Assessment of the Autism Spectrum Disorder Based on Machine Learning and Social Visual Attention: A Systematic Review

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 eviden...

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Bibliographic Details
Published in:Journal of autism and developmental disorders 2022-05, Vol.52 (5), p.2187-2202
Main Authors: Minissi, Maria Eleonora, Chicchi Giglioli, Irene Alice, Mantovani, Fabrizia, Alcañiz Raya, Mariano
Format: Article
Language:English
Subjects:
Artificial Intelligence
Attention
Autism
Autism Spectrum Disorder - diagnosis
Autistic children
Behavioral Science and Psychology
Biological markers
Biomarkers
Brain stimulation
Caregivers
Child
Child and School Psychology
Children
Clinical Diagnosis
Diagnosis
Evaluation
Eye Movements
Health aspects
Humans
Identification
Learning algorithms
Machine Learning
Medical diagnosis
Methods
Neurosciences
Original Paper
Pediatrics
Pervasive Developmental Disorders
Physiological psychology
Physiology
Psychological aspects
Psychology
Public Health
Social discrimination learning
Systematic review
Technology application
Visual attention
Visual discrimination learning
Visual perception
Visual Stimuli
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Summary: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.
ISSN:0162-3257
1573-3432
DOI:10.1007/s10803-021-05106-5