Diagnosis of autism spectrum disorder (ASD) is typically performed using traditional tools based on behavioral observations. However, these diagnosis methods are time-consuming and can be misleading. Integrating machine learning algorithms with technological screening tools within the typical behavioral observations can possibly enhance the traditional assessment and diagnostic process. In the last two decades, to improve the accuracy and reliability of autism detection, many clinicians and researchers began to develop new screening methods by means of advanced technology like machine learning (ML). These methods include artificial neural networks (ANN), support vector machines (SVM), a priori algorithms, and decision trees (DT). Mostly, these methods have been applied to pre-existing datasets, derived from the standard diagnostic and assessment tools, to implement and test predictive models. On the other hand, the detection of new objective behavioral measures such as biomarkers could lead to a significant strengthening of existing screening tools. In the present study, we carried out a critical review of the literature about the latest findings in this field. The aim was to shed light about the effectiveness of using ML systems for motion analysis to enhance both clinical assessment and diagnostic processes. Specifically, we discussed the contribution of ML systems in promoting early diagnosis of ASD. The literature review showed that motion patterns ML analysis predicts ASD classification as accurately as that of classical gold standard tools. However, the application of these methods is still challenging, as discussed in this review.

Autism is a neurodevelopmental disorder typically assessed and diagnosed through observational analysis of behavior. Assessment exclusively based on behavioral observation sessions requires a lot of time for the diagnosis. In recent years, there is a growing need to make assessment processes more motivating and capable to provide objective measures of the disorder. New evidence showed that motor abnormalities may underpin the disorder and provide a computational marker to enhance assessment and diagnostic processes. Thus, a measure of motor patterns could provide a means to assess young children with autism and a new starting point for rehabilitation treatments. In this study, we propose to use a software tool that through a smart tablet device and touch screen sensor technologies could be able to capture detailed information about children’s motor patterns. We compared movement trajectories of autistic children and typically developing children, with the aim to identify autism motor signatures analyzing their coordinates of movements. We used a smart tablet device to record coordinates of dragging movements carried out by 60 children (30 autistic children and 30 typically developing children) during a cognitive task. Machine learning analysis of children’s motor patterns identified autism with 93% accuracy, demonstrating that autism can be computationally identified. The analysis of the features that most affect the prediction reveals and describes the differences between the groups, confirming that motor abnormalities are a core feature of autism.

The functional analysis of problem behavior is an important type of assessment that has been shown to increase the likelihood of arriving at an efficacious treatment. Several studies described the efficient use of the Interviewed-Informed Synthesized Contingency Analysis (IISCA) to reveal the variables that influence problem behavior across a variety of topographies, participants, and settings. In this study, we describe a new adaptation of the IISCA, the Performance-Based IISCA, used to assess the severe problem behavior of five Italian children with autism admitted to a clinic in the south of Italy. In all applications, the Performance-Based IISCA elucidated the synthesized establishing operations that evoked problem behavior and the synthesized reinforcers that influenced its maintenance. This study extends the literature on functional analysis by introducing the Performance-Based IISCA, demonstrating its viability with Italian clients and demonstrating how its procedural refinements can lead to safe analyses.

Introduction: Autism Spectrum Disorder (ASD) is a by-birth neurodevelopmental disorder difficult to diagnose owing to the lack of clinical objective and quantitative measures. Classical diagnostic processes are time-consuming and require many specialists’ collaborative efforts to be properly accomplished. Most recent research has been conducted on automated ASD detection using advanced technologies. The proposed model automates ASD detection and provides a new quantitative method to assess ASD.Methods: The theoretical framework of our study assumes that motor abnormalities can be a potential hallmark of ASD, and Machine Learning may represent the method of choice to analyse them. In this study, a variational autoencoder, a particular type of Artificial Neural Network, is used to improve ASD detection by analysing the latent distribution description of motion features detected by a tablet-based psychometric scale.Results: The proposed ASD detection model revealed that the motion features of children with autism consistently differ from those of children with typical development.Discussion: Our results suggested that it could be possible to identify potential motion hallmarks typical for autism and support clinicians in their diagnostic process. Potentially, these measures could be used as additional indicators of disorder or suspected diagnosis.

While the negative impact of COVID-19 total lockdown on mental health in youth has been extensively studied, findings collected during subsequent waves of the pandemic, in which restrictive rules were more eased, are very sparse. Here, we explore perceived psychological distress during the partial lockdown of the third wave in Southern Italy in a large sample of students, focusing on age and gender differences. Also, we assessed whether attending the type of education could have a protective role on students’ psychological well-being. An online survey was completed by 1064 southern Italian students (age range: 8–19 years; males = 368) from March to July 2021. The survey consists of a set of questions regarding general sociodemographic information as well as several aspects of students’ psychological well-being. Psychological distress was higher in high school students compared to both elementary and middle ones. In addition, we found gender differences, but only in high school students, with females reporting higher psychological distress than males. Finally, our mediation analysis showed a mediated role of face-to-face schooling in the relationship between age and psychological distress. In conclusion, this study highlights age-related differences in psychological distress during the pandemic and the protective role of school in presence for mental health in Italian students.

In the recent literature, there is a broad consensus on the effectiveness of Applied Behavior Analysis interventions for autism spectrum disorder (ASD). Despite their proven efficacy, research in clinical settings shows that these treatments are not equally effective for all children and the issue of which intervention should be chosen for an individual remains a common dilemma. The current work systematically reviewed studies on predictors and moderators of response to different types of evidence-based treatment for children with ASD. Specifically, our goal was to critically review the relationships between pre-treatment child characteristics and specific treatment outcomes, covering different aspects of functioning (i.e., social, communicative, adaptive, cognitive, motor, global functioning, play, and symptom severity). Our results questioned the binomial “better functioning-better outcome”, emphasizing the complex interplay between pre-treatment child characteristics and treatment outcomes. However, some pre-treatment variables seem to act as prerequisites for a specific treatment, and the issue of “what works for whom and why” remains challenging. Future research should focus on the definition of evidence-based decision-making models that capture those individual factors through which a specific intervention will exert its effects.