Identification of ASD via Graph Convolutional Network with Visual Semantic Encoding of Saccade

Atypical eye movement is one of the critical symptoms of autism spectrum disorder (ASD). Automatic quantification of eye-tracking data can provide an objective, convenient, and non-invasive way to identify subjects with ASD, which can develop scalable screening tools for ASD to apply in areas with limited medical resources. However, existing eye-tracking-based ASD classification models usually calculated the score under each image separately and averaged the scores under different images in a post-processing manner to achieve ASD recognition. Determining how to utilize all eye-tracking data of each subject to globally integrate perceptual information and establish a subject-based visual preference for ASD screening is still an unresolved challenge. To address this issue, we propose a novel ASD screening model based on global visual preference encoding. First, we utilize the segment anything model (SAM) and vision transformer (ViT) to extract semantic label regions from all test images. Then, we establish a personalized visual preference graph for each subject based on the saccadic shifts between different semantic regions. Finally, we apply a graph convolutional network (GCN) to learn the mapping between the visual preference graph and classification labels for ASD recognition. In the experiment, we recruited 28 children with ASD and 30 typically developing (TD) children between 2 and 8 years of age to record their eye-tracking data under 220 test images from four types. The experimental results have shown that the proposed model can outperform the state-of-the-art eye-tracking-based ASD recognition models. Furthermore, the evaluation results have indicated the potential to extend the proposed model to other eye-tracking applications, resulting in progress in visual research, accessibility, and healthcare.