Artificial Intelligence: the "Trait D'Union" in Different Analysis Approaches of Autism Spectrum Disorder Studies.

Autistic Spectrum Disorder (ASD) is a neurodevelopmental condition affecting approximately 1 out of 70 (range 1:59 - 1:89) children worldwide. It is characterized by a delay in cognitive capabilities, repetitive and restricted behaviors and deficit in communication and social interaction. Several factors seem to be associated with ASD development; its heterogeneous nature makes the diagnosis difficult and slow since it is essentially based on screening tools focused on stereotypical and repetitive behaviors, gait, facial emotion expression and speech assessments. Recently, artificial intelligence (AI) has been widely used to investigate ASD with the overall goal of simplifying and speeding up the diagnostic process as well as making earlier access to therapies possible. The aim of this review is to provide an overview of the state-of-the-art research in the ASD field, identifying and describing machine learning (ML) approaches in ASD literature that could be used by clinicians to improve diagnostic capability and treatment efficiency. A systematic search was conducted and the resulting articles were subdivided into several categories reflecting the different fields of study associated with ASD research. The existing literature has widely demonstrated the potential of ML in several types of ASD study analyses: behavior, gait, speech, facial emotion expression, neuroimaging, genetics, and metabolomics. Therefore, AI techniques are becoming increasingly implemented and accepted, so highlighting the power of ML approaches to extract and obtain knowledge from a large volume of data. This makes ML a promising tool for future ASD research and clinical endeavors suggesting possible avenues for improving ASD screening, diagnostic and therapeutic tools.

An innovative adaptive control strategy for sensorized left ventricular assist devices.

Nowadays advanced heart failure is mainly treated through heart transplantation. However, the low availability of donors makes the research of alternative therapies urgent. Continuous-flow left ventricular assist devices (LVADs) are going to assume a more significant role in assisting the failing heart. A recent challenge in clinical practice is the possibility to use LVAD as long-term therapy rather than as a bridge to transplantation. For this reason, more comfortable devices, able to dynamically adapt to the physiological cardiac demand in relation to the patient activity level, are needed in order to improve the life quality of patients with implants. Nevertheless, no control system has been developed yet for this purpose. This work proposes an innovative control strategy for a novel sensorized LVAD, based on the continuous collection of physical and functional parameters coming from implantable sensors and from the LVAD itself. Thanks to the proposed system, both the patient and the LVAD conditions are continuously monitored and the LVAD activity regulated accordingly. Specifically, a Proportional Integrative (PI) and a threshold control algorithms have been implemented, respectively based on flow and pressure feedbacks collected from the embedded sensors. To investigate the feasibility and applicability of this control strategy, an on-bench platform for LVADs sensing and monitoring has been developed and tested.