Deep learning algorithms for autism spectrum disorder detection using eye-tracking patterns

Abstract

Early diagnosis of autism spectrum disorder (ASD) plays a crucial role in facilitating prompt interventions and assessing post-therapy progress, both of which can greatly improve developmental trajectories. The emergence of artificial intelligence—particularly deep learning—has opened new possibilities for clinicians to detect ASD with improved precision and speed. This study investigates the utilization of deep learning models to recognize ASD among 13-year-old children based on eye movement data collected as participants observed static images and short video sequences. The dataset included visual and numerical variables, such as gaze position and pupil diameter, allowing for a multimodal analytical approach. For the numerical dataset, a multilayer perceptron (MLP) neural network produced the best outcomes, yielding an accuracy of 91.7% and a recall rate of 83.3% in ASD classification. Meanwhile, the Vision Transformer (ViT) model performed best for image-based analysis, reaching an accuracy of 78.2% and a recall rate of 88.6%. Overall, the findings highlight the potential of deep learning frameworks as objective, data-driven tools for ASD detection in both clinical and research contexts.Key words: Autism, Computer Vision, Deep Learning Model, Vision Transformer.