Machine learning-based early diagnosis of autism according to eye movements of real and artificial faces scanning - Meng, Fanchao; Li, Fenghua; Wu, Shuxian; Yang, Tingyu; Xiao, Zhou; Zhang, Yujian; Liu, Zhengkui; Lu, Jianping; Luo, Xuerong (Frontiers in Neuroscience, 2023)
Background: Studies on eye movements found that children with autism spectrum disorder (ASD) had abnormal gaze behavior to social stimuli. The current study aims to investigate whether their eye-movement patterns to cartoon characters or real persons could be useful in identifying ASD children. Methods: Eye-tracking tests based on videos of cartoon characters and real persons were performed for ASD and typically developing (TD) children aged between 12 and 60 months. A 3-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 diagnoses 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. The 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 to identify ASD. The value of artificial faces, such as cartoon characters in the field of ASD diagnosis and intervention is worth further exploring.