Special areas of involvement in psoriasis include the scalp region, the palms and soles, genital areas, as well as intertriginous sites. The involvement of these topographical regions is associated with important physical and emotional implications, resulting in reduced quality of life, social isolation, and work disability. Palms and soles can be affected as part of the generalized form of psoriasis or can be exclusively affected as palmo-plantar psoriasis. Nail involvement may be encountered in 10–55% of patients with psoriasis, while scalp involvement occurs in 45–56% of individuals with psoriasis. Genital involvement may be the only manifestation of cutaneous psoriasis in 2–5% of patients. Inverse or intertriginous psoriasis represents a special variant of psoriasis as it may mimic and be difficult to differentiate from other dermatological entities that involve the intertriginous skin, such as bacterial or fungal infections, eczema, or lichen planus. Treatment of psoriasis in special areas is challenging due to the facts that special areas are more resistant to standard therapies and are more sensitive to potent local treatments. Biological therapies, proven to be more efficient than standard therapies, are not widely available in the absence of extensive skin involvement. This manuscript aims to provide an up-to-date literature review on psoriasis in special areas, benefiting the everyday clinical practice of physicians in optimizing the evaluation and treatment of their patients.

Background: Despite recent advancements, medical technology has not yet reached its peak. Precision medicine is growing rapidly, thanks to machine learning breakthroughs powered by increased computational capabilities. This article explores a deep-learning application for computer-aided diagnosis in dermatology. Methods: Using a custom model based on EfficientNetB3 and deep learning, we propose an approach for skin lesion classification that offers superior results with smaller, cheaper, and faster inference times compared to other models. The skin images dataset used for this research includes 8,222 files selected from the authors' collection and the ISIC2019 archive, covering six dermatological conditions. Results: The model achieved 95.4% validation accuracy on four categories—melanoma, basal cell carcinoma, benign keratosis-like lesions, and melanocytic nevi—using an average of 1,600 images per category. Adding two categories with fewer images (about 700 each)—squamous cell carcinoma and actinic keratoses—reduced the validation accuracy to 88.8%. The model maintained accuracy on new clinical test images taken under the same conditions as the training dataset. Conclusions: The custom model demonstrated excellent performance on the diverse skin lesions dataset, with significant potential for further enhancements.