Early Skin Research Articles

Automating skin cancer screening: a deep learning

Skin cancer presents in various forms, including squamous cell carcinoma (SCC), basal cell carcinoma (BCC), and melanoma. Established risk factors include ultraviolet (UV) radiation exposure from solar or artificial sources, lighter skin pigmentation, a history of sunburns, and a family history of the disease. Early detection and prompt intervention are crucial for achieving a favorable prognosis. Traditionally, treatment modalities include surgery, radiation therapy, and chemotherapy. Recent advancements in immunotherapy have revolutionized skin cancer diagnosis, but manual identification remains time-consuming. Artificial intelligence (AI) has shown potential in skin cancer classification, leading to automated screening methods. To support dermatologists, we improved the model for classifying images. This model is able to recognize seven different kinds of skin lesions. On the ISIC dataset, an analysis has been done. This study offers a novel approach to early skin cancer diagnosis based on image processing. Our approach leverages the high accuracy of a specific convolutional neural network architecture, utilizing transfer learning with pre-trained data to further enhance detection performance. Our findings demonstrate that the employed ResNet-50 transfer learning model achieves a remarkable accuracy of 97%, while ResNet50 without augmentation gives an accuracy of 81.57% and an F1-score of 75.75%.

Role of Community Pharmacies in the Secondary Prevention of Skin Cancer

Skin cancer prevention is a public health priority due to its high incidence and potential to cause significant morbidity and mortality. This study evaluated the effectiveness of an early skin cancer detection program in a community pharmacy, utilizing advanced technologies and interdisciplinary collaboration. 1. Early detection of melanoma and non-melanoma skin cancer in patients visiting the community pharmacy. 2. Facilitate patient referral to a dermatologist through the pharmacy. A cross-sectional observational study was conducted in a community pharmacy between June 2018 and September 2023. Adult patients concerned about skin lesions were included. After obtaining written informed consent, an interview was conducted, and photographs of the lesions were taken and electronically sent to a dermatology service for a preliminary diagnosis within 24-48 hours. A total of 41 patients were evaluated (51% men; 54% over 50 years old). Benign lesions were identified in 54% of cases, avoiding unnecessary consultations and optimizing health resources. 46% presented suspicious cancer lesions requiring detailed evaluation, with 63% of this group being over 60 years old. More than 85% of the preliminary diagnoses were accurate with the images sent. The results underscore the effectiveness of early detection programs in community pharmacies for identifying benign and suspicious lesions, optimizing resources, and facilitating early interventions. The collaboration between pharmacists and dermatologists, along with the use of advanced technologies, were key to the program's success. However, the study's limitations include the sample size and its focus on a single pharmacy. Community pharmacies can play a crucial role in the secondary prevention of skin cancer, improving healthcare system efficiency and benefiting patients through early detection and timely intervention. Future studies with larger samples are recommended to validate these findings and quantify resource optimization.

Developing an early detection model for skin diseases using a hybrid deep neural network to enhance health independence in coastal communities

The object of study is a solution for early skin disease diagnosis by integrating hybrid deep neural networks – EfficientNetB7 for Classification and YOLOv8 for detection. The system is designed to classify five skin conditions: Melanoma, Basal Cell Carcinoma (BCC), melanoma is a type of skin cancer that originates from melanocytes, the cells that produce skin pigment, Melanocytic Nevi (NV) Melanocytic nevus is a mole or dark spot on the skin formed due to the accumulation of melanocytes, Benign Keratosis-like Lesions (BKL) is a term for a group of skin changes that resemble keratosis but are non-cancerous, and Seborrheic Keratoses and other benign tumors to enhance the health diagnostics. The problem to be solved in this study revolves around improving early and accurate skin disease diagnosis, particularly in resource-limited or underserved areas and the lack of Accessible Diagnostic Tools and Low Efficiency of Current Diagnostic Methods. The study highlights EfficientNetB7's classification accuracy at 94 % and YOLOv8's means average precision (mAP) of 0.812 for detection. This hybrid system processes skin images efficiently, providing classification and detection outcomes with consistent performance in multiple tests. The results demonstrate that the EfficientNetB7 model achieved an accuracy of 94 % on test data, while YOLOv8 delivered a detection performance with a mean average precision (mAP) of 0.812. The web-based system efficiently processed skin images and provided classification and detection outcomes. Furthermore, integrating EfficientNetB7 and YOLOv8 allowed the skin disease detection system to classify five different diseases and assess malignancy risk. The systems are portable and can be used with minimal setup, making them practical for real-world diagnostic use. The Scope and Practical applications are designed for accessibility in resource-limited settings. The website-based skin disease detection tool provides a user-friendly platform accessible to the public and healthcare providers, especially in underserved areas. Each application's high accuracy and ease of use make them viable aids in early diagnosis, potentially improving healthcare access

Enhancing Melanoma Diagnostic: Harnessing the Synergy of AI and CNNs for Groundbreaking Advances in Early Melanoma Detection and Treatment Strategies

ABSTRACTSkin cancer is one of the most prevalent and deadly neoplasms globally. Although melanoma constitutes a minor percentage of all skin cancer types, it presently stands as the primary cause of skin cancer‐related deaths. Previous studies indicate that deep learning algorithms may identify subtle patterns and detailed features in medical images for melanoma detection, however, challenges remain due to the scarcity of annotated images and the intricacy of cancer images. The need for early skin cancer detection, particularly melanoma, is an urgent concern due to its potential for high mortality when not identified and treated promptly. This paper introduces a comprehensive method for melanoma detection in medical images through the incorporation of data augmentation approaches. We employed a CNN model for the categorization of melanoma images with data augmentation techniques such as random horizontal flips, random cropping, grayscale conversion, Gaussian blur, and random perspective transformations. Experiments demonstrate that the suggested method surpasses the existing peak performance in melanoma identification within medical imaging. The results indicate the potential of data augmentation techniques in alleviating the issue of insufficient medical images and improving melanoma detection. We attained an overall accuracy of 93.43%, a sensitivity of 99.74%, and a specificity of 88.53% in melanoma detection, surpassing state‐of‐the‐art approaches with the HAM10000 dataset. Our model is beneficial in clinical settings to aid dermatologists in precisely identifying patients, facilitating early intervention, and potentially preserving lives. In the future, we intend to test our algorithm on more skin cancer datasets that may enhance the accuracy of melanoma diagnosis. A crucial component of the design is the ablation study, which seeks to discover and improve the most significant model parameters for computational efficiency and diagnostic precision. The HAM10000 dataset is utilized for ablation tests to assess and validate the efficacy of the suggested method.

Low-Dose Ionizing Radiation Exposure and Its Impact on Skin Aging Among Healthcare Workers.

Ionizing radiation (IR) is a occupational risk for healthcare professionals, which can result in a range of health issues. This study aims to assess the relationship between low-dose IR exposure and the development of early aging-related skin wrinkles in healthcare workers. In the current cross-sectional study, 70 health workers exposed to low-dose ionizing radiation for at least five years were compared with 70 non-exposed health workers. Skin wrinkles of the participants were assessed by Lemperle Wrinkle Assessment Scale and statistical analysis comparing wrinkle score between the exposed to control was done. There were no significant differences in sociodemographic factors and Fitzpatrick skin types between the radiation-exposed healthcare workers and the control group. The Lemperle Wrinkle Assessment Scale scores revealed significant differences, with radiation workers showing higher scores in all wrinkle categories, including horizontal forehead lines, glabellar frown lines, periorbital lines, preauricular lines, cheek folds, nasolabial folds, upper and lower lip lines, corner of the mouth lines, marionette lines, and labiomental creases (all p < 0.00001), and for horizontal neck folds (p = 0.00152). This is the first study to investigate health care workers with long-term low-dose IR exposure for skin wrinkles, a visible consequence of skin aging. Chronic low-dose IR exposure can lead to increased skin wrinkles, representative of advanced skin aging in health workers. These findings underscore the need for enhanced protective measures and regular monitoring for individuals exposed to occupational radiation.

Early Excision And Skin Allograft Helps To Control Infection In Major Burn At Burn Unit Cipto Mangunkusumo Hospital

Backgrounds: Burns are systemic complex injuries following an exposure to thermal energy and can cause damage to the skin. Skin, as body cover, will protect the underlying tissue from infection, act as a thermo regulator and control the evaporations. The principle of burn therapy is to remove the necrotic tissue and to replace the function of the skin in the body by using a temporary cover with synthetic dressings or skin allograft. Skin allograft remains an ideal temporary cover, particularly when sufficient autograft skin is not available.Patients and Methods: We performed early excision and cover the wound with cadaver skin allograft in two patients suffered from major burn admitted to Cipto Mangunkusumo Hospital in 2012. We observed the wound clinically and monitored the leukocytes count to evaluate the rate of infection.Results: We observed four patients with major burn. Two patients treated with early excision and skin allograft has decreasing in exudates production, less bad odor in wound area, body temperature stable in normal value and also the decreasing leukocytes counts gradually. The results of wound area and body temperature in two patients treated conventionally are not much different from the initial wound and have high leukocytes count more than 10.000 in the majority of laboratory findings. The skin allograft can control the infection by suppressing the bacterial proliferation, promote epithelialization and prepare the wound better.Summary: Patient in this study treated with early excision and skin allograft has better outcome compared to patients treated conservatively.

Skin as outermost immune organ of vertebrates that elicits robust early immune responses after immunization with glycoprotein of spring viraemia of carp virus.

As the outermost immune organ in vertebrates, the skin serves as the primary interface with the external environment and plays a crucial role in initiating the early immune response. The skin contains a variety of immune cells that induce mucosal and systemic immune responses, rendering it a prime target for vaccination strategies. Insight into the mechanisms through which vaccination triggers early immune responses is paramount for advancing animal and human health, yet our current understanding remains limited. Given its significance in vertebrate evolution, teleost fish emerges as an excellent model for investigating the early immune response of skin. In this study, we demonstrate that significant quantities of vaccine can be absorbed by the skin and transported to the body through dermis and muscle metabolism by immerses immune zebrafish with glycoprotein of spring viraemia of carp virus. Immersion immunization can elicit robust and enduring immune protection, with the skin triggering a potent immune response early in the immunization process. Analysis of the skin transcriptome revealed the involvement of numerous immune-related genes in the immersion immune response, with indications that HSP70 and MAPK signals might play pivotal roles in the immune process induced by glycoprotein. Co-immunoprecipitation and cell co-localization studies confirmed the interaction between glycoprotein and HSP70. Subsequent research demonstrated that overexpression or inhibition of HSP70 could respectively enhance or impede the expression of JNK and related proteins. However, the survival rate and immune response of HSP70 inhibited zebrafish with glycoprotein treatment were significantly reduced. These findings propose that the interaction between glycoprotein and HSP70 may activate JNK, thereby modulating mucosal and systemic immune responses induced by glycoprotein. This investigation offers novel insights and a foundational understanding of early skin immune reactions.

A Review on Ayurvedic Cosmetology and Cosmetic Toxicity

The skin is the fundamental component of one's exterior appearance. Both in the past and the present, external beauty has drawn a great deal of interest and desire. Ayurveda is highly sought after in the cosmetology profession because of its distinct outlook on beauty and its long-lasting, cost-effective, and efficacious beauty therapy. Person's overall health conditions determine their level of healthy skin, which necessitates a variety of skin care treatments throughout life. These treatments - Panchakarma, Varnya Mahakashay, Achara Rasayan, Rasayan therapy, Sadvritta, Dincharya, Rutucharya, and diet control - are advised by Acharyas and are detailed in the Samhita. Ayurveda's cosmetic approach in cosmetology is linked to mental and physical well-being. We purchase skin care products to take better care of our skin and to seem younger. However, due to the components in cosmetics, prolonged use of those items builds up within the body and can have negative effects like irritation, allergic hypersensitivity, and an increased risk of infection. It can also induce early skin aging by causing damage to skin tissues. Ayurvedic cosmetics, which is based on natural principles, meets modern standards. It is possible to successfully combine the concepts of Gara Visha and Dushi Visha with cosmetic toxicity. This article discusses the toxicity of beauty products and Ayurvedic notions related to cosmetology.

Sistem Pakar Identifikasi Masalah Kulit Wajah Menggunakan Metode Case Based Reasoning

Problems that occur with facial skin reduce women's self-confidence, especially in terms of appearance. Several factors influence the condition of women's facial skin, such as pollution, food and drink consumed, sun exposure, genetic factors, and lack of knowledge in using appropriate products. The MCM Beauty Center Clinic has problems, namely a lack of public awareness, namely people's ignorance about the condition of their facial skin, limited access because they only provide offline consultations and require an appointment first. The aim of this research is to build an expert system that can help to identify early facial skin problems in the form of acne, melasma and spots based on a website. This system will be realized in the form of a website by applying the Case Based Reasoning (CBR) method, which is designed to help patients who have limited time to consult directly with beauty experts. The CBR method has a first stage of collecting data in the form of symptoms from cases that have occurred previously based on experts, then processing the calculation data using nearest neighbors and producing a decision as a treatment suggestion solution. The implementation of a website-based expert system using the CBR method has been tested using black box testing and system precision testing. The results of black box testing of the system went as expected while precision testing showed results of 93% with 28 successful data out of a total of 30 data tested. By applying existing technology and knowledge, this expert system is feasible and useful for users in carrying out more accurate, fast and efficient diagnoses.

Enhancing Skin Lesion Classification Performance with the ABC Ensemble Model

Skin cancer is one of the most easily developed cancers and is continuously seeing an increased incidence rate. In this study, we propose a novel ABC ensemble model for skin lesion classification by leveraging the ABCD rule, which is commonly used in dermatology to evaluate lesion features such as asymmetry, border, color, and diameter. Our model consists of five distinct blocks, two of which focus on learning general image characteristics, while the remaining three focus on specialized features related to the ABCD rule. The final classification results are achieved through a weighted soft voting approach, where the generalization blocks are assigned higher weights to optimize performance. Through 15 experiments using various model configurations, we show that the weighted ABC ensemble model outperforms the baseline models, achieving the best performance with an accuracy of 0.9326 and an F1-score of 0.9302. Additionally, Grad-CAM analysis is employed to assess how each block in the ensemble focuses on distinct lesion features, further enhancing the interpretability and reliability of the model. Our findings demonstrate that integrating general image features with specific lesion characteristics improves classification performance, and that adjusting the soft voting weights yields optimal results. This novel model offers a reliable tool for early skin lesion diagnosis.

Skin graft take rate among minor burns – A cohort study to investigate the effect of factors such as burn depth, burn size, and timing of the operation

BackgroundIt has been known for decades that early excision of burns reduces morbidity and mortality. Early surgical excision and skin grafting has become the most important part of successful healing in burn management, especially in major burns. However, it is not entirely clear whether early excision and skin transplantation has the same advantages in smaller burns and there is no consensus on the timing of skin grafting in this group. The aim was to investigate the effect of timing and other factors for skin graft take rate among minor burns. MethodsThis retrospective study included patients with thermal injury, a burn size smaller than 11 % total body surface area (TBSA), and who were treated with a skin graft operation. Take rate at the second dressing change after operation was used as main outcome, a cut-off of 95% take rate was for the multivariable logistic regression. ResultsA total of 195 patients were included, median (IQR) age was 42 (9–68) years, 65 % were male, and median (IQR) area of deep burns was 2 (1–4) % of the body surface area (BSA). Multivariable regression showed that smaller area of deep burns and scalds (compared with flame and contact burns) were associated with a take rate of ≥ 95 %. Age, timing of the skin graft transplantation, and plasma C-reactive protein showed no independent effect on take rate. The regression model was significant but weak (ROC AUC 0.71, 95 % CI 0.62–0.79). ConclusionOur results suggest that the extent and depth of the burn are the most important factors for skin graft take rate among minor burns, while timing of the transplantation is not associated with take rate for the skin graft. The advantageous effect of scalds may be interpreted to mean that scalds in general are more superficial than flame and contact burns, a difference that may not be detected by the use of a Lund and Browder chart. The conclusion is, however, tempered by the retrospective study design and the relatively low discriminatory power in our study.

