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Kollias, K. Machine Learning and Eye-Tracking Technology in ASD. Encyclopedia. Available online: https://encyclopedia.pub/entry/17095 (accessed on 17 August 2024).
Kollias K. Machine Learning and Eye-Tracking Technology in ASD. Encyclopedia. Available at: https://encyclopedia.pub/entry/17095. Accessed August 17, 2024.
Kollias, Konstantinos. "Machine Learning and Eye-Tracking Technology in ASD" Encyclopedia, https://encyclopedia.pub/entry/17095 (accessed August 17, 2024).
Kollias, K. (2021, December 14). Machine Learning and Eye-Tracking Technology in ASD. In Encyclopedia. https://encyclopedia.pub/entry/17095
Kollias, Konstantinos. "Machine Learning and Eye-Tracking Technology in ASD." Encyclopedia. Web. 14 December, 2021.
Machine Learning and Eye-Tracking Technology in ASD
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This entry is adapted from the peer-reviewed paper 10.3390/electronics10232982

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.

machine learning eye-tracking technology ASD autism assessment classification

1. 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”. Asperger in [5] defined ASD as “autistic psychopathy,” meaning autism (self) and psychopathy (personality). ASD reaches a high male-to female ratio, attaining an average of 4:1, a steep increase to 10:1 in “high functioning autism” or Asperger syndrome and a fall to 2:1 in people presenting comorbidity with moderate-to-severe intellectual disability [6].
In addition to reduced social interaction and communication, restricted, repetitive, and stereotyped behaviour, people with ASD tend to show a deficit in eye gaze, a characteristic which cannot cause autism [2] but which constitutes an important item in several diagnostic tests [7]. Eye gaze deficits of ASD people are related both to social and non-social stimuli. As far as social and facial stimuli are concerned, individuals with ASD are likely to have difficulties to preferentially attend both biological motion, i.e., gestures of the body, expressions of the face, as well as the eyes of others [8]. In other words, individuals with ASD tend to show visual differences in visual attention to faces, compared to typically developing ones. 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].
Early ASD assessment and intervention have long-term outcomes for ASD children and their families, who require educational, medical, social, and economic support to improve the quality of their lives. ASD assessment challenges professionals, as there are not any well-established biophysiological diagnostic tests [10][11]. Thus, diagnosis is usually based on behavioural assessment, employing standardised tools of high validity and reliability, such as the Autism Diagnostic Observation Schedule (ADOS) [7] and the Autism Diagnostic Interview-Revised (ADI-R) [12]. These tools, broadly recognised in research, are presented as the gold standard for ASD diagnosis regarding clinical settings [13][14]. Nevertheless, their utilisation requires numerous materials, takes considerable time and is rather costly [10][11]. In addition, complex clinical protocols are included, whereas experienced and trained interviewers, who can affect the diagnostic procedure, are required [10][11][15]. Taken together, these challenges can often lead to a delayed diagnosis, resulting in a delay regarding the onset of early intervention [11]. Literature shows that when interventions start before ASD children reach age 5, children show a significantly increased success rate (67%), in comparison with the 11% success rate when interventions start later than age 5 [16].
Eye-tracking technology is considered an advantageous approach to ASD research, as it offers the ability to detect autism and features of it [8][17] earlier and in a more objective and reliable way than conventional assessment [18]. There has been a steep increase in the number of eye-tracking studies concerning autism during the last decade, either due to easier access to eye-tracking technology [19], or because of the special devices and software devised contributing to easier and less-expensive recording of eye-tracking data [18].
Eye-tracking instruments are often combined with modern artificial intelligence techniques, such as machine learning, a data driven technique, based on advanced learning of mathematics, statistical estimation, and theories of information [20] in which the computer algorithm is trained in order to analyse a set of data observed and learns the latent patterns in a statistical approach [20][21]. Machine learning can contribute to autism research by providing a less biased and reproducible second opinion [22], i.e., early autism screening [23] and diagnosis enhancement [20], as well as different behaviours [24] and brain activity observation [25]. Moreover, machine learning can be a valid biomarker-based technique that can contribute to objective ASD diagnosis [26]. Machine learning has also been applied in the Internet of Things (IoT) systems for ASD assessment [27][28]. Finally, regarding intervention, the quality of life of ASD people can be improved by assistive technology in the training of children with autism spectrum disorders [29].
Our Contribution
This systematic review provides a comprehensive discussion of the literature concerning machine learning and eye-tracking ASD studies conducted since 2015. To our knowledge, although machine learning and eye-tracking technology hold promise for earlier and more objective autism diagnosis, this is the first systematic review study concerning machine learning and eye-tracking ASD studies, irrespective of the stimuli/tasks and dataset used, the algorithms applied, the eye-tracking tools utilised, and their goals. The only systematic review, similar to this one, presents 11 papers about early ASD assessment which applied ML models related only to children’s social visual attention (SVA) [26]. Thus, the present study reviews machine learning and eye-tracking technology ASD studies formulating the hypothesis that machine learning and eye-tracking technology can contribute to an earlier and more objective ASD detection.

2. Review Flow

The PRISMA 2020 flow diagram of study selection is presented in Figure 1. Searches on PubMed® identified 33 articles and one systematic review study. Fifteen of the articles were removed after their title and abstract had been screened. Twelve more articles were identified in the reference lists of chosen articles. Therefore, the present systematic review involves 30 articles and one systematic review study.
Figure 1. PRISMA 2020 flow diagram of study selection.

3. Conclusion

The present systematic review identified studies most of which employed image and video stimuli. There were also four emotion recognition ones two of which found results of increased significance. In addition, there were two web-browsing studies, one gaze and demographic features study, one movement imitation study, one virtual reality (VR) interaction study, one social interaction task (SIT) study and one face-to-face conversation study.

It is difficult to compare the results of all the above-mentioned studies as they included different tasks and datasets, applied different algorithms, utilised different eye-tracking tools and had different goals. Moreover, there were limitations in some studies, such as limited sample size, difficulty to create and/or choose an appropriate algorithm/task and find significant eye-tracking results. Despite these differences and limitations, the results obtained showed that most of the studies utilised Machine Learning and eye-tracking technology for ASD classification and reached an accuracy higher than 80%. Additionally, there were five studies that applied Machine Learning and eye-tracking technology with a different approach. Finally, when Eye-tracking and Machine Learning were combined with Kinematic Measures and Electroencephalography (EEG), classification accuracy increased showing that multimodal assessment can be more reliable and accurate
Therefore, the formulated hypothesis is supported, for the present systematic review study, and Machine Learning and eye-tracking technology appear to contribute to an earlier and more objective ASD detection. Regarding suggestions for future research, Machine Learning and eye-tracking technology could be used to identify not only ASD, but other disorders, such as anxiety and schizophrenia. The combination of Machine Learning and eye-tracking technology with other technological approaches could be promising for future research as well. Internet of Things (IoT)-based systems, for instance, could be considered as one more developing method used not only to improve ASD diagnosis, but also the Quality of Life of autistic people. Future research could also deal with assistive technology in the training of children with autism spectrum disorders, as it can play an important role in improving the quality of life of ASD children, whereas it is also thought to have potentials in intervention programmes and ASD research. Finally, we would also like to involve and apply Machine Learning, eye-tracking and other tools used for ASD assessment in our future research, i.e., systematic reviews and experiments.

References

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