Diagnosing Autism Spectrum Disorder Without Expertise: A Pilot Study of 5- to 17-Year-Old Individuals Using Gazefinder

Abstract

Atypical eye gaze is an established clinical sign in the diagnosis of autism spectrum disorder (ASD). We propose a computerized diagnostic algorithm for ASD, applicable to children and adolescents aged between 5 and 17 years using Gazefinder, a system where a set of devices to capture eye gaze patterns and stimulus movie clips are equipped in a personal computer with a monitor. We enrolled 222 individuals aged 5–17 years at seven research facilities in Japan. Among them, we extracted 39 individuals with ASD without any comorbid neurodevelopmental abnormalities (ASD group), 102 typically developing individuals (TD group), and an independent sample of 24 individuals (the second control group). All participants underwent psychoneurological and diagnostic assessments, including the Autism Diagnostic Observation Schedule, second edition, and an examination with Gazefinder (2 min). To enhance the predictive validity, a best-fit diagnostic algorithm of computationally selected attributes originally extracted from Gazefinder was proposed. The inputs were classified automatically into either ASD or TD groups, based on the attribute values. We cross-validated the algorithm using the leave-one-out method in the ASD and TD groups and tested the predictability in the second control group. The best-fit algorithm showed an area under curve (AUC) of 0.84, and the sensitivity, specificity, and accuracy were 74, 80, and 78%, respectively. The AUC for the cross-validation was 0.74 and that for validation in the second control group was 0.91. We confirmed that the diagnostic performance of the best-fit algorithm is comparable to the diagnostic assessment tools for ASD.

Application of Eye Tracking Technology in Medicine: A Bibliometric Analysis

“…Since the diagnosis of autism is challenging and no biomarker is available [ 51 ], the development of computational models based on early abnormalities such as the differences in gaze processing might be of substantial help to improve and anticipate the diagnosis, thus, making it possible to initiate treatment at an earlier stage, when it is most effective [ 52 ]. Eye tracking measurements that might prove to be useful as early biomarkers include dysregulations in pupil dilation [ 53 , 54 , 55 ], changes in saccadic behavior, differences in gaze patterns during vision exposure to social stimuli [ 56 , 57 , 58 ] and analysis of scan paths or gaze patterns [ 59 , 60 , 61 , 62 , 63 , 64 ]. Some studies combined eye tracking data with other measurements such as resting-state EEG data [ 65 , 66 , 67 ].…”

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

“…Gazefinder was used to create the best-fit diagnostic algorithm with an aim to distinguish school-aged and adolescent ASD individuals from TD individuals of the same age [10]. The diagnostic performance was evaluated in two ways: the first was an ML procedure known as the leave-one-out (LOO) method.…”

“…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].…”

“…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].…”