Temporal dynamics of early child-clinician prosodic synchrony predict one year autism intervention outcomes using AI driven affective computing.

ABSTRACT: The patient-therapist interpersonal dynamics is a cornerstone of psychotherapy, yet how it shapes clinical outcomes remains underexplored and difficult to quantify. This is also true in autism, where interpersonal interplay is recognized as an active element of intervention. Moreover, behavioral research is time-consuming and labor-intensive, limiting its translational applications. We studied 25 autistic preschoolers (17 therapists) across two naturalistic 60-minute sessions of developmental intervention at baseline and after three months (50 videos total). Clinical outcomes were assessed at baseline and one year into intervention. We developed a fully automated pipeline combining deep learning and affective computing to: (i) segment full-session audio recordings, (ii) model child-clinici

SUBMITTER: Bertamini G 

PROVIDER: S-EPMC12375780 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature