Influenza B-induced longitudinally extensive transverse myelitis and bithalamic acute disseminated encephalomyelitis.

In the COVID-era, other viral pathogens, like influenza B, gain less attention in scientific reporting. However, influenza still is endemic, and rarely affects central nervous system (CNS). Here, we report the case of a 35-year-old male who presented with fever since 1 week, and developed acute ascending flaccid paralysis and urinary retention. The clinical presentation of paraparesis in combination with the inflammation proven by the lumbar puncture, and the MRI full spine, fulfilled the diagnostic criteria of longitudinally extensive transverse myelitis (LETM). In this case, it is most likely based on a post-viral Influenza type B. Additionally, the brain MRI showed a necrotizing encephalopathy bilaterally in the thalamus. Both locations of inflammatory disease were part of one auto-immune-mediated, monophasic CNS disorder: influenza-induced ADEM which is very unique, fortunately with favorable outcome.

Unveiling the power of convolutional neural networks in melanoma diagnosis.

Convolutional neural networks are a type of deep learning algorithm. They are mostly applied in visual recognition and can be used for the identification of melanomas. Multiple studies have evaluated the performance of convolutional neural networks, and most algorithms match or even surpass the accuracy of dermatologists. However, only 23.8% of dermatologists have good or excellent knowledge of the topic. We believe that the lack of knowledge physicians experience regarding artificial intelligence is an obstacle to its clinical implementation. We describe how a convolutional neural network differentiates a benign from a malignant lesion. We systematically searched the Web of Science, Medline (PubMed), and The Cochrane Library on the 9th February, 2022. We focused on articles describing the role and use of artificial intelligence in melanoma recognition between 2017 and 2022, using the following MeSH terms: "melanoma," "diagnosis," and "artificial intelligence". Traditional machine learning algorithms comprise different parts which must preprocess, segment, extract features and classify the lesion into benign or malignant. Deep learning algorithms can perform these steps simultaneously, which significantly enhances efficiency. Convolutional neural networks include a convolutional layer, a pooling layer, and a fully connected layer. Convolutional and pooling layers extract features from the lesion and reduce computational power, whereas fully connected layers classify the image into two or more categories. Additionally, we suggest that further studies should be performed to accelerate the clinical implementation of artificial intelligence, to create comprehensive datasets and to generate explainable algorithms.