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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Among SNOT-20 scores the PNS subscore is related to rhinoscopically detected polyposis and sinonasal secretion. Therefore, we recommend including short questions regarding PNS into CF-routine care. At the same time our results show that a high inspiratory airflow is not associated with a good sensation of nasal patency. Altogether, rhinomanometry is not required within routine CF-care, but it can be interesting as an outcome parameter within clinical trials. Pediatr Pulmonol. 2017;52:167-174. © 2016 Wiley Periodicals, Inc.

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