Background: Recent studies have successfully demonstrated the use of deep-learning algorithms for dermatologist-level classification of suspicious lesions by the use of excessive proprietary image databases and limited numbers of dermatologists. For the first time, the performance of a deep-learning algorithm trained by open-source images exclusively is compared to a large number of dermatologists covering all levels within the clinical hierarchy. Methods: We used methods from enhanced deep learning to train a convolutional neural network (CNN) with 12,378 open-source dermoscopic images. We used 100 images to compare the performance of the CNN to that of the 157 dermatologists from 12 university hospitals in Germany.

Background: Recent studies have demonstrated the use of convolutional neural networks (CNNs) to classify images of melanoma with accuracies comparable to those achieved by board-certified dermatologists. However, the performance of a CNN exclusively trained with dermoscopic images in a clinical image classification task in direct competition with a large number of dermatologists has not been measured to date. This study compares the performance of a convolutional neuronal network trained with dermoscopic images exclusively for identifying melanoma in clinical photographs with the manual grading of the same images by dermatologists.

Background: In recent studies, convolutional neural networks (CNNs) outperformed dermatologists in distinguishing dermoscopic images of melanoma and nevi. In these studies, dermatologists and artificial intelligence were considered as opponents. However, the combination of classifiers frequently yields superior results, both in machine learning and among humans. In this study, we investigated the potential benefit of combining human and artificial intelligence for skin cancer classification. Methods: Using 11,444 dermoscopic images, which were divided into five diagnostic categories, novel deep learning techniques were used to train a single CNN. Then, both 112 dermatologists of 13 German university hospitals and the trained CNN independently classified a set of 300

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SummaryPyoderma gangrenosum (PG) is an orphan disease. While research on such disorders is based on only few randomized multicenter as well as retrospective studies, most of the data comes from case series of small patient groups. Apart from topical and intralesional therapeutic options for early stages and mild disease courses, treatment predominantly involves systemic therapeutic agents. Besides systemic corticosteroids and cyclosporine A (CsA), options also include intravenous immunoglobulins (IVIG) and biologics such as the TNF α inhibitors infliximab, adalimumab, and etanercept; the interleukin (IL) 12/23 antibody ustekinumab; the IL-1 receptor antagonist anakinra; and the IL-1 β antibody canakinumab. The best evidence-based study data is available for CsA, prednisolone, and infliximab; the latter especially in patients with concomitant ulcerative colitis or Crohn's disease. A response to IVIG and canakinumab has been reported in smaller case series. First described by Brocq almost 100 years ago, it was soon recognized that PG did in fact require treatment. To this day, however, such treatment remains a clinical challenge. Despite the severe -albeit rare -clinical picture, improvement in therapeutic options may be expected in the future, primarily due to further clinical studies -especially with a greater number of patients, a better understanding of the etiopathogenesis, as well as the use of modern targeted therapies with higher efficacy and a lower rate of side effects than conventional immunosuppressants such as prednisolone and CsA.