@conference{Kollias2021,

title = {The contribution of Machine Learning and Eye-tracking technology in Autism Spectrum Disorder research: A Review Study},

author = { K.-F. Kollias and C.K. Syriopoulou-Delli and P. Sarigiannidis and G.F. Fragulis},

url = {https://www.researchgate.net/publication/352737405_The_contribution_of_Machine_Learning_and_Eye-tracking_technology_in_Autism_Spectrum_Disorder_research_A_Review_Study},

doi = {10.1109/MOCAST52088.2021.9493357},

year  = {2021},

date = {2021-01-01},

journal = {2021 10th International Conference on Modern Circuits and Systems Technologies, MOCAST 2021},

abstract = {According to Diagnostic and Statistical Manual of Mental Disorders, Autism spectrum disorder (ASD) is a developmental disorder characterised by reduced social interaction and communication, and by restricted, repetitive, and stereotyped behaviour. An important characteristic of autism, referred in several diagnostic tests, is a deficit in eye gaze. The objective of this study is to review the literature concerning machine learning and eye-tracking in ASD studies conducted since 2015. Our search on PubMed identified 18 studies which used various eye-tracking instruments, applied machine learning in different ways, distributed several tasks and had a wide range of sample sizes, age groups and functional skills of participants. There were also studies that utilised other instruments, such as Electroencephalography (EEG) and movement measures. Taken together, the results of these studies show that the combination of machine learning, and eye-tracking technology can contribute to autism identification characteristics by detecting the visual atypicalities of ASD people. In conclusion, machine learning and eye-tracking ASD studies could be considered a promising tool in autism research and future studies could involve other technological approaches, such as Internet of Things (IoT), as well. © 2021 IEEE.},

keywords = {ASD, ASD; Autism; Eye-tracking technology; Machine learning, Autism, Eye-tracking technology, machine learning},

pubstate = {published},

tppubtype = {conference}

}