Accepted for/Published in: JMIR Human Factors
Date Submitted: Feb 3, 2021
Open Peer Review Period: Feb 3, 2021 - Feb 17, 2021
Date Accepted: May 24, 2021
(closed for review but you can still tweet)
Computer-Aided Screening of Autism Spectrum Disorder Using Eye-Tracking, Data Visualization and Deep Learning
ABSTRACT
Background:
The 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. Though 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.
Objective:
This paper follows on the path of utilizing 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 for utilizing the eye-tracking technology to support the process of ASD screening.
Methods:
The proposed approach basically aims to integrate eye-tracking with visualization and Machine Learning (ML). 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 (CNN) was trained to perform the image classification task. The experimental results evidently demonstrated that the visual representation could simplify the diagnostic task and attained a high accuracy as well. Specifically, the CNN model could achieve a promising classification accuracy. This largely translates into 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 (MIC).
Results:
The study primarily concludes that the coupling of eye-tracking, visualization and ML holds a strong potential for developing an objective tool to assist the screening of ASD.
Conclusions:
In a broader sense, it is conceived that our approach could be transferable to comparable screening and then diagnosis problems.
Citation
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Copyright
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