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

Ozdemir S., Akin-Bulbul I., Kok I., Ozdemir S.

International journal of psychophysiology : official journal of the International Organization of Psychophysiology, cilt.173, ss.69-81, 2022 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 173
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1016/j.ijpsycho.2022.01.004
  • Dergi Adı: International journal of psychophysiology : official journal of the International Organization of Psychophysiology
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus, Academic Search Premier, IBZ Online, PASCAL, BIOSIS, EMBASE, MEDLINE, Psycinfo
  • Sayfa Sayıları: ss.69-81
  • Anahtar Kelimeler: Autism spectrum disorders, Eye tracking, Visual attention, Screening, Machine learning, Biomarker, SOCIAL ATTENTION, CIRCUMSCRIBED INTERESTS, EARLY IDENTIFICATION, EYE-TRACKING, CHILDREN, TODDLERS, PATTERNS, ADULTS, BRAIN, IMPAIRMENT
  • Hacettepe Üniversitesi Adresli: Evet

Özet

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.