Context-Based Screening of Autism Risk in Children

Autism Spectrum Disorder (ASD) is a neurode-velopmental condition characterized by difficulties in social communication, restricted interests, and repetitive behaviors. ASD approximately affects 1 in 36 children, however, most children are not formally diagnosed until they are four or five years old, significantly delaying early intervention. Traditional diagnostic methods for autism are often time-consuming, costly, and prone to subjective bias. Therefore, automatic methods for ASD screening and diagnosis are vital as they can help professionals to make earlier and more accurate diagnosis. Considering this, we present a context-based approach for screening autism risk in children. Our method uses a vision transformer to analyze facial data from the publicly available USF ASD dataset. The results are encouraging showing that context is important for the screening of autism in children. Empirical evaluation shows the enhanced performance of our model in accurately predicting autism risk levels, as evaluated under the RITA-T scoring criteria. We also perform a qualitative analysis using Grad-Cam visualizations, providing insights into the classification results.