Computational Methods to Measure Patterns of Gaze in Toddlers With Autism Spectrum Disorder

Abstract

IMPORTANCE Atypical eye gaze is an early-emerging symptom of autism spectrum disorder (ASD) and holds promise for autism screening. Current eye-tracking methods are expensive and require special equipment and calibration. There is a need for scalable, feasible methods for measuring eye gaze.OBJECTIVE Using computational methods based on computer vision analysis, we evaluated whether an app deployed on an iPhone or iPad that displayed strategically designed brief movies could elicit and quantify differences in eye-gaze patterns of toddlers with ASD vs typical development. DESIGN, SETTING, AND PARTICIPANTSA prospective study in pediatric primary care clinics was conducted from December 2018 to March 2020, comparing toddlers with and without ASD. Caregivers of 1564 toddlers were invited to participate during a well-child visit. A total of 993 toddlers (63%) completed study measures. Enrollment criteria were aged 16 to 38 months, healthy, English-or Spanish-speaking caregiver, and toddler able to sit and view the app. Participants were screened with the Modified Checklist for Autism in Toddlers-Revised With Follow-up during routine care. Children were referred by their pediatrician for diagnostic evaluation based on results of the checklist or if the caregiver or pediatrician was concerned. Forty toddlers subsequently were diagnosed with ASD.EXPOSURES A mobile app displayed on a smartphone or tablet. MAIN OUTCOMES AND MEASURESComputer vision analysis quantified eye-gaze patterns elicited by the app, which were compared between toddlers with ASD vs typical development.RESULTS Mean age of the sample was 21.1 months (range, 17.1-36.9 months), and 50.6% were boys, 59.8% White individuals, 16.5% Black individuals, 23.7% other race, and 16.9% Hispanic/Latino individuals. Distinctive eye-gaze patterns were detected in toddlers with ASD, characterized by reduced gaze to social stimuli and to salient social moments during the movies, and previously unknown deficits in coordination of gaze with speech sounds. The area under the receiver operating characteristic curve discriminating ASD vs non-ASD using multiple gaze features was 0.90 (95% CI, 0.82-0.97). CONCLUSIONS AND RELEVANCEThe app reliably measured both known and new gaze biomarkers that distinguished toddlers with ASD vs typical development. These novel results may have potential for developing scalable autism screening tools, exportable to natural settings, and enabling data sets amenable to machine learning.

Deep Learning Approach for Screening Autism Spectrum Disorder in Children with Facial Images and Analysis of Ethnoracial Factors in Model Development and Application

“…The standard approaches to diagnosing autism spectrum disorder (ASD) evaluate between 20 and 100 behaviors and take several hours to complete [ 39 ]. To make this approach easier and faster, several researchers reported using videos with machine learning to accelerate and automate the process [ 39 , 40 , 41 , 42 ]. These proposed video-based approaches use tablets or other devices that can capture the child’s behaviors, for example, eye gaze, or responses to stimuli, while the child is watching the specially designed movie clips or engaging in activities.…”

Application of Eye Tracking Technology in Medicine: A Bibliometric Analysis

“…This finding might be explained by the fact that, in the last few years, a number of studies have started to explore the utility of eye tracking markers to improve the diagnosis of autism spectrum disorders. While different pipelines to analyze eye tracking data are available, increasing attention is now focused on the development of visual attention models using machine learning methods [ 46 , 47 , 48 , 49 , 50 ]. 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 ].…”

Organizing the Methodological Toolbox: Lessons Learned From Implementing Developmental Methods Online

“…Of course, in-person research also opens up a host of additional methodological possibilities, like neuroimaging, pupillometry, and eye-tracking, that simply can’t be done online with current tools. The methods that can be used for online research may also expand as new technology is developed: while it’s unlikely that we’ll ever be able to do remote fMRI, PET, MEG, or EEG studies, there is online eye-tracking for adult participants 13 , and developmental applications are currently under investigation, with some recent successes using offline analyses of videos to get fixation data (e.g., Chouinard et al, 2018 ; Chang et al, 2021 ).…”