Machine learning-based early diagnosis of autism according to eye movements of real and artificial faces scanning

Fanchao Meng; Fenghua Li; Shuxian Wu; Tingyu Yang; Zhou Xiao; Yujian Zhang; Zhengkui Liu; Jianping Lu; Xuerong Luo

doi:10.3389/fnins.2023.1170951

Machine learning-based early diagnosis of autism according to eye movements of real and artificial faces scanning

Front Neurosci. 2023 Sep 15:17:1170951. doi: 10.3389/fnins.2023.1170951. eCollection 2023.

Authors

Fanchao Meng^{1

2}, Fenghua Li³, Shuxian Wu², Tingyu Yang², Zhou Xiao⁴, Yujian Zhang⁵, Zhengkui Liu³, Jianping Lu⁴, Xuerong Luo²

Affiliations

¹ The National Clinical Research Center for Mental Disorder & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China.
² Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.
³ Key Lab of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.
⁴ Department of Child Psychiatry, Kangning Hospital of Shenzhen, Shenzhen Mental Health Center, Shenzhen, Guangdong, China.
⁵ Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Chengdu, Sichuan, China.

Abstract

Background: Studies on eye movements found that children with autism spectrum disorder (ASD) had abnormal gaze behavior to social stimuli. The current study aimed to investigate whether their eye movement patterns in relation to cartoon characters or real people could be useful in identifying ASD children.

Methods: Eye-tracking tests based on videos of cartoon characters and real people were performed for ASD and typically developing (TD) children aged between 12 and 60 months. A three-level hierarchical structure including participants, events, and areas of interest was used to arrange the data obtained from eye-tracking tests. Random forest was adopted as the feature selection tool and classifier, and the flattened vectors and diagnostic information were used as features and labels. A logistic regression was used to evaluate the impact of the most important features.

Results: A total of 161 children (117 ASD and 44 TD) with a mean age of 39.70 ± 12.27 months were recruited. The overall accuracy, precision, and recall of the model were 0.73, 0.73, and 0.75, respectively. Attention to human-related elements was positively related to the diagnosis of ASD, while fixation time for cartoons was negatively related to the diagnosis.

Conclusion: Using eye-tracking techniques with machine learning algorithms might be promising for identifying ASD. The value of artificial faces, such as cartoon characters, in the field of ASD diagnosis and intervention is worth further exploring.

Keywords: autism spectrum disorder; cartoon character; eye-tracking; machine learning; random forest.