Enhancing autism spectrum disorder early detection with parent-child dyads block-play protocol and attention-enhanced hybrid deep learning framework

Abstract

Autism Spectrum Disorder (ASD) is a rapidly growing neurodevelopmental disorder. Early intervention is crucial for the development of young children with ASD, yet traditional clinical screening methods often lack objectivity. We introduce a novel Parent-Child Dyads Block-Play (PCB) protocol that captures distinct behavioral patterns in ASD toddlers during naturalistic interactions with their parents. This protocol systematically captures and quantifies parent–child interactions during the block-play task, providing a structured and naturalistic environment to observe ASD-relevant behaviors. Drawing on kinesiological and neuroscientific insights, our approach analyzes movement dynamics to reliably differentiate ASD from typically developing (TD) toddlers. In a dataset of 129 toddlers (40 ASD, 89 TD), we analyze the videos using a hybrid deep learning framework that integrates a two-stream graph convolution network (2sGCN) with an attention-enhanced extended long short-term memory (AxLSTM), enabling the capture of both spatial and temporal aspects of movement. Our 2sGCN-AxLSTM framework efficiently analyzes human dynamic behavioral patterns and is able to distinguish between ASD and typical developmental disorders with an unprecedented 89.6% accuracy. This high level of accuracy holds promise for practical clinical use, as it could facilitate timely interventions and potentially improve developmental outcomes. By focusing on real-life parent–child interactions, the proposed PCB protocol provides a valuable tool that can complement traditional assessments, facilitating timely interventions, and potentially improving developmental outcomes.