Reading is a highly complex process in which integrative neurocognitive functions are required. Visual-spatial abilities play a pivotal role because of the multi-faceted visual sensory processing involved in reading. Several studies show that children with developmental dyslexia (DD) fail to develop effective visual strategies and that some reading difficulties are linked to visual-spatial deficits. However, the relationship between visual-spatial skills and reading abilities is still a controversial issue. Crucially, the role that age plays has not been investigated in depth in this population, and it is still not clear if visual-spatial abilities differ across educational stages in DD. The aim of the present study was to investigate visual-spatial abilities in children with DD and in age-matched normal readers (NR) according to different educational stages: in children attending primary school and in children and adolescents attending secondary school. Moreover, in order to verify whether visual-spatial measures could predict reading performance, a regression analysis has been performed in younger and older children. The results showed that younger children with DD performed significantly worse than NR in a mental rotation task, a more-local visual-spatial task, a more-global visual-perceptual task and a visual-motor integration task. However, older children with DD showed deficits in the more-global visual-perceptual task, in a mental rotation task and in a visual attention task. In younger children, the regression analysis documented that reading abilities are predicted by the visual-motor integration task, while in older children only the more-global visual-perceptual task predicted reading performances. Present findings showed that visual-spatial deficits in children with DD were age-dependent and that visual-spatial abilities engaged in reading varied across different educational stages. In order to better understand their potential role in affecting reading, a comprehensive description and a multi-componential evaluation of visual-spatial abilities is needed with children with DD.

In order to reduce the burden on healthcare systems and in particular to support an appropriate way to the Emergency Department (ED) access, home tele-monitoring patients was strongly recommended during the COVID-19 pandemic. Furthermore, paper from numerous groups has shown the potential of using data from wearable devices to characterize each individual's unique baseline, identify deviations from that baseline suggestive of a viral infection, and to aggregate that data to better inform population surveillance trends. However, no evidence about usage of Artificial Intelligence (AI) applicatives on digitally data collected from patients and doctors exists. With a growing global population of connected wearable users, this could potentially help to improve the earlier diagnosis and management of infectious individuals and improving timeliness and precision of tracking infectious disease outbreaks. During the study RICOVAI-19 (RICOVero ospedaliero con strumenti di Artificial Intelligence nei pazienti con COVid-19) performed in a Marche Region, Italy, we evaluated N129 subjects monitored at home in a six-months period between March 22, 2021 and October 22, 2021. During the monitoring, personal on demand health technologies were used to collect clinical and vital data in order to feed the database and the machine learning engine. The AI output resulted in a clinical stability index (CSI) which enables the system to deliver suggestions to the population and doctors about how intervene . Results showed the beneficial influence of CSI for predicting clinical classes of subjects and identifying who of them need to be admitted at ED. The same pattern of results was confirming the alert included in the decision support system in order to request further testing or clinical information in some cases. In conclusion, our study does support an high impact of AI tools on COVID outcomes to fight this pandemic by driving new approaches to public awareness.