Personalized Training via Serious Game to Improve Daily Living Skills in Pediatric Patients With Autism Spectrum Disorder.

The majority of people with Autism Spectrum Disorder (ASD) exhibit difficulties in social communication and behavior, which hinder their learning capability, amid others. Among technological solutions for people with ASD, serious games are frequently used to enhance learning of specific skills and instructional contents. However, because of heterogeneity in applications and game design, few studies have investigated their use in training daily activities. This paper presents a 3D personalized serious game we developed and validated to help ASD patients practice with shopping activities. Personalized training is paramount in people with ASD, thus several elements of this game were personalized to improve engagement and therefore the effectiveness of the virtual training. In order to assess the validity of the game, ten subjects (age [Formula: see text], 20% female) with ASD played ten sessions of the serious game, once per week. The participants underwent a real-life experience pre- and post-training in a real-life supermarket. Changes in daily living skills among participants were evaluated through specific tools: a form based on the International Classification of Functioning, Disability and Health for Children and Youth; and the Vineland Adaptive Behavior Scale II. Significant improvements (p 0.05) were detected in the main skills trained with the serious game, especially in learning the shopping procedure, directing attention, and problem-solving skills. These findings suggest that personalized serious games can represent a prominent tool to enhance daily living skills, but future work should clinically validate their efficacy.

A Multidisciplinary Telerehabilitation Approach for Supporting Social Interaction in Autism Spectrum Disorder Families: An Italian Digital Platform in Response to COVID-19.

Due to its complexity and high variability in symptomology, autism spectrum disorder (ASD) requires a coordinated and multidisciplinary intervention to better support the different programs over time and to promote social interactions in all contexts of life. Telemedicine can offer a valuable contribution in this regard, providing low-cost and portable applications. In this paper, we presented an Italian project, SUPER, which aimed to foster collaboration and information sharing between ASD families, health services, and schools. SUPER provided a digital platform with several tools that were useful both to enhance general and specific ASD knowledge and to promote personalized programs for children with ASD. We conducted a preliminary user test for the platform with 30 participants (18 therapists and 12 parents of children with ASD) using the system usability scale (SUS). The total mean SUS score (89.2) showed that SUPER is an excellent, usable system. Moreover, we extracted the usability and learnability mean components from the SUS scores, which were 96.1 and 61.7, respectively. Our preliminary results indicate that SUPER is a very user-friendly application and its innovative telemedicine approach could be ahelpful communication and collaboration tool among the different contexts of care for children with ASD.

Automated search of control points in surface-based morphometry.

Cortical surface-based morphometry is based on a semi-automated analysis of structural MRI images. In FreeSurfer, a widespread tool for surface-based analyses, a visual check of gray-white matter borders is followed by the manual placement of control points to drive the topological correction (editing) of segmented data. A novel algorithm combining radial sampling and machine learning is presented for the automated control point search (ACPS). Four data sets with 3 T MRI structural images were used for ACPS validation, including raw data acquired twice in 36 healthy subjects and both raw and FreeSurfer preprocessed data of 125 healthy subjects from public databases. The unedited data from a subgroup of subjects were submitted to manual control point search and editing. The ACPS algorithm was trained on manual control points and tested on new (unseen) unedited data. Cortical thickness (CT) and fractal dimensionality (FD) were estimated in three data sets by reconstructing surfaces from both unedited and edited data, and the effects of editing were compared between manual and automated editing and versus no editing. The ACPS-based editing improved the surface reconstructions similarly to manual editing. Compared to no editing, ACPS-based and manual editing significantly reduced CT and FD in consistent regions across different data sets. Despite the extra processing of control point driven reconstructions, CT and FD estimates were highly reproducible in almost all cortical regions, albeit some problematic regions (e.g. entorhinal cortex) may benefit from different editing. The use of control points improves the surface reconstruction and the ACPS algorithm can automate their search reducing the burden of manual editing.

Electrocardiogram pattern recognition and analysis based on artificial neural networks and support vector machines: a review.

Computer systems for Electrocardiogram (ECG) analysis support the clinician in tedious tasks (e.g., Holter ECG monitored in Intensive Care Units) or in prompt detection of dangerous events (e.g., ventricular fibrillation). Together with clinical applications (arrhythmia detection and heart rate variability analysis), ECG is currently being investigated in biometrics (human identification), an emerging area receiving increasing attention. Methodologies for clinical applications can have both differences and similarities with respect to biometrics. This paper reviews methods of ECG processing from a pattern recognition perspective. In particular, we focus on features commonly used for heartbeat classification. Considering the vast literature in the field and the limited space of this review, we dedicated a detailed discussion only to a few classifiers (Artificial Neural Networks and Support Vector Machines) because of their popularity; however, other techniques such as Hidden Markov Models and Kalman Filtering will be also mentioned.

Assessment of Standardised Uptake Values in PET Imaging Using Different Software Packages.

BACKGROUND: This study investigated whether the follow-up by means of positron emission tomography with computed tomography (PET/CT) is reliable when using different software packages for image reviewing. METHODOLOGY: This study explored the influence of software on patient follow up by analysing images acquired by means of PET/CT equipment. Images were analysed using three software packages. The study included two patients who underwent examinations over the course of 2 years. An expert radiologist selected the regions of interest surrounding the lesions on each slice. RESULTS: We carried out an analysis of differences among software packages on a region of interest and on a volume of interest basis, and using time-course analysis (differences among successive examinations). CONCLUSIONS: Our results suggest that within the limits of the currently available guidelines for PET evaluation, although differences exist among the standardised uptake value measurements by different software packages, they might not alter the qualitative time-course analysis (evaluation of follow-up) if the whole analysis is performed using a fixed package.