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Within the literature concerning modern machine learning techniques applied to the medical field, there is a growing interest in the application of these technologies to the nephrological area, especially regarding the study of renal pathologies, because they are very common and widespread in our society, afflicting a high percentage of the population and leading to various complications, up to death in some cases. For these reasons, the authors have considered it appropriate to collect, using one of the major bibliographic databases available, and analyze the studies carried out until February 2022 on the use of machine learning techniques in the nephrological field, grouping them according to the addressed pathologies: renal masses, acute kidney injury, chronic kidney disease, kidney stone, glomerular disease, kidney transplant, and others less widespread. Of a total of 224 studies, 59 were analyzed according to inclusion and exclusion criteria in this review, considering the method used and the type of data available. Based on the study conducted, it is possible to see a growing trend and interest in the use of machine learning applications in nephrology, becoming an additional tool for physicians, which can enable them to make more accurate and faster diagnoses, although there remains a major limitation given the difficulty in creating public databases that can be used by the scientific community to corroborate and eventually make a positive contribution in this area.

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In the last years, smart-shoes moved from the medical domain, where they are used to collect gait-related data during rehabilitation or in case of pathologies, to the every-day life of an increasing number of people. In this paper, a method useful to effortlessly authenticate the user during gait periods is proposed. The method relies on the information collected by shoe-mounted accelerometers and gyroscopes, and on the distance between feet collected by Ultra-WideBand (UWB) transceivers. Experimental results show that a balanced accuracy equal to 97% can be achieved even when information about the possible impostors is not known in advance. The contribution of the different information sources, accelerometer, gyroscope, and UWB, is also evaluated.

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