机器学习和眼动追踪技术在自闭症谱系障碍研究中的贡献综述

Konstantinos-Filippos Kollias, Christine K. Syriopoulou-Delli, P. Sarigiannidis, G. Fragulis
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引用次数: 10

摘要

根据《精神障碍诊断与统计手册》,自闭症谱系障碍(ASD)是一种发育障碍,其特征是社交互动和沟通减少,行为受限、重复和刻板。在一些诊断测试中提到的自闭症的一个重要特征是眼睛注视的缺陷。本研究的目的是回顾2015年以来ASD研究中关于机器学习和眼动追踪的文献。我们在PubMed上的搜索确定了18项研究,这些研究使用了各种眼动追踪工具,以不同的方式应用了机器学习,分配了几个任务,样本量、年龄组和参与者的功能技能范围都很广。也有研究使用其他仪器,如脑电图(EEG)和运动测量。综上所述,这些研究的结果表明,机器学习和眼球追踪技术的结合可以通过检测ASD患者的视觉非典型性来帮助自闭症识别特征。总之,机器学习和眼球追踪ASD研究可以被认为是自闭症研究中很有前途的工具,未来的研究可能涉及其他技术方法,如物联网(IoT)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The contribution of Machine Learning and Eye-tracking technology in Autism Spectrum Disorder research: A Review Study
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.
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