The Contribution of Machine Learning and Eye-Tracking Technology in Autism Spectrum Disorder Research: A Systematic Review

Konstantinos-Filippos Kollias,Christine K Syriopoulou-Delli,Panagiotis Sarigiannidis,George F Fragulis

doi:10.3390/electronics10232982

Konstantinos-Filippos Kollias, Christine K Syriopoulou-Delli + Show 2 more

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https://doi.org/10.3390/electronics10232982

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Abstract

Early and objective autism spectrum disorder (ASD) assessment, as well as early intervention are particularly important and may have long term benefits in the lives of ASD people. ASD assessment relies on subjective rather on objective criteria, whereas advances in research point to up-to-date procedures for early ASD assessment comprising eye-tracking technology, machine learning, as well as other assessment tools. This systematic review, the first to our knowledge of its kind, provides a comprehensive discussion of 30 studies irrespective of the stimuli/tasks and dataset used, the algorithms applied, the eye-tracking tools utilised and their goals. Evidence indicates that the combination of machine learning and eye-tracking technology could be considered a promising tool in autism research regarding early and objective diagnosis. Limitations and suggestions for future research are also presented.

Highlights

The Diagnostic and Statistical Manual of Mental Disorders defines autism spectrum disorder (ASD) as a highly complicated neurodevelopmental disorder with complex etiological causes [1] characterised by social communication/interaction difficulties and repetitive behaviours/interests [2], prevalent in 1% of the world’s population [3]
Studies complying with the following inclusion criteria were selected: (a) patient groups had an ASD diagnosis, there were participants with ASD and ASD+ADHD diagnosis in [30]; (b) control groups consisted of TD participants apart from one study with Low/Medium/High ASD risk and ASD participants only [31]; (c) participants’ ages ranged from toddlers to adults; (d) the aim of the studies was ASD detection using machine learning combined with eye-tracking technology
Accuracy of 78% was achieved, and the area under curve (AUC) of the best-fit algorithm was 0.84

Summary

Introduction

The Diagnostic and Statistical Manual of Mental Disorders defines autism spectrum disorder (ASD) as a highly complicated neurodevelopmental disorder with complex etiological causes [1] characterised by social communication/interaction difficulties and repetitive behaviours/interests [2], prevalent in 1% of the world’s population [3]. It was first introduced by Kanner [4], who described it as involving “resistance to change” and “need for sameness”. Regarding non-social stimuli, individuals with ASD appear to show differences in comparison with typically developing people, i.e., impaired global and intact local visual processing [9]

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