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...
| Publicado en: | Journal of Autism & Developmental Disorders Vol. 52; no. 5; pp. 2187 - 2203 |
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| Autores principales: | , , , |
| Formato: | pictorial research systematic review tables/charts Journal Article |
| Publicado: |
Springer Nature
May2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=156413872&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 156413872 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01623257 AUT jtl: Journal of Autism & Developmental Disorders issn: 01623257 maglogo: N pubinfo: dt: May2022 vid: 52 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 156413872 150783389 156413872 156413872 10.1007/s10803-021-05106-5 156413872 ppf: 2187 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Assessment of the Autism Spectrum Disorder Based on Machine Learning and Social Visual Attention: A Systematic Review. aug: au: Minissi, Maria Eleonora Chicchi Giglioli, Irene Alice Mantovani, Fabrizia Alcañiz Raya, Mariano affil: Institute for Research and Innovation in Bioengineering (i3B), Universitat Politécnica de Valencia, Ciudad de la Innovación, Building 8B, s/n Camino de Vera, 46022, Valencia, Spain sug: subj: Autism Spectrum Disorder Diagnosis Machine Learning Utilization Attention Visual Perception Biological Markers Eye Movements Human Systematic Review PubMed Early Diagnosis ab: 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. pubtype: Academic Journal doctype: pictorial research systematic review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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