As early intervention is highly effective for young children with autism spectrum disorder (ASD), it is imperative to make accurate diagnosis as early as possible. ASD has often been associated with atypical visual attention and eye gaze data can be collected at a very early age. An automatic screening tool based on eye gaze data that could identify ASD risk offers the opportunity for intervention before the full set of symptoms is present. In this paper, we propose two machine learning methods, synthetic saccade approach and image based approach, to automatically classify ASD given children's eye gaze data collected from free-viewing tasks of natural images. The first approach uses a generative model of synthetic saccade patterns to represent the baseline scan-path from a typical non-ASD individual and combines it with the real scan-path as well as other auxiliary data as inputs to a deep learning classifier. The second approach adopts a more holistic image-based approach by feeding the input image and a sequence of fixation maps into a convolutional or recurrent neural network. Using a publicly-accessible collection of children's gaze data, our experiments indicate that the ASD prediction accuracy reaches 67.23% accuracy on the validation dataset and 62.13% accuracy on the test dataset.
|Journal||Signal Processing: Image Communication|
|State||Published - May 2021|
Bibliographical noteFunding Information:
Research reported in this publication was supported by the National Institute of Mental Health, United States of America of the National Institutes of Health, United States of America under award number R01MH121344-01 . Chongruo Wu was also supported by the UC Davis 2019 College of Engineering Dean’s Collaborative Research (DECOR) Award, United States of America . The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
© 2021 Elsevier B.V.
- Autism spectrum disorders
- Deep learning
- Eye gaze data
ASJC Scopus subject areas
- Signal Processing
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering