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Updated: Nov 8, 2025
Eye Tracking Young Children with Autism
Published on: March 27, 2012
PREDICTING AUTISM DIAGNOSIS USING IMAGE WITH FIXATIONS AND SYNTHETIC SACCADE PATTERNS
Chongruo Wu1, Sidrah Liaqat2, Sen-Ching Cheung2
1University of California, Davis.
Insights
Machine learning models can now detect autism spectrum disorder (ASD) risk in infants using eye-tracking data. Early identification through these AI tools may enable timely intervention for better outcomes.
Area of Science:
- Neuroscience
- Computer Science
- Developmental Psychology
Background:
- Autism spectrum disorder (ASD) signs appear early, but diagnosis is delayed, missing crucial intervention windows.
- Early intervention significantly improves outcomes for young children with ASD.
- A need exists for tools to identify ASD risk in infancy, enabling earlier support.
Purpose of the Study:
- To develop and evaluate machine learning methods for early ASD risk classification using infant scanpath data.
- To explore novel approaches for automatic ASD identification from visual attention patterns.
Main Methods:
- Two machine learning approaches were proposed: a synthetic saccade method and an image-based convolutional neural network method.
- Scanpath data from infants viewing natural images were used as input.
- The synthetic saccade approach incorporated generative models and auxiliary data for deep learning classification.
Main Results:
- The proposed methods achieved 65.41% accuracy in classifying ASD risk on a validation dataset.
- Both the synthetic saccade and image-based approaches demonstrated potential for automated ASD screening.
Conclusions:
- Machine learning, particularly using eye-tracking data, shows promise for early ASD risk detection in infants.
- These computational methods could facilitate earlier diagnosis and intervention, improving developmental trajectories for children with ASD.
Abstract:
Signs of autism spectrum disorder (ASD) emerge in the first year of life in many children, but diagnosis is typically made much later, at an average age of 4 years in the United States. Early intervention is highly effective for young children with ASD, but is typically reserved for children with a formal diagnosis, making accurate identification as early as possible imperative. A screening tool that could identify ASD risk during infancy 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 the scanpath data from children on free viewing 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 input scanpath 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 state-of-the-art convolutional neural network. Our experiments indicate that we can get 65.41% accuracy on the validation dataset.
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