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

Autor/inn/en	Minissi, Maria Eleonora; Chicchi Giglioli, Irene Alice; Mantovani, Fabrizia; Alcañiz Raya, Mariano
Titel	Assessment of the Autism Spectrum Disorder Based on Machine Learning and Social Visual Attention: A Systematic Review
Quelle	In: Journal of Autism and Developmental Disorders, 52 (2022) 5, S.2187-2202 (16 Seiten) PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Minissi, Maria Eleonora) ORCID (Alcañiz Raya, Mariano)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0162-3257
DOI	10.1007/s10803-021-05106-5
Schlagwörter	Autism; Pervasive Developmental Disorders; Identification; Artificial Intelligence; Eye Movements; Visual Stimuli; Attention; Physiology; Clinical Diagnosis + Suchen Sie Ihr Suchwort? Autismus; Identifikation; Identifizierung; Künstliche Intelligenz; Augenbewegung; Aufmerksamkeit; Physiologie
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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01