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

Composite decoupling of temperature and strain in FBG sensors using physics-informed neural network (PINN).

Optics express·2026

Same author

Denoising algorithm of Φ-OTDR systems based on adaptive fractional wavelet transform denoising.

Optics express·2026

Same author

LncRNA SOX21-AS1 is associated with poor prognosis and immunomodulation in glioblastoma.

Translational cancer research·2026

Same author

Genome-Wide Association Study Uncovers Candidate Genes Governing Oil Quality Traits in Sunflower (<i>Helianthus annuus</i> L.).

Plants (Basel, Switzerland)·2026

Same author

ANS-coded high-ratio data compression for a distributed acoustic sensing system.

Optics express·2025

Same author

Mechanism of Shenzhu components in alleviating chemotherapy-induced diarrhea via mitochondria-associated endoplasmic reticulum membranes (MAMs) regulation.

Journal of ethnopharmacology·2025

Search research articles

Related Experiment Video

Updated: Sep 27, 2025

Eye Tracking Young Children with Autism

Eye Tracking Young Children with Autism

Published on: March 27, 2012

Identifying Visual Attention Features Accurately Discerning Between Autism and Typically Developing: a Deep Learning

Jin Xie^1,2, Longfei Wang¹, Paula Webster³

¹The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, 518055, China.

Interdisciplinary Sciences, Computational Life Sciences

|April 13, 2022

Summary

This summary is machine-generated.

Researchers identified novel visual attention patterns in autism spectrum disorder (ASD) using deep learning. This framework accurately distinguishes individuals with ASD, revealing new attention features like food and outdoor objects.

Keywords:

Autism spectrum disorder Deep learning Eye movement Visual attention

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

Simultaneous Eye Tracking and Single-Neuron Recordings in Human Epilepsy Patients

Simultaneous Eye Tracking and Single-Neuron Recordings in Human Epilepsy Patients

Published on: June 17, 2019

Related Experiment Videos

Last Updated: Sep 27, 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

Simultaneous Eye Tracking and Single-Neuron Recordings in Human Epilepsy Patients

Simultaneous Eye Tracking and Single-Neuron Recordings in Human Epilepsy Patients

Published on: June 17, 2019

Area of Science:

Neuroscience
Computer Science
Developmental Psychology

Background:

Atypical visual attention is a key characteristic of autism spectrum disorder (ASD).
Accurately identifying individual-level attention features to differentiate between ASD and typically developing (TD) individuals remains difficult.

Purpose of the Study:

To develop a systematic framework for identifying discriminative visual attention features in ASD.
To achieve high accuracy in classifying individuals with ASD using deep learning models.
To uncover novel attention features beyond previously known ones.

Main Methods:

Developed a novel deep learning framework combining classification, segmentation, and image ablation.
Utilized a two-stream model achieving state-of-the-art performance.
Employed a direct measurement of classification ability to identify key features.

Main Results:

The two-stream model achieved a classification accuracy of 0.95.
Identified 'Food & drink' and 'Outdoor-objects' as new discriminative attention features, alongside 'Center-object' and 'Human-faces'.
A combined set of top-9 features yielded an AUC of 0.92 for individual-level classification.
A small dataset (12 images) achieved an AUC of 0.86, indicating potential for efficient diagnosis.

Conclusions:

The deep learning framework based on VGG-16 effectively recognizes and interprets abnormal visual attention in ASD.
The identified features, including novel categories, enhance understanding of ASD-related behaviors.
This approach offers a powerful tool for identifying biomarkers and facilitating clinical diagnosis of ASD.