Abstract
The current standard of care for ASD diagnosis requires the administration of structured assessment protocols by certified professionals, a process that can take several hours to complete. The waiting time has grown to an average of 14 months, leaving many children without access to treatment. To ameliorate these limitations, a scalable and mobile ASD diagnostic tool is necessary. We hypothesized that machine-learning classification using features extracted from short home videos of children in their natural environments can accurately detect ASD in minutes remotely.
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