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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This study explores differences in the out-of-home behavior of community-dwelling older adults with different cognitive impairment. Three levels of complexity of out-of-home behavior were distinguished: (a) mostly automatized walking behavior (low complexity), (b) global out-of-home mobility (medium complexity), and (c) defined units of concrete out-of-home activities, particularly cognitively demanding activities (high complexity). A sample of 257 older adults aged 59 to 91 years (M = 72.9 years, SD = 6.4 years) included 35 persons with early-stage Alzheimer's disease (AD), 76 persons with mild cognitive impairment (MCI), and 146 cognitively healthy persons (CH). Mobility data were gathered by using a GPS tracking device as well as by questionnaire. Predicting cognitive impairment status by out-of-home behavior and a range of confounders by means of multinomial logistic regression revealed that only cognitively demanding activities showed at least a marginally significant difference between MCI and CH and were highly significant between AD and CH.

Background: One of the more common behavioral manifestations of dementia-related disorders is severe problems with out-of-home mobility. Various efforts have been attempted to attain a better understanding of mobility behavior, but most studies are based on institutionalized patients and the assessment usually relies on reports of caregivers and institutional staff, using observational approaches, activity monitoring, or behavioral checklists. The current manuscript describes the research protocol of a project that measures mobility in Alzheimer's disease and related cognitive disorders in an innovative way, by taking advantage of advanced tracking technologies.