Deep AI model for autism detection using naturalistic behavioral videos

Profound heterogeneity in Autism Spectrum Disorder (ASD) complicates diagnosis and the development of effective treatments. In healthcare systems with limited specialist resources, the need for rapid and accessible screening tools is particularly urgent. In the present study, we developed an objective, scalable pipeline that pairs a simple two-minute video recording of a childs naturalistic behavior with a deep AI model for autism detection, representing the first such rapid, scalable framework. By analyzing a rich spectrum of childrens responses to social stimuli, such as subtle behavioral patterns, gaze dynamics, facial morphology, and dynamic facial complexity, the deep AI model provides powerful support for clinical workflows, demonstrating high accuracy in identifying ASD risk across diverse internal and external test cohorts, irrespective of sex, age, or cognitive function. Furthermore, a series of comprehensive analyses confirmed the models clinical relevance and revealed its capacity to objectively stratify ASD heterogeneity into neurobiologically distinct subtypes. This work establishes a highly efficient and objective framework for large-scale screening, providing a data-driven foundation to stratify heterogeneity and paving the way for the future development of targeted interventions.