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Autism Res. 2016 Aug;9(8):888-98. doi: 10.1002/aur.1615. Epub 2016 Apr 1.

Identifying children with autism spectrum disorder based on their face processing abnormality: A machine learning framework.

Liu W1,2, Li M3, Yi L4.

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From the Sun Yat-sen University Carnegie Mellon University Joint Institute of Engineering, Sun Yat-sen University, Guangzhou Higher Education Mega Center, Guangzhou, China.
Department of ECE, Carnegie Mellon University, Pittsburgh, PA, USA.
Sun Yat-sen University Carnegie Mellon University Shunde International Joint Research Institute, Shunde, Guangdong, China.
Department of Psychology and Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, China.


The atypical face scanning patterns in individuals with Autism Spectrum Disorder (ASD) has been repeatedly discovered by previous research. The present study examined whether their face scanning patterns could be potentially useful to identify children with ASD by adopting the machine learning algorithm for the classification purpose. Particularly, we applied the machine learning method to analyze an eye movement dataset from a face recognition task [Yi et al., 2016], to classify children with and without ASD. We evaluated the performance of our model in terms of its accuracy, sensitivity, and specificity of classifying ASD. Results indicated promising evidence for applying the machine learning algorithm based on the face scanning patterns to identify children with ASD, with a maximum classification accuracy of 88.51%. Nevertheless, our study is still preliminary with some constraints that may apply in the clinical practice. Future research should shed light on further valuation of our method and contribute to the development of a multitask and multimodel approach to aid the process of early detection and diagnosis of ASD. Autism Res 2016, 9: 888-898.


autism spectrum disorder; eye tracking; face processing; machine learning

[Indexed for MEDLINE]

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