A naturalistic, non-invasive method for capturing biometric data during autism evaluations (opens in new tab)

IntroductionThis study evaluated a machine learning tool designed to non-intrusively quantify and analyze biometric data of gaze, facial expressions, and paralinguistic social communication features during standardized autism observational assessments. The primary aim was to assess the diagnostic accuracy of this multimodal tool in capturing key social communication features of autism in a diverse neurodevelopmental disabilities cohort and neurotypical (NT) cohort, ages 2-12.MethodsThe study ...