Assessment of the Autism Spectrum Disorder Based on Machine Learning and Social Visual Attention: A Systematic Review
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Published:2021-06-08
Issue:
Volume:
Page:
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ISSN:0162-3257
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Container-title:Journal of Autism and Developmental Disorders
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language:en
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Short-container-title:J Autism Dev Disord
Author:
Minissi Maria EleonoraORCID, Chicchi Giglioli Irene Alice, Mantovani Fabrizia, Alcañiz Raya MarianoORCID
Abstract
AbstractThe 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.
Funder
Ministerio de Economía, Industria y Competitividad, Gobierno de España
Publisher
Springer Science and Business Media LLC
Subject
Developmental and Educational Psychology
Reference91 articles.
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