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Investigation of Eye-Tracking Scan Path as a Biomarker for Autism Screening Using Machine Learning Algorithms.

Mujeeb Rahman Kanhirakadavath1,2, Monica Subashini Mohan Chandran3

  • 1School of Electronics Engineering, Vellore Institute of Technology, Vellore 632014, India.

Diagnostics (Basel, Switzerland)
|February 25, 2022
PubMed
Summary

Eye-tracking data can help screen for autism spectrum disorder (ASD) in children. Machine learning models, particularly deep neural networks, accurately predict ASD using visual scan path data, aiding early detection.

Keywords:
ASD screeningautism spectrum disorderconvolutional neural network (CNN)eye-tracking scan path imagesmachine learning

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Area of Science:

  • Neuroscience
  • Developmental Psychology
  • Computer Science

Background:

  • Autism spectrum disorder (ASD) is characterized by social, communication, and behavioral challenges.
  • Children with ASD often exhibit atypical visual attention and processing of social stimuli.
  • Early identification of ASD is crucial for timely intervention and support.

Purpose of the Study:

  • To evaluate the efficacy of eye-tracking data and machine learning for early autism screening.
  • To identify the optimal machine learning model for predicting ASD from eye-tracking scan path images.
  • To explore the potential of eye-tracking as an objective biomarker for ASD.

Main Methods:

  • Utilized a publicly available dataset of 547 eye-tracking scan paths from 328 typically developing and 219 autistic children.
  • Applied image augmentation techniques to enhance the dataset and mitigate overfitting.
  • Compared three traditional machine learning models against a deep neural network classifier.

Main Results:

  • The deep neural network model demonstrated superior performance in predicting ASD.
  • Achieved high performance metrics: 97% AUC, 93.28% sensitivity, 91.38% specificity, 94.46% NPV, and 90.06% PPV (fivefold cross-validated).
  • Eye-tracking scan path visualization proved effective for ASD prediction.

Conclusions:

  • Eye-tracking data, analyzed with machine learning, shows significant promise for early autism screening.
  • The deep neural network approach offers a reliable and efficient method for identifying potential ASD cases.
  • This technology can serve as a valuable tool for clinicians in supporting early diagnosis and intervention for autism spectrum disorder.