This study is registered with PROSPERO, CRD42022296037.Ģ61 articles were identified, of which 24 studies with sample sizes ranging from 28 to 141 were included (n = 1396 individuals). We extracted the data on study population, model performances, algorithms of machine learning, and paradigms of eye-tracking. Studies using machine learning methods to classify ASD and TD individuals based on eye-tracking technologies were included. We searched Medline, Embase, Web of Science, Scopus, Cochrane Library, IEEE Xplore Digital Library, Wan Fang Database, China National Knowledge Infrastructure, Chinese BioMedical Literature Database, VIP Database for Chinese Technical Periodicals, from database inception to December 24, 2021. We aimed to summarize the available evidence on the performances of machine learning algorithms in classifying ASD and typically developing (TD) individuals based on eye-tracking data. Machine learning has been widely used to identify Autism Spectrum Disorder (ASD) based on eye-tracking, but its accuracy is uncertain. We discuss the clinical implications of the results and propose that analysis of changes in visual attention and physiological level to social stimuli might be an integral part of a neurodevelopmental assessment. The results confirm the clinical relevance of eye-tracking and pupillometric indices in the field of ASD. Participants with autism were slower to fixate on social elements in the social scenes, and this latency metric correlated with clinical measures of poor social functioning. Contrary to individuals without autism diagnosis, participants with autism did not show greater pupillary response to images with a human. Eye tracking and pupillometry measures were collected while participants observed images of tightly controlled natural scenes with or without a human being. In this study, a group of 35 adolescents and young adults with neurodevelopmental disorders were tested to investigate the processing of social and non-social scenes in individuals who meet the diagnostic criteria for autism and those who do not. Moreover, due to high comorbidity with other neurodevelopmental disorders, it is often difficult to distinguish which differences are distinctive to ASD. People with Autism Spectrum Disorder (ASD) present with atypical social, communicative and behavioral patterns, but underlying substrates of these behavioral differences remain unclear. This can be shown through tracking of gaze patterns and pupil size, the latter of which taps into an individual’s cognitive engagement and affective arousal. Typically, developing humans innately place subjective value on social information and orient attention to it.

Social scene pictures