAM during training. Promising results were achieved, proving the effectiveness of the proposed approach. Full article

Dermoscopic images allow the detailed examination of subsurface characteristics of the skin, which led to creating several substantial databases of diverse skin lesions. However, the dermoscope is not an easily accessible tool in some regions. A less expensive alternative could be acquiring medium resolution clinical macroscopic images of skin lesions. However, the limited volume of macroscopic images available, especially mobile-acquired, hinders developing a clinical mobile-based deep learning approach. In this work, we present a technique to efficiently utilize the sizable number of dermoscopic images to improve the segmentation capacity of macroscopic skin lesion images. A Cycle-Consistent Adversarial Network is used to translate the image between the two distinct domains created by the different image acquisition devices. A visual inspection was performed on several databases for qualitative evaluation of the results, based on the disappearance and appearance of intrinsic dermoscopic and macroscopic features. Moreover, the Fréchet Inception Distance was used as a quantitative metric. The quantitative segmentation results are demonstrated on the available macroscopic segmentation databases, SMARTSKINS and Dermofit Image Library, yielding test set thresholded Jaccard Index of 85.13% and 74.30%. These results establish a new state-of-the-art performance in the SMARTSKINS database. Full article

Background and Objectives: Acne, an inflammatory disorder of the pilosebaceous unit associated with both physiological and psychological morbidities, should be considered a chronic disease. The application of self-regulation theory and therapeutic patient education has been widely utilized in different health-related areas to help patient with a chronic disease to attain better behavioral modification. The present study aims at investigating the treatment efficacy of combining a self-regulation-based patient education module with mobile application in acne patients. Materials and Methods: This was one-grouped pretest–posttest design at a single tertiary referral center with the enrollment of 30 subjects diagnosed with acne vulgaris. Relevant information was collected before (week 0) and after (week 4) treatment in the present study, including the Acne Self-Regulation Inventory (ASRI), Cardiff Acne Disability Index (CADI), and Dermatology Life Quality Index (DLQI) that involved a questionnaire-based subjective evaluation of the patient’s ability in self-regulation and quality of life as well as clinical Acne Grading Scores (AGS) that objectively assessed changes in disease severity. To reinforce availability and feasibility, an individualized platform was accessible through mobile devices for real-time problem solving between hospital visits. Results: Thirty subjects completed the designed experiment. An analysis of the differences between scores of pretest and posttest of ASRI demonstrated substantial elevations (p < 0.001). The questionnaire survey of CADI and DLQI dropped significantly after the application of a self-regulation-based patient education module with a mobile application, revealing substantial reductions in both parameters (p < 0.001). The sign test demonstrated a remarkably significant difference in AGS (Z = −7.38, p < 0.001), indicating notable improvement in the clinical severity of acne after treatment. Conclusions: After incorporating modern mobile application, a self-regulation-based therapeutic patient education module could significantly improve treatment outcomes among acne patients. Full article

Over recent years, there has been an increase in popularity of the acquisition of dermoscopic skin lesion images using mobile devices, more specifically using the smartphone camera. The demand for self-care and telemedicine solutions requires suitable methods to guide and evaluate the acquired images’ quality in order to improve the monitoring of skin lesions. In this work, a system for automated focus assessment of dermoscopic images was developed using a feature-based machine learning approach. The system was designed to guide the user throughout the acquisition process by means of a preview image validation approach that included artifact detection and focus validation, followed by the image quality assessment of the acquired picture. This paper also introduces two different datasets, dermoscopic skin lesions and artifacts, which were collected using different mobile devices to develop and test the system. The best model for automatic preview assessment attained an overall accuracy of 77.9% while focus assessment of the acquired picture reached a global accuracy of 86.2%. These findings were validated by implementing the proposed methodology within an android application, demonstrating promising results as well as the viability of the proposed solution in a real life scenario. Full article