Abstract
BackgroundThe early diagnosis of autism spectrum disorder (ASD) is highly desirable but remains a challenging task, which requires a set of cognitive tests and hours of clinical examinations. In addition, variations of such symptoms exist, which can make the identification of ASD even more difficult. Although diagnosis tests are largely developed by experts, they are still subject to human bias. In this respect, computer-assisted technologies can play a key role in supporting the screening process.ObjectiveThis paper follows on the path of using eye tracking as an integrated part of screening assessment in ASD based on the characteristic elements of the eye gaze. This study adds to the mounting efforts in using eye tracking technology to support the process of ASD screeningMethodsThe proposed approach basically aims to integrate eye tracking with visualization and machine learning. A group of 59 school-aged participants took part in the study. The participants were invited to watch a set of age-appropriate photographs and videos related to social cognition. Initially, eye-tracking scanpaths were transformed into a visual representation as a set of images. Subsequently, a convolutional neural network was trained to perform the image classification task.ResultsThe experimental results demonstrated that the visual representation could simplify the diagnostic task and also attained high accuracy. Specifically, the convolutional neural network model could achieve a promising classification accuracy. This largely suggests that visualizations could successfully encode the information of gaze motion and its underlying dynamics. Further, we explored possible correlations between the autism severity and the dynamics of eye movement based on the maximal information coefficient. The findings primarily show that the combination of eye tracking, visualization, and machine learning have strong potential in developing an objective tool to assist in the screening of ASD.ConclusionsBroadly speaking, the approach we propose could be transferable to screening for other disorders, particularly neurodevelopmental disorders.
Highlights
autism spectrum disorder (ASD) CharacteristicsAutism spectrum disorder (ASD) has been described as a pervasive developmental disorder characterized by a set of impairments including social communication problems, difficulties with reciprocal social interactions, and unusual patterns of repetitive behaviors or interests [1]
The results showed that participants with ASD spent significantly more time fixating on dynamic geometric images compared to other diagnostic groups
This study demonstrated the strong potential of eye tracking as an objective tool for assisting ASD diagnosis
Summary
ASD CharacteristicsAutism spectrum disorder (ASD) has been described as a pervasive developmental disorder characterized by a set of impairments including social communication problems, difficulties with reciprocal social interactions, and unusual patterns of repetitive behaviors or interests [1]. During naturalistic interaction, making and maintaining eye contact is not always easy or spontaneous for ASD-diagnosed individuals Such troubling deficits can place a considerable strain on their lives and their families. The variation of symptoms with regard to deficits in social communication and social interaction as well as the social communication impairments and restricted, repetitive patterns of behavior make the identification of ASD more complicated to decide In this respect, computer-aided technologies have been embraced to provide helpful guidance through the course of examination and assessment. A considerable number of other psychology studies in eye tracking have been based on the particularities of eye movements in response to verbal or visual cues as signs of ASD [5,6,7]. Conclusions: Broadly speaking, the approach we propose could be transferable to screening for other disorders, neurodevelopmental disorders
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