This study presents an innovative application of artificial intelligence (AI) in distinguishing dermoscopy images depicting individuals with benign and malignant skin lesions. Leveraging the collaborative capabilities of Google\'s platform, the developed model exhibits remarkable efficiency in achieving accurate diagnoses. The model underwent training for a mere one hour and 33 minutes, utilizing Google\'s servers to render the process both cost-free and carbon-neutral. Utilizing a dataset representative of both benign and malignant cases, the AI model demonstrated commendable performance metrics. Notably, the model achieved an overall accuracy, precision, recall (sensitivity), specificity, and F1 score of 92%. These metrics underscore the model\'s proficiency in distinguishing between benign and malignant skin lesions. The use of Google\'s Collaboration platform not only expedited the training process but also exemplified a cost-effective and environmentally sustainable approach. While these findings highlight the potential of AI in dermatopathology, it is crucial to recognize the inherent limitations, including dataset representativity and variations in real-world clinical scenarios. This study contributes to the evolving landscape of AI applications in dermatologic diagnostics, showcasing a promising tool for accurate lesion classification. Further research and validation studies are recommended to enhance the model\'s robustness and facilitate its integration into clinical practice.

Melanomas arise de novo or in the context of a precursor lesion. Lesions typically grow radially and then undergo a vertical growth phase proceeding to local invasion and metastasis. This review describes the utility of different imaging modalities in diagnosis and melanocytic lesion monitoring. A literature search was performed in November 2023 utilizing EMBASE, Medline, and PubMed. The PRISMA diagram demonstrates the review process. Reflectance confocal microscopy (RCM) utilizes near-infrared light to help diagnose dermatologic lesions. RCM was found to demonstrate nearly two times the positive predictive value compared to dermoscopy. The introduction of the Berlin Ultrasound (US) Morphology Criteria permitted a 65-80% improvement in diagnostic sensitivity. US with fine-needle aspiration cytology (FNAC) accurately predicts the necessity for sentinel lymph node biopsy and lymphadenectomy, sparing patients with metastasis and prompting biopsy for equivocal lesions. Single-photon emission computed tomography/computed tomography (SPECT/CT) is an adjunctive tool to anatomically and functionally assess lymphatic invasion. SPECT/CT improves the detection of sentinel nodes while decreasing operating time and improving cosmetic outcomes. 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) with small voxel reconstruction demonstrated increased specificity and sensitivity for detecting in-transit metastases of melanomas, specifically in the limbs. Dermoscopy allows providers to cost-effectively recognize common lesion patterns. Multiphoton microscopy assigns a weight-based score based on malignant features. Optical coherence angiography captures images of vessels to help diagnose equivocal lesions. Utilization of imaging techniques may increase diagnostic accuracy, reduce unnecessary procedures, and help guide treatment plans. Additional research is needed to further characterize the utility of these techniques in order to improve the diagnosis and treatment of melanomas.

OBJECTIVE: To date, the degree of skin damage caused by diverse factors, such as aging and persistent sunlight exposure, has been evaluated based on the personal experience and knowledge of dermatologists because there is no standard method for objective evaluation. If a standard method were available, patients could obtain more consistent information about their skin condition, and hence perform more effective treatment of the skin damage. In this paper, we demonstrate how to establish a standard method using dermoscopy images of subjects of various ages. We focus on three body parts, specifically the face, neck, and hands, and extract various skin texture features to quantitatively and objectively represent the skin condition.
METHODS: We construct a model for skin damage evaluation based on various skin texture features. To accomplish this objective, we consider various features from face, neck, and hand dermoscopy images, including texture length, width and depth, cell area, the number of cells in a fixed region, radius ratio of inscribed and circumscribed circles of a wrinkle cell, and average perimeter of a wrinkle cell. In this study, a wrinkle cell represents the smallest skin region enclosed by textures. We then perform a linear regression for texture features based on subject age.
RESULTS: A dermoscopy image can be automatically analyzed by extracting skin texture features. We demonstrate aging trends by performing linear regression on these features. Based on this result, a quantitative and objective evaluation of the skin condition can be provided.
CONCLUSIONS: We proposed several new skin texture features and developed algorithms to accurately extract them. We analyzed these features and demonstrated their age-related change trends by using graphs and charts. We believe that our result can be used as a standard method for evaluating degrees of skin damage. Moreover, we believe that our proposed method can be applied in various areas, such as performance evaluation of certain skin products.