| Literature DB >> 30097760 |
Guobin Wan1, Xuejun Kong2, Binbin Sun1, Siyi Yu3, Yiheng Tu3, Joel Park3, Courtney Lang3, Madelyn Koh2, Zhen Wei1, Zhe Feng1, Yan Lin4, Jian Kong5.
Abstract
Eye tracking (ET) holds potential for the early detection of autism spectrum disorder (ASD). To overcome the difficulties of working with young children, developing a short and informative paradigm is crucial for ET. We investigated the fixation times of 37 ASD and 37 typically developing (TD) children ages 4-6 watching a 10-second video of a female speaking. ASD children showed significant reductions in fixation time at six areas of interest. Furthermore, discriminant analysis revealed fixation times at the mouth and body could significantly discriminate ASD from TD with a classification accuracy of 85.1%, sensitivity of 86.5%, and specificity of 83.8%. Our study suggests that a short video clip may provide enough information to distinguish ASD from TD children.Entities:
Keywords: Autism; Eye tracking; Face; Fixation time; Machine learning
Mesh:
Year: 2019 PMID: 30097760 DOI: 10.1007/s10803-018-3690-y
Source DB: PubMed Journal: J Autism Dev Disord ISSN: 0162-3257