Early identification of autism spectrum disorder based on machine learning with eye-tracking data.

ABSTRACT

BACKGROUND: Early identification of autism spectrum disorder (ASD) improves long-term outcomes, yet significant diagnostic delays persist. METHODS: A retrospective cohort of 449 children (ASD 246, typically developing [TD] 203) was used for model development. Eye-movement data were collected from the participants watching videos that featured eye-tracking paradigms for assessing social and non-social cognition. Five machine learning algorithms, namely random forest, support vector machine, logistic regression, artificial neural network, and extreme gradient boosting, were trained to classify children with ASD and TD. The best-performing algorithm was selected to build the final model which was further evaluated in a prospective cohort of 80 children. The Shapley values interpreted important eye-tracking features. RESULTS: Random forest outperformed other algorithms during model development and achieved an area under the curve of 0.849 (< 3 years 0.832, ≥ 3 years 0.868) on the external validation set. Of the ten most important eye-tracking features, three measured social cognition, and the rest were related to non-social cognition. A deterioration in model performance was observed using only the social or non-social cognition-related eye-tracking features. LIMITATIONS: The sample size of this study, although larger than that of existing studies of ASD based on eye-tracking data, was still relatively small compared to the number of features. CONCLUSIONS: Machine learning models based on eye-tracking data have the potential to be cost- and time-efficient digital tools for the early identification of ASD. Eye-tracking phenotypes related to social and non-social cognition play an important role in distinguishing children with ASD from TD children.