Identifying Children with Autism Spectrum Disorder Based on Their Face Processing Abnormality: A Machine Learning Framework

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. (C) 2016 International Society for Autism Research, Wiley Periodicals, Inc.

先前的研究多次发现自闭症谱系障碍（ASD）患者的面部扫描模式不典型。本研究通过采用机器学习算法进行分类，检验他们的面部扫描模式是否可能有助于识别自闭症儿童。具体而言，我们应用机器学习方法分析了一个人脸识别任务中的眼动数据集[Yi等人，2016]，对患有和未患有自闭症的儿童进行分类。我们从分类自闭症的准确性、敏感性和特异性方面评估了我们模型的性能。结果表明，基于面部扫描模式应用机器学习算法识别自闭症儿童有很有前景的证据，最高分类准确率为88.51%。然而，我们的研究仍然是初步的，在临床实践中可能存在一些限制。未来的研究应进一步评估我们的方法，并有助于开发一种多任务和多模型的方法，以辅助自闭症的早期检测和诊断过程。（C）2016国际自闭症研究协会，威利期刊公司