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Related Experiment Video

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Comparing Eye-tracking Data of Children with High-functioning ASD, Comorbid ADHD, and of a Control Watching Social Videos
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Computer-aided autism diagnosis based on visual attention models using eye tracking.

Jessica S Oliveira1, Felipe O Franco2,3, Mirian C Revers2

  • 1School of Arts, Sciences and Humanities (EACH), University of Sao Paulo (USP), Sao Paulo, SP, 03828-000, Brazil.

Scientific Reports
|May 13, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel computational method using eye tracking data for non-invasive diagnosis. The approach enhances diagnostic accuracy by integrating visual attention models, image processing, and AI for autism spectrum disorder (ASD) identification.

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Area of Science:

  • Ophthalmology and Computer Science
  • Artificial Intelligence in Healthcare
  • Biomedical Signal Processing

Background:

  • Eye tracking is a non-invasive diagnostic tool applicable across various ages and functional levels.
  • Current computer-aided diagnosis relies on region of interest (ROI) demarcation, which can be limited by feature diversity and diagnostic accuracy.
  • Identifying relevant video frames and features for eye tracking analysis, particularly for autism spectrum disorder (ASD) diagnosis, remains challenging.

Purpose of the Study:

  • To develop a computational method integrating visual attention models, image processing, and AI for improved eye tracking-based diagnosis.
  • To create a supervised classifier utilizing learned models from eye tracking data for group differentiation (case vs. control).
  • To evaluate the proposed method's efficacy in the context of ASD diagnosis.

Main Methods:

  • A computational method was developed, combining Visual Attention Models, Image Processing, and Artificial Intelligence.
  • Machine learning models were trained for both case and control groups using eye tracking data.
  • A supervised classifier was implemented to perform diagnoses based on the learned models.

Main Results:

  • The method was tested for autism spectrum disorder (ASD) diagnosis.
  • Achieved an average precision of 90%.
  • Achieved an average recall of 69% and specificity of 93%.

Conclusions:

  • The proposed computational method offers a robust approach for eye tracking-based diagnosis.
  • The integration of visual attention, image processing, and AI enhances diagnostic capabilities.
  • The method demonstrates significant potential for aiding in the diagnosis of neurodevelopmental disorders like ASD.