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Onychomycosis is common. Diagnosis can be confirmed by various methods; a commonly used method is the histological examination of nail clippings. A deep learning system was developed and its diagnostic accuracy compared with that of human experts. A dataset with annotations for fungal elements was used to train an artificial intelligence (AI) model. In a second dataset ( n =199) the diagnostic accuracy of the AI was compared with that of dermatopathologists. The results show a non-inferiority of the deep learning system to that of analogue diagnosis (non-inferiority margin 5%) with respect to specificity and the area under the receiver operating characteristic curve (AUC). The AI achieved an AUC of 0.981. One limitation of this system is the need for a large number of training images. The AI had difficulty recognizing spores and confused serum or aggregated bacteria with fungal elements. Use of this deep learning system in dermatopathology routine might help to diagnose onychomycosis more efficiently.

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Unsupervised learning represents an important opportunity for obtaining useful speech representations. Recently, variational autoencoders (VAEs) have been shown to extract useful representations in an unsupervised manner. These models are usually not designed to explicitly disentangle specific sources of information. When processing data of sequential nature which involves multi-timescale information, disentanglement can however be beneficial. In this paper we address this issue by developing a predictive auxiliary variational autoencoder to obtain speech representations at different timescales. We will present an auxiliary lower bound which is used to develop a model that we call the Predictive Aux-VAE. The model is designed to disentangle global from local information into a dedicated auxiliary variable. Learned representations are analysed with respect to their ability to capture global speech characteristics. We observe that representations of individual speakers are separated well in the latent space and can successfully be used in a subsequent speaker identification task where they achieve high classification accuracy, comparable to a fully supervised model. Moreover, manipulating the global variable allows to change global characteristics while retaining the local content during generation which demonstrates the success of our model to disentangle global from local information.