Classifying autism in a clinical population based on motion synchrony: a proof-of-concept study using real-life diagnostic interviews.

ABSTRACT: Predictive modeling strategies are increasingly studied as a means to overcome clinical bottlenecks in the diagnostic classification of autism spectrum disorder. However, while some findings are promising in the light of diagnostic marker research, many of these approaches lack the scalability for adequate and effective translation to everyday clinical practice. In this study, our aim was to explore the use of objective computer vision video analysis of real-world autism diagnostic interviews in a clinical sample of children and young individuals in the transition to adulthood to predict diagnosis. Specifically, we trained a support vector machine learning model on interpersonal synchrony data recorded in Autism Diagnostic Observation Schedule (ADOS-2) interviews of patient-clinician dyads

SUBMITTER: Koehler JC 

PROVIDER: S-EPMC10920641 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature