Melasma is a chronic hyperpigmentation disorder with increasing global prevalence, particularly affecting women and individuals with Fitzpatrick skin types III-V. While curative treatments remain elusive, advancements in diagnostic tools like dermoscopy allow for more precise, customized care. This presentation discusses the integration of dermoscopy in melasma management to tailor treatments based on the pigmentation and vascular components. By addressing the multiple pathophysiological factors of melasma—such as UV exposure, hormonal influences, and considering individual factors such as underlying skin sensitivity — personalized treatments using a combination of topical agents, oral medications, and procedural therapies can significantly improve patient outcomes. This approach not only enhances therapeutic efficacy but also positively impacts patients' quality of life.<br/><br/>Keywords: melasma, dermoscopy, customized care

Dermoscopy is a non-invasive imaging technique that enhances the visualization of skin lesions. It has traditionally been employed for diagnosing suspicious skin conditions, particularly in distinguishing between benign and malignant melanocytic lesions. Moreover, its applications have expanded significantly into both procedural and aesthetic dermatology.<br/><br/>Dermoscopy offers diagnostic accuracy, enhanced patient communication, monitoring treatment outcomes. If applying in aesthetic procedures, it assists in planning procedures by providing detailed insights into the skin's condition, though challenges and limitations do exist.<br/><br/>Overall, dermoscopy serves as a friend in both procedural and aesthetic dermatology by enhancing the aforementioned benefits. However, its effectiveness is contingent upon proper training and integration into clinical practice. If misuse or over-reliance could lead to adverse outcomes. Thus, while dermoscopy is largely advantageous, maintaining a balanced approach is essential for optimal patient services.

Introduction: Various types of skin disorders may affect the female external genital region. Differential diagnosis includes a wide spectrum of neoplastic, inflammatory, and infectious diseases. Most of these disorders can be diagnosed based on the clinical features, medical history, but in most cases further examinations are needed to be done. Dermoscopy may be useful into differentiating these disorders and helps a lot to avoid biopsies or to orientate precisely where to do it. <br/>We aim to provide clinical cases for each disorder affecting the vulva, describe the dermoscopy features and review the available literature. <br/>Material and Method: Different clinical cases diagnosed with dermoscopy. Review of the literture refering dermoscopic features of different diseases of the vulva. <br/>Conclusion:Deep knowledges in the use of dermoscopy is becoming a must for dermatologists.<br/>Key Words: Vulvar Diseases, dermoscopy, melanoma, nevus, psoriasis.

Skin cancer, specifically melanoma has increased rapidly in recent years. Melanoma is a malignancy derived from melanocytes that has undergone a malignant transformation. According to data from Global Cancer Observatory (GLOBOCAN) 2024, the number of melanoma reaches 331.722 cases and 58.667 deaths worldwide. Melanoma poses a significant public health challenge across the globe with incidence rates that are rising faster than the rate of other malignancies, but with early detection, a good prognosis from this cancer can be achieved. The 5-year relative survival rate for patients with stage 0 melanoma is 97%, compared to 30% for those with stage 4 disease. National Cancer Institute (NCI) surveillance, epidemiology, and end results (SEER) program reports melanoma is currently the fifth most common malignancy in both men and women. The occurrence of the disease in the overall population is 44,1% in Europe, 34% in Northern America, and 7,5% in Asia.<br/>Indonesia is the largest archipelago in the world consisting of 280 million people with diverse skin colors. The most common skin cancers that are found in Indonesia are basal cell carcinoma (65,5%), followed by squamous cell carcinoma (23%), and melanoma (7,5%). The incidence of melanoma in Indonesia ranks 22nd among other cancers, with 1.716 cases and 774 deaths according to data from GLOBOCAN 2022. An epidemiologic study done by Wibawa et al. the incidence of melanoma in a referral center hospital in Indonesia, dr. Cipto Mangunkusumo Hospital (RSCM) Jakarta in 2014 – 2017 is 5,7%. <br/> Melanoma can be identified by a visual exam with inexpensive and non-invasive tools that can be done in primary healthcare. Data stated by the Indonesian Medical Council (KKI) in 2024, Indonesia has 178.927 registered general practitioners nationwide. In the clinical area, it is well documented that experience increases diagnostic accuracy. In line with this observation, general practitioners act as front liners in primary health care in which early skin cancer detection accuracy is suboptimal. The professional dermatovenereologist organization, Indonesian Society of Dermatology and Venereology (PERDOSKI), has 2926 members. Concentrated in the more populated and urban areas, such as Jakarta (21%) and Surabaya (10%), showed a disparity between urban and rural areas, such as Palu Central Sulawesi (0,4%) and Jayapura (0,44%), due to geographical and demographic problems. This centralization serves as a challenge in detecting melanoma. The distribution of specialized doctors in rural areas limits access for patients to be treated accordingly. Whilst in primary care, most general practitioners are untrained to interpret lesion in a standardized fashion to accurately determine early melanoma skin lesions as it leads to a late diagnosis or even undiagnosed. Therefore, a need for a tool to aid accuracy in early detection is needed to minimize late intervention or further invasive diagnostic procedures such as biopsies. <br/>In the recent years, innovations in artificial intelligence (AI)-based techniques for skin lesion image interpretation shows potential for the use of AI in the early detection of melanoma. Studies have demonstrated that the use of AI in medical image recognition has grown tremendously and found that machine learning algorithms have the potential to minimize the number of artifacts that need to be reviewed and speed up the diagnostic process. Deep Learning, a type of machine learning, has the potential to make better clinical decisions. Convolutional Neural Network (CNN) is a class from Deep Learning that is commonly used to analyze image datas and it has the ability to learn the characteristics of the data provided using layers and training. Several studies have been conducted using various image databases, consistently reporting levels of diagnostic accuracy comparable to those achieved by experienced dermatologists. Deep Learning models depend on some factors to achieve accuracy in detecting melanoma and other skin cancers including datasets, good quality of clinical images, and varieties in accordance to age, skin tone, and ethnicity. It is also crucial to compare the diagnostic accuracy of different models trained on diverse datasets to assess their potential for clinical use accurately. Performance metrics, such as the area under the curve (AUC), can comprehensively evaluate the effectiveness of machine learning in detecting skin cancers such as melanoma. The analysis of this metric provides identification of AI-based machine learning capability as a diagnostic decision support tool to detect melanoma and improvement for future research.

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