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In spite of recent advances in the genetics and neuroscience of early childhood mental health, behavioral observation is still the gold standard in screening, diagnosis, and outcome assessment. Unfortunately, clinical observation is often subjective, needs significant rater training, does not capture data from participants in their natural environment, and is not scalable for use in large populations or for longitudinal monitoring. To address these challenges, we developed and tested a self-contained app designed to measure toddlers' social communication behaviors in a primary care, school, or home setting. Twenty 16-30 month old children with and without autism participated in this study. Toddlers watched the developmentally-appropriate visual stimuli on an iPad in a pediatric clinic and in our lab while the iPad camera simultaneously recorded video of the child's behaviors. Automated computer vision algorithms coded emotions and social referencing to quantify autism risk behaviors. We validated our automatic computer coding by comparing the computer-generated analysis of facial expression and social referencing to human coding of these behaviors. We report our method and propose the development and testing of measures of young children's behaviors as the first step toward development of a novel, fully integrated, low-cost, scalable screening tool for autism and other neurodevelopmental disorders of early childhood.

A scalable app for measuring autism risk behaviors in young children: A technical validity and feasibility study