DSCIMABNet: A novel multi-head attention depthwise separable CNN model for skin cancer detection

Skin cancer is a common type of cancer worldwide. Early diagnosis of skin cancer can reduce the risk of death by increasing treatment success. However, it is challenging for dermatologists or specialists because the symptoms are vague in the early stages and cannot be noticed by the naked eye. This study examines digital diagnostic techniques supported by artificial intelligence, focusing on early skin cancer detection and two methods have been proposed. In the first method, DSCIMABNet deep learning architecture was developed by combining multi-head attention and depthwise separable convolution techniques. This model provides flexibility in learning the dataset's local features, abstract concepts, and long-term relationships. The DSCIMABNet model and modern deep learning models trained on ImageNet are proposed to be combined with the ensemble learning method in the second method. This approach provides a comprehensive feature extraction process that will increase the performance of the classification process with ensemble learning. The proposed approaches are trained and evaluated on the ISIC 2018 dataset with image enhancement applied in preprocessing. In the experimental results, DSCIMABNet achieved 84.28% accuracy, while the proposed hybrid method achieved 99.40% accuracy. Moreover, on the Mendeley dataset (CNN for Melanoma Detection Data), DSCIMABNet achieved 92.58% accuracy, while the hybrid method achieved 99.37% accuracy. This study may significantly contribute to developing new and effective methods for the early diagnosis and treatment of skin cancer.

Apocrine Gland Damage and the Release of Specific Keratins in Early Stage Indicate the Crucial Involvement of Apocrine Glands in Hidradenitis Suppurativa

The apocrine glands (AGs) are not considered to be primarily involved in hidradenitis suppurativa (HS). This study investigated the potential role of AGs in HS pathogenesis using immunohistochemistry and single-cell sequencing of nonlesional skin and early lesional skin (LS) from patients with HS (n= 12) and healthy controls (n= 8). AG cell destruction was more frequent, and AG size was significantly reduced in the nonlesional skin and LS. Barrier-related genes (eg, CLDN1 and CDH1) were downregulated in the AGs of the nonlesional skin and LS. Damaged AGs in the LS primarily recruited and activated neutrophils through the CXCL-CXCR and SAA1-FPR2 pathways. Elevated levels of specific keratins (keratin 18 and keratin 19) released from damaged AGs were observed on the skin surface of patients and were associated with disease severity. Keratin 19 was also detected in the dermis of the nonlesional skin and LS and was surrounded by neutrophils and macrophages. Moreover, serum keratin 19 levels in patients (N= 20) were significantly negatively correlated with the age at HS onset. Collectively, our findings provide previously unreported evidence that the AGs are damaged and release specific keratins in early HS lesions, indicating a crucial role of the AGs in HS pathogenesis.

Disparities in Skin Cancer Outcomes in the Latine/Hispanic Population.

Despite having an overall lower lifetime risk for skin cancer, Latine/Hispanic individuals experience increased morbidity and mortality in skin cancer outcomes compared to non-Hispanic White individuals. The reasons for these disparate outcomes are multifactorial, but challenges in early skin cancer detection, limited awareness of risks, and inequitable access to care and/or treatment among this patient population likely are contributory. In this article, we review cutaneous malignancies in the Latine/Hispanic population and explore factors that impact overall prognosis, including unique clinical features, inadequate health coverage, medical mistrust, language barriers, differing cultural perspectives, and inadequate research. We aim to provide a pivotal foundation for development of effective strategies to advance skin cancer education and awareness and promote equity in skin cancer treatment among this population.

Quantitative estimation of optical properties in bilayer media within the subdiffusive regime using tilted fiber-optic probe diffuse reflectance spectroscopy, part 2: probe design, realization, and experimental validation.

Tissues like skin have a layered structure where each layer's optical properties vary significantly. However, traditional diffuse reflectance spectroscopy assumes a homogeneous medium, often leading to estimations that reflects the properties of neither layer. There's a clear need for probes that can precisely measure the optical properties of layered tissues. This paper aims to design a diffuse reflectance probe capable of accurately estimating the optical properties of bilayer tissues in the subdiffusive regime. Using Monte Carlo simulations, we evaluated key geometric factors-fiber placement, tilt angle, diameter, and numerical aperture-on optical property estimation, following the methodology in Part I. A robust design is proposed that balances accurate intrinsic optical property (IOP) calculations with practical experimental constraints. The designed probe, featuring eight illumination and eight detection fibers with varying spacings and tilt angles. The estimation error of the IOP calculation for bilayer phantoms is less than 20% for top layers with thicknesses between 0.2 and 1.0mm. Building on the approach from Part I and using a precise calibration, the probe effectively quantified and distinguished the IOPs of bilayer samples, particularly those relevant to early skin pathology detection and characterization.

