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Digital Behavioral Phenotyping Detects Atypical Pattern of Facial Expression in Toddlers with Autism.

Authors
  • Carpenter, Kimberly L H1
  • Hahemi, Jordan1, 2
  • Campbell, Kathleen1, 3
  • Lippmann, Steven J4
  • Baker, Jeffrey P5
  • Egger, Helen L1, 6
  • Espinosa, Steven1, 2
  • Vermeer, Saritha1
  • Sapiro, Guillermo7
  • Dawson, Geraldine1, 8
  • 1 Duke Center for Autism and Brain Development, Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, North Carolina, USA.
  • 2 Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina, USA.
  • 3 Department of Pediatrics, University of Utah, Salt Lake City, Utah, USA.
  • 4 Department of Population Health Sciences, Duke University School of Medicine, Durham, North Carolina, USA.
  • 5 Department of Pediatrics, Duke University School of Medicine, Durham, North Carolina, USA.
  • 6 NYU Langone Child Study Center, New York University, New York, New York, USA.
  • 7 Departments of Biomedical Engineering Computer Science, and Mathematics, Duke University, Durham, North Carolina, USA.
  • 8 Duke Institute for Brain Sciences, Duke University, Durham, North Carolina, USA.
Type
Published Article
Journal
Autism Research
Publisher
Wiley (John Wiley & Sons)
Publication Date
Sep 14, 2020
Identifiers
DOI: 10.1002/aur.2391
PMID: 32924332
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Commonly used screening tools for autism spectrum disorder (ASD) generally rely on subjective caregiver questionnaires. While behavioral observation is more objective, it is also expensive, time-consuming, and requires significant expertise to perform. As such, there remains a critical need to develop feasible, scalable, and reliable tools that can characterize ASD risk behaviors. This study assessed the utility of a tablet-based behavioral assessment for eliciting and detecting one type of risk behavior, namely, patterns of facial expression, in 104 toddlers (ASD N = 22) and evaluated whether such patterns differentiated toddlers with and without ASD. The assessment consisted of the child sitting on his/her caregiver's lap and watching brief movies shown on a smart tablet while the embedded camera recorded the child's facial expressions. Computer vision analysis (CVA) automatically detected and tracked facial landmarks, which were used to estimate head position and facial expressions (Positive, Neutral, All Other). Using CVA, specific points throughout the movies were identified that reliably differentiate between children with and without ASD based on their patterns of facial movement and expressions (area under the curves for individual movies ranging from 0.62 to 0.73). During these instances, children with ASD more frequently displayed Neutral expressions compared to children without ASD, who had more All Other expressions. The frequency of All Other expressions was driven by non-ASD children more often displaying raised eyebrows and an open mouth, characteristic of engagement/interest. Preliminary results suggest computational coding of facial movements and expressions via a tablet-based assessment can detect differences in affective expression, one of the early, core features of ASD. LAY SUMMARY: This study tested the use of a tablet in the behavioral assessment of young children with autism. Children watched a series of developmentally appropriate movies and their facial expressions were recorded using the camera embedded in the tablet. Results suggest that computational assessments of facial expressions may be useful in early detection of symptoms of autism. © 2020 International Society for Autism Research and Wiley Periodicals LLC.

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