Abstract
The assessment of autism spectrum disorder (ASD) is based on semi-structured procedures addressed to children and caregivers. Such methods rely on the evaluation of behavioural symptoms rather than on the objective evaluation of psychophysiological underpinnings. Advances in research provided evidence of modern procedures for the early assessment of ASD, involving both machine learning (ML) techniques and biomarkers, as eye movements (EM) towards social stimuli. This systematic review provides a comprehensive discussion of 11 papers regarding the early assessment of ASD based on ML techniques and children’s social visual attention (SVA). Evidences suggest ML as a relevant technique for the early assessment of ASD, which might represent a valid biomarker-based procedure to objectively make diagnosis. Limitations and future directions are discussed.
Highlights
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder affecting worldwide 1 in 160 children (WHO, 2019) which emerges in childhood and persists in adulthood
Remained articles plus 1 additional article from reference lists of selected articles met above criteria, with articles included in the systematic review
The aim of this systematic review was to discuss the recent scientific evidence on machine learning (ML) models used to classify children with ASD and typical developmental (TD) according to their eye movements (EM) on different social stimuli (SS)
Summary
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder affecting worldwide 1 in 160 children (WHO, 2019) which emerges in childhood and persists in adulthood. Traditional ASD Assessments of Social Skills and Diagnosis: Advantages and Limitations. An additional measure to assess ASD is the Child Autism Rating Scale (CARS; Schopler et al, 1980), which is analogous to ADOS since it is based on the clinician’s evaluation of child’s behaviours, following two short rating scales. Since SCQ and SRS refer to caregivers, they share the same ADI-R limitation regarding the absence of a direct observation on child’s social behaviours. Traditional assessment scores rely on the examiner’s interpretation of respectively child’s behaviours and parents’ reports, examiner’s strong expertise in the ASD field, as well as clinical training in ASD assessment procedure, are highly recommended to avoid the misleading detection and interpretation of symptoms (Lord et al, 2001; Reaven et al, 2008). Traditional ASD assessment is not always sensitive to differential diagnosis, as in the case of low-functioning children, who are often wrongly diagnosed as ASD instead of children with intellectual disability (De Bildt et al, 2004)
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