MUCM-Net: a Mamba powered UCM-Net for skin lesion segmentation

Aim: Skin lesion segmentation is critical for early skin cancer detection. Challenges in automatic segmentation from dermoscopic images include variations in color, texture, and artifacts of indistinct lesion boundaries. This study aims to develop and evaluate MUCM-Net, a lightweight and efficient model for skin lesion segmentation, leveraging Mamba state-space models integrated with UCM-Net architecture optimized for mobile deployment and early skin cancer detection. Methods: MUCM-Net combines Convolutional Neural Networks (CNNs), multi-layer perceptions (MLPs), and Mamba elements into a hybrid feature learning module. Results: The model was trained and tested on the International Skin Imaging Collaboration (ISIC) 2017 and ISIC2018 datasets, consisting of 2,000 and 2,594 dermoscopic images, respectively. Critical metrics for evaluation included Dice Similarity Coefficient (DSC), sensitivity (SE), specificity (SP), and accuracy (ACC). The model’s computational efficiency was also assessed by measuring Giga Floating-point Operations Per Second (GFLOPS) and the number of parameters. MUCM-Net demonstrated superior performance in skin lesion segmentation with an average DSC of 0.91 on the ISIC2017 dataset and 0.89 on the ISIC2018 dataset, outperforming existing models. It achieved high SE (0.93), SP (0.95), and ACC (0.92) with low computational demands (0.055–0.064 GFLOPS). Conclusions: The model’s innovative Mamba-UCM layer significantly enhanced feature learning while maintaining efficiency that is suitable for mobile devices. MUCM-Net establishes a new standard in lightweight skin lesion segmentation, balancing exceptional ACC with efficient computational performance. Its ability to perform well on mobile devices makes it a scalable tool for early skin cancer detection in resource-limited settings. The open-source availability of MUCM-Net supports further research and collaboration, promoting advances in mobile health diagnostics and the fight against skin cancer. MUCM-Net source code will be posted on https://github.com/chunyuyuan/MUCM-Net.

Cytological Characteristics of the Eye Conjunctiva in Dogs with Atopic Dermatitis

Atopic dermatitis is a chronic, pruritic inflammatory skin disease affecting approximately 10% of dogs worldwide. The disease typically manifests at an early age, causing pruritus and secondary skin lesions in specific anatomical regions. The pathophysiology of canine atopic dermatitis (CAD) remains incompletely understood, but recent research suggests a complex interplay between skin barrier dysfunction, allergic sensitisation, and microbial dysbiosis. Despite the well-established association between atopic dermatitis and conjunctivitis, the prevalence of conjunctivitis in dogs with CAD is often underestimated in clinical practice due to inadequate recognition of symptoms and a lack of specific diagnostic protocols. This study aimed to analyse the cytological characteristics of the conjunctiva in dogs affected by atopic dermatitis. Samples were collected from 24 dogs diagnosed with CAD and treated at the University Veterinary Hospital, Faculty of Veterinary Medicine, University of Zagreb. Cytological analysis was performed on samples obtained using a cytobrush. The results revealed the presence of inflammatory cells in all samples, with lymphocytes most prevalent (87.5%). A mild degree of conjunctival metaplasia was observed in all dogs, regardless of the type of inflammatory cells and the severity of conjunctivitis. These findings suggest that atopic dermatitis may induce changes in the conjunctiva that are not necessarily correlated with the intensity of the inflammatory response. The presence of lymphocytes, along with eosinophils in some cases, underscores the diagnostic value of conjunctival cytology in dogs with CAD. Furthermore, the increased density of goblet cells suggests similarities in the pathophysiology of ocular allergies in dogs and humans, opening avenues for further research to better understand and treat these conditions. This study highlights the importance of an interdisciplinary approach in the diagnosis and treatment of atopic dermatitis, including regular ophthalmologic examinations as part of standard clinical practice.

Real-World Evaluation of the Effectiveness and Safety of Dupilumab in Bullous Pemphigoid: An Ambispective Multicenter Case Series.

