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Original Investigation
April 26, 2021

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

Author Affiliations
  • 1Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina
  • 2Department of Psychiatry and Behavioral Sciences, Duke University, Durham, North Carolina
  • 3Duke Center for Autism and Brain Development, Duke University, Durham, North Carolina
  • 4Department of Pediatrics, Duke University, Durham, North Carolina
  • 5Office of Information Technology, Duke University, Durham, North Carolina
  • 6Duke Global Health Institute, Duke University, Durham, North Carolina
  • 7Department of Psychology & Neuroscience, Duke University, Durham, North Carolina
  • 8Ecole Normale Supérieure Paris-Saclay, Cachan, France
  • 9Duke Center for Childhood Obesity Research, Duke University, Durham, North Carolina
  • 10Duke Institute for Brain Sciences, Duke University, Durham, North Carolina
  • 11Department of Biomedical Engineering, Mathematics, and Computer Sciences, Duke University, Durham, North Carolina
JAMA Pediatr. 2021;175(8):827-836. doi:10.1001/jamapediatrics.2021.0530
Key Points

Question  Using computational methods based on computer vision analysis, can a smartphone or tablet be used in real-world settings to reliably detect early symptoms of autism spectrum disorder?

Findings  In this study, a mobile device application deployed on a smartphone or tablet and used during a pediatric visit detected distinctive eye-gaze patterns in toddlers with autism spectrum disorder compared with typically developing toddlers, which were characterized by reduced attention to social stimuli and deficits in coordinating gaze with speech sounds.

Meaning  These methods may have potential for developing scalable autism screening tools, exportable to natural settings, and enabling data sets amenable to machine learning.

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 Participants  A 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 Measures  Computer 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 Relevance  The 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.

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