Predicting Core Characteristics of ASD Through Facial Emotion Recognition and Eye Tracking in Youth
Ming Jiang, Sunday M. Francis, Angela Tseng, Diksha Srishyla, Megan DuBois, Katie Beard, Christine A. Conelea, Qi Zhao, Suma Jacob
Abstract
Autism Spectrum Disorder (ASD) is a heterogeneous neurodevelopmental disorder (NDD) with a high rate of comorbidity. The implementation of eye-tracking methodologies has informed behavioral and neurophysiological patterns of visual processing across ASD and comorbid NDDs. In this study, we propose a machine learning method to predict measures of two core ASD characteristics: impaired social interactions and communication, and restricted, repetitive, and stereotyped behaviors and interests. Our method extracts behavioral features from task performance and eye-tracking data collected during a facial emotion recognition paradigm. We achieved high regression accuracy using a Random Forest regressor trained to predict scores on the SRS-2 and RBS-R assessments; this approach may serve as a classifier for ASD diagnosis.