Development of a visual attention based decision support system for autism spectrum disorder screening

Selda Ozdemir; Isik Akin-Bulbul; Ibrahim Kok; Suat Ozdemir

doi:10.1016/j.ijpsycho.2022.01.004

Development of a visual attention based decision support system for autism spectrum disorder screening

Int J Psychophysiol. 2022 Mar:173:69-81. doi: 10.1016/j.ijpsycho.2022.01.004. Epub 2022 Jan 8.

Authors

Selda Ozdemir¹, Isik Akin-Bulbul², Ibrahim Kok³, Suat Ozdemir⁴

Affiliations

¹ Hacettepe University, Hacettepe Education Faculty, Department of Special Education, Beytepe, Ankara, Turkey. Electronic address: seldaozdemir@hacettepe.edu.tr.
² Gazi University, Gazi Education Faculty, Special Education Department Teknikokullar, Ankara, Turkey.
³ Pamukkale University, Engineering Faculty, Computer Engineering Department, Denizli, Turkey.
⁴ Hacettepe University, Engineering Faculty, Computer Engineering Department, Beytepe, Ankara, Turkey.

PMID: 35007668
DOI: 10.1016/j.ijpsycho.2022.01.004

Abstract

Visual attention of young children with autism spectrum disorder (ASD) has been well documented in the literature for the past 20 years. In this study, we developed a Decision Support System (DSS) that uses machine learning (ML) techniques to identify young children with ASD from typically developing (TD) children. Study participants included 26 to 36 months old young children with ASD (n = 61) and TD children (n = 72). The results showed that the proposed DSS achieved up to 87.5% success rate in the early assessment of ASD in young children. Findings suggested that visual attention is a unique, promising biomarker for early assessment of ASD. Study results were discussed, and suggestions for future research were provided.

Keywords: Autism spectrum disorders; Biomarker; Eye tracking; Machine learning; Screening; Visual attention.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Autism Spectrum Disorder* / diagnosis
Biomarkers
Child
Child, Preschool
Humans
Machine Learning

Substances

Biomarkers