A Scalable Computational Approach to Assessing Response to Name in Toddlers with Autism

Background: This study is part of a larger research program focused on developing objective, scalable tools for digital behavioral phenotyping. We evaluated whether a digital app delivered on a smartphone or tablet using computer vision analysis (CVA) can elicit and accurately measure one of the most common early autism symptoms, namely failure to respond to a name call.

Methods: During a pediatric primary care well-child visit, 910 toddlers, 17-37 months old, were administered an app on an iPhone or iPad consisting of brief movies during which the child's name was called three times by an examiner standing behind them. Thirty-seven toddlers were subsequently diagnosed with autism spectrum disorder (ASD). Name calls and children's behavior were recorded by the camera embedded in the device, and children's head turns were coded by both CVA and a human.

Results: CVA coding of response to name was found to be comparable to human coding. Based on CVA, children with ASD responded to their name significantly less frequently than children without ASD. CVA also revealed that children with ASD who did orient to their name exhibited a longer latency before turning their head. Combining information about both the frequency and the delay in response to name improved the ability to distinguish toddlers with and without ASD.

Conclusions: A digital app delivered on an iPhone or iPad in real-world settings using computer vision analysis to quantify behavior can reliably detect a key early autism symptom-failure to respond to name. Moreover, the higher resolution offered by CVA identified a delay in head turn in toddlers with ASD who did respond to their name. Digital phenotyping is a promising methodology for early assessment of ASD symptoms.