Background: In developed countries, health care delivery in dermatology is hampered by the low availability of dermatologists.Objective: To analyze the feasibility of a teledermatology-based action plan to provide initial dermatologic care in areas with low availability of dermatologists.Methods: A cross-sectional study describing the feasibility and cost of a 12-month action plan based on a store-and-forward teledermatology (TD) connecting primary care centers and a TD center. Teleconsultations from patients complaining of any cutaneous condition were included. The primary outcome measure was the percentage of patients not referred to the local dermatologist.Results: Among the total of 15,523 teleconsultations attended in the TD-based action plan, 3360 (21.65%) required a face-to-face visit with a local dermatologist. In 32.32% (n = 5017) of the cases, a therapeutic and follow-up plan report was issued. The most common conditions managed were melanocytic nevi (15.63%, n = 2426), followed by seborrheic keratosis (14.89%, n = 2312), and actinic keratosis (8.65%, n = 1342). The average response time was 14.52 days (95% CI 14. 35-15.23

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Background and Objective. Skin cancer is the most common cancer worldwide. One of the most common non-melanoma tumors is basal cell carcinoma (BCC), which accounts for 75% of all skin cancers. There are many benign lesions that can be confused with these types of cancers, leading to unnecessary biopsies. In this paper, a new method to identify the different BCC dermoscopic patterns present in a skin lesion is presented. In addition, this information is applied to classify skin lesions into BCC and non-BCC. Methods. The proposed method combines the information provided by the original dermoscopic image, introduced in a convolutional neural network (CNN), with deep and handcrafted features extracted from color and texture analysis of the image. This color analysis is performed by transforming the image into a uniform color space and into a color appearance model. To demonstrate the validity of the method, a comparison between the classification obtained employing exclusively a CNN with the original image as input and the classification with additional color and texture features is presented. Furthermore, an exhaustive comparison of classification employing different color and texture measures derived from different color spaces is presented. Results. Results show that the classifier with additional color and texture features outperforms a CNN whose input is only the original image. Another important achievement is that a new color cooccurrence matrix, proposed in this paper, improves the results obtained with other texture measures. Finally, sensitivity of 0.99, specificity of 0.94 and accuracy of 0.97 are achieved when lesions are classified into BCC or non-BCC. Conclusions. To the best of our knowledge, this is the first time that a methodology to detect all the possible patterns that can be present in a BCC lesion is proposed. This detection leads to a clinically explainable classification into BCC and non-BCC lesions. In this sense, the classification of the proposed tool is based on the detection of the dermoscopic features that dermatologists employ for their diagnosis.

Hidradenitis suppurativa (HS) is a chronic autoinflammatory disease of the pilosebaceous follicle with a multifactorial and poorly understood etiopathogenesis. 1 HS entails the development of painful nodules, abscesses, fistulous tracts, and scarring, mainly in the intertriginous areas, which give rise to an unpleasant smelling purulent drainage. HS has been shown to have a significant impact on patient quality of life, 2 and its management requires an individualized and multidisciplinary approach. 3 Although various biologic drugs have emerged to treat HS, antibiotics remain the first-line therapy for patients with HS. 4 Recent