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.

Introduction: It is claimed that the coronavirus disease 2019 (COVID-19) pandemic has had a negative impact on mental health. However, to date, prospective studies are lacking. Moreover, it is important to identify which factors modulate the stress response to the pandemic. Previously, sense of coherence (SOC) has emerged as a particularly important resistance factor. Objective: This prospective study aimed to assess the impact of the COVID-19 outbreak on mental health and to investigate the ability of pre-outbreak SOC levels to predict changes in psychopathological symptoms. Methods: This study assessed psychopathological symptoms and SOC before and after the COVID-19 outbreak as well as postoutbreak COVID-19-related traumatic distress in a Germanspeaking sample (n = 1,591). Bivariate latent change score (BLCS) modeling was used to analyze pre-to post-outbreak changes in psychopathological symptoms and the ability of SOC to predict symptom changes. Results: Overall, there was no change in psychopathological symptoms. However, on an individual-respondent level, 10% experienced a clinically significant increase in psychopathological symptoms and 15% met cutoff criteria for COVID-19-related traumatic distress. Using BLCS modeling, we identified a high-stress group experiencing an increase in psychopathological symptoms and a decrease in SOC and a low-stress group showing the reversed pattern. Changes in SOC and psychopathological symptoms were predicted by pre-outbreak SOC and psychopathological symptom levels. Conclusions: Although mental health was stable in most respondents, a small group of respondents characterized by low levels of SOC experienced increased psychopathological symptoms from pre-to post-outbreak. Thus, SOC training might be a promising approach to enhance the resistance to stressors.

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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