UV Irradiation of Nevi: Impact on Performance of Electrical Impedance Spectroscopy and a Convolution Neural Network.

Introduction: UV irradiation of nevi induces transient melanocytic activation with dermoscopic and histological changes.

Objectives: We investigated whether UV irradiation of nevi may influence electrical impedance spectroscopy (EIS) or convolution neural networks (CNN).

Methods: Prospective, controlled trial in 50 patients undergoing phototherapy (selective UV phototherapy (SUP), UVA1, SUP/UVA1, or PUVA).

Skin lesions of face and scalp - Classification by a market-approved convolutional neural network in comparison with 64 dermatologists.

Background: The clinical differentiation of face and scalp lesions (FSLs) is challenging even for trained dermatologists. Studies comparing the diagnostic performance of a convolutional neural network (CNN) with dermatologists in FSL are lacking.

Methods: A market-approved CNN (Moleanalyzer-Pro, FotoFinder Systems) was used for binary classifications of 100 dermoscopic images of FSL.

Early detection of cutaneous melanoma by sequential digital dermatoscopy (SDD).

The early diagnosis and excision of cutaneous melanoma is essential for an improved prognosis of the disease. Besides the investigation of pigmented lesions with the unaided eye and conventional dermatoscopy, long-term sequential digital dermatoscopy has been shown to improve the sensitivity of melanoma detection, especially in high-risk patients. In addition to the static clinical and dermatoscopic assessment, the sequential digital dermatoscopy strategy helps to detect changes over time.