Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Autism Spectrum Disorder

Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by persistent deficits in social communication and interaction alongside restrictive and repetitive behaviors or interests. ASD is sometimes accompanied by intellectual impairment.
These core symptoms manifest differently among individuals, ranging from mild to severe. The disorder's complexity extends beyond its clinical presentation, encompassing a diverse range of biological, cognitive, and sociocultural influences.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Psychological Impact of Autism Screening on Caregivers.

Autism research : official journal of the International Society for Autism Research·2026

Same author

Use of Machine Learning to Identify Markers of Risk for Fragile X-Associated Tremor/Ataxia Syndrome: A Preliminary Analysis.

Annals of neurology·2026

Same author

Cross-disciplinary methodologies for whole-person research - insights from EMPOWER2024.

Npj imaging·2026

Same author

Clinical and pathologic correlations of machine learning quantification of Aβ deposits across 3 brain regions of decedents with Alzheimer disease.

Journal of neuropathology and experimental neurology·2026

Same author

Repetitive Motor Behaviors in Non-Autistic Toddlers.

The Journal of pediatrics·2026

Same author

Basic Science and Pathogenesis.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2025

Search research articles

Related Experiment Video

Updated: Nov 8, 2025

Eye Tracking Young Children with Autism

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.

... IEEE International Conference on Multimedia and Expo Workshops. IEEE International Conference on Multimedia and Expo

|April 28, 2021

Summary

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.

Keywords:

Autism Spectrum Disorders Deep Learning Visual Saliency

More Related Videos

Comparing Eye-tracking Data of Children with High-functioning ASD, Comorbid ADHD, and of a Control Watching Social Videos

Comparing Eye-tracking Data of Children with High-functioning ASD, Comorbid ADHD, and of a Control Watching Social Videos

Published on: December 7, 2018

Using the Visual World Paradigm to Study Sentence Comprehension in Mandarin-Speaking Children with Autism

Using the Visual World Paradigm to Study Sentence Comprehension in Mandarin-Speaking Children with Autism

Published on: October 3, 2018

Related Experiment Videos

Last Updated: Nov 8, 2025

Eye Tracking Young Children with Autism

Eye Tracking Young Children with Autism

Published on: March 27, 2012

Comparing Eye-tracking Data of Children with High-functioning ASD, Comorbid ADHD, and of a Control Watching Social Videos

Comparing Eye-tracking Data of Children with High-functioning ASD, Comorbid ADHD, and of a Control Watching Social Videos

Published on: December 7, 2018

Using the Visual World Paradigm to Study Sentence Comprehension in Mandarin-Speaking Children with Autism

Using the Visual World Paradigm to Study Sentence Comprehension in Mandarin-Speaking Children with Autism

Published on: October 3, 2018

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.