[article] 
inJournal of Autism and Developmental Disorders > 52-5 (May 2022) . - p.2187-2202

Titre :	Assessment of the Autism Spectrum Disorder Based on Machine Learning and Social Visual Attention: A Systematic Review
Type de document :	Texte imprimé et/ou numérique
Auteurs :	Maria Eleonora MINISSI, Auteur ; Irene Alice CHICCHI GIGLIOLI, Auteur ; Fabrizia MANTOVANI, Auteur ; Mariano ALCAÑIZ RAYA, Auteur
Article en page(s) :	p.2187-2202
Langues :	Anglais (eng)
Mots-clés :	Autism Spectrum Disorder/diagnosis  Biomarkers  Child  Eye Movements  Humans  Machine Learning  Assessment  Autism spectrum disorder  Classification  Eye tracking  Social visual attention
Index. décimale :	PER Périodiques
Résumé :	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.
En ligne :	http://dx.doi.org/10.1007/s10803-021-05106-5
Permalink :	https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=476

[article] Assessment of the Autism Spectrum Disorder Based on Machine Learning and Social Visual Attention: A Systematic Review [Texte imprimé et/ou numérique] / Maria Eleonora MINISSI, Auteur ; Irene Alice CHICCHI GIGLIOLI, Auteur ; Fabrizia MANTOVANI, Auteur ; Mariano ALCAÑIZ RAYA, Auteur . - p.2187-2202.
Langues : Anglais (eng)
in Journal of Autism and Developmental Disorders > 52-5 (May 2022) . - p.2187-2202

Mots-clés :	Autism Spectrum Disorder/diagnosis  Biomarkers  Child  Eye Movements  Humans  Machine Learning  Assessment  Autism spectrum disorder  Classification  Eye tracking  Social visual attention
Index. décimale :	PER Périodiques
Résumé :	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.
En ligne :	http://dx.doi.org/10.1007/s10803-021-05106-5
Permalink :	https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=476