Bullous pemphigoid affects elderly individuals with multiple comorbidities, making conventional treatments unsuitable. Evaluate the effectiveness and safety of dupilumab in the treatment of bullous pemphigoid. A multicenter ambispective cohort study was conducted in 34 hospitals. Patients with bullous pemphigoid treated with Dupilumab were included. Most of patients (97.1%) received an initial 600 mg dose followed by 300 mg every two weeks. The primary outcome was the proportion of patients achieving complete remission within 4 weeks, defined as Investigator Global Assessment score of 0 or 1. Complete remission at weeks 16, 24, and 52, adverse events, reductions in peak pruritus numerical rating scale, and systemic glucocorticoid use were also assessed. The study included 103 patients with a median age of 77.3 years, 58.0% male. Complete remission was achieved by 53.4% within 4 weeks and 95.7% by week 52. Peak pruritus scale reduced by 70.0% by week 4 and was completely controlled by week 24. Thirteen patients presented adverse events, most of which were mild. Systemic glucocorticoid use reduced by 82.1% by week 52. Shorter disease duration and exclusive cutaneous involvement predicted better response at 16 weeks. No differences in response rates to dupilumab were observed between drug-associated bullous pemphigoid and idiopathic cases. No significant difference in response rates was observed between patients treated with dupilumab in monotherapy and those receiving dupilumab with concomitant treatments. Dupilumab is effective, rapid, and safe in managing bullous pemphigoid, reducing the need for corticosteroids and other treatments. Early initiation and exclusive skin involvement predict better outcomes.

Prospective observational study on scar sequelae after MPOX infection: an analysis of 40 patients.

Monkeypox (MPOX) caused a public health emergency of international concern (PHEIC) outbreakbetween 2022 and 2023, with a recent rise in cases that prompted the World Health Organization (WHO) to declare the disease a PHEIC once again. There is little information on its long-term scarring sequelae. The objective of this study was to assess the risk and characteristics of scarring in patients with MPOX in a tertiary hospital. This is a prospective cohort study including patients diagnosed using polymerase chain reaction (PCR) tests. Clinical data were collected and followed up at 12-15 months to assess scarring and its impact on quality of life. Of the 40 patients, 19 (47.5%) developed scars, which were more common in those with initial cutaneous manifestations. Scars significantly affected the quality of life, especially in the genital and mucosal areas. The limited sample and loss to follow-up may affect the validity of the results. Scarring is a frequent and disfiguring sequela of MPOX, particularly in patients with early skin symptoms. Prevention and close follow-up are crucial in mitigating these complications.

اكتشاف سرطان الجلد الميلانيني بناءً على الذكاء الاصطناعي - دراسة استقصائية

In recent decades, melanoma, a lethal form of skin cancer, has become more common internationally. Due to the recovery strategy used in the medical field, dermoscopic image-based automatic skin lesion detection remains a challenging and complicated task. This difficulty in diagnosing lesions can be attributed to various factors, such as the lesions revealing diverse characteristics, including uncertain borders, inadequate color assessment, shape variations, positional dependence, and complex structures. To prevent the growing public health burden from spreading to other body organs and potentially save many lives, early detection and appropriate treatment are essential among medical professionals and researchers. An individual may develop melanoma if there is an abnormal change in the appearance of the skin. To achieve more effective cancer detection, dermatology expertise should be integrated with computer vision strategies. Consequently, it is crucial to create a variety of detection strategies to aid medical experts in diagnosing early-stage cancer. This paper provides a thorough, methodical analysis of machine learning techniques for early skin cancer detection, examining published studies. It offers an overview of an artificial intelligence-assisted evaluation approach for diagnosing skin cancer. To address existing challenges in skin cancer detection, this paper proposes a model integrating improved techniques across preprocessing, feature extraction, and classification. The proposed model utilizes adaptive histogram equalization to enhance image clarity, combined with feature extraction methods such as the ABCD rule, GLCM, and HOG, alongside deep learning-based autoencoders. For classification, it employs an ensemble approach that integrates CNNs and SVMs, leveraging transfer learning from pre-trained models and implementing a multi-class framework. This model aims to enhance detection accuracy, reduce costs, and improve usability. The review aims to provide researchers with the latest advancements in machine learning for cancer diagnostics, offering a comprehensive understanding of current and emerging techniques. Keywords: Skin Cancer, Melanoma Detection, Artificial Intelligence, Machine Learning, Deep Learning ،Neural Network Melanin.