用于自闭症谱系障碍筛查的基于视觉注意力的决策支持系统的开发。
Development of a visual attention based decision support system for autism spectrum disorder screening.
作者信息
Ozdemir Selda, Akin-Bulbul Isik, Kok Ibrahim, Ozdemir Suat
机构信息
Hacettepe University, Hacettepe Education Faculty, Department of Special Education, Beytepe, Ankara, Turkey.
Gazi University, Gazi Education Faculty, Special Education Department Teknikokullar, Ankara, Turkey.
出版信息
Int J Psychophysiol. 2022 Mar;173:69-81. doi: 10.1016/j.ijpsycho.2022.01.004. Epub 2022 Jan 8.
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.
在过去20年里，自闭症谱系障碍（ASD）幼儿的视觉注意力在文献中已有充分记载。在本研究中，我们开发了一种决策支持系统（DSS），该系统使用机器学习（ML）技术从发育正常（TD）的儿童中识别出自闭症谱系障碍幼儿。研究参与者包括26至36个月大的自闭症谱系障碍幼儿（n = 61）和发育正常的儿童（n = 72）。结果表明，所提出的决策支持系统在幼儿自闭症早期评估中成功率高达87.5%。研究结果表明，视觉注意力是自闭症早期评估中一种独特且有前景的生物标志物。我们讨论了研究结果，并为未来研究提供了建议。
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