Identification of social engagement indicators associated with autism spectrum disorder using a game-based mobile application

ObjectiveAutism spectrum disorder (ASD) is a widespread neurodevelopmental condition with a range of potential causes and symptoms. Children with ASD exhibit behavioral and social impairments, giving rise to the possibility of utilizing computational techniques to evaluate a childs social phenotype from home videos. MethodsHere, we use a mobile health application to collect over 11 hours of video footage depicting 95 children engaged in gameplay in a natural home environment. We utilize automated dataset annotations to analyze two social indicators that have previously been shown to differ between children with ASD and their neurotypical (NT) peers: (1) gaze fixation patterns and (2) visual scanning methods. We compare the gaze fixation and visual scanning methods utilized by children during a 90-second gameplay video in order to identify statistically-significant differences between the two cohorts; we then train an LSTM neural network in order to determine if gaze indicators could be predictive of ASD. ResultsOur work identifies one statistically significant region of fixation and one significant gaze transition pattern that differ between our two cohorts during gameplay. In addition, our deep learning model demonstrates mild predictive power in identifying ASD based on coarse annotations of gaze fixations. DiscussionUltimately, our results demonstrate the utility of game-based mobile health platforms in quantifying visual patterns and providing insights into ASD. We also show the importance of automated labeling techniques in generating large-scale datasets while simultaneously preserving the privacy of participants. Our approaches can generalize to other healthcare needs.