Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Learning Disabilities01:25

Learning Disabilities

576
Learning disabilities are cognitive disorders caused by neurological impairments that affect cognitive functions like language and reading, without indicating overall intellectual or developmental challenges. These disabilities differ from global intellectual or developmental disabilities as they are limited to distinct cognitive functions. Common learning disabilities include dysgraphia, dyslexia, and dyscalculia, each of which impacts unique aspects of learning.
Dyslexia
Dyslexia is a...
576
Autism Spectrum Disorder01:19

Autism Spectrum Disorder

987
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.
987

You might also read

Related Articles

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

Sort by
Same author

An Adaptive Transfer Learning Framework for Multimodal Autism Spectrum Disorder Diagnosis.

Life (Basel, Switzerland)·2025
Same author

RETRACTED: A deep learning-based ensemble for autism spectrum disorder diagnosis using facial images.

PloS one·2025
Same author

End-to-End Deep Learning Method for Detection of Invasive Parkinson's Disease.

Diagnostics (Basel, Switzerland)·2023
Same author

Diabetic Retinopathy Detection from Fundus Images of the Eye Using Hybrid Deep Learning Features.

Diagnostics (Basel, Switzerland)·2022
Same author

Automatic Detection of Liver Cancer Using Hybrid Pre-Trained Models.

Sensors (Basel, Switzerland)·2022
Same author

A Novel Binary Hybrid PSO-EO Algorithm for Cryptanalysis of Internal State of RC4 Cipher.

Sensors (Basel, Switzerland)·2022
Same journal

Correction: Luca et al. Global and Regional Diagnostic Results of Progress Toward Cervical Cancer Elimination, According to the WHO Strategy: A Systematic Literature Review with Narrative Synthesis. <i>Diagnostics</i> 2026, <i>16</i>, 1224.

Diagnostics (Basel, Switzerland)·2026
Same journal

Association Between Systemic Inflammatory Response Biomarkers and Disease Activity in Systemic Lupus Erythematosus: A Multi-Center Retrospective Study.

Diagnostics (Basel, Switzerland)·2026
Same journal

Vertebrogenic Low Back Pain and Basivertebral Nerve Ablation: A Review of Mechanisms, Imaging-Driven Selection, and Clinical Outcomes.

Diagnostics (Basel, Switzerland)·2026
Same journal

Multivalvular Carcinoid Heart Disease: The Role of Echocardiography in Diagnosis and Selection for Heterotopic Bicaval Valve Implantation.

Diagnostics (Basel, Switzerland)·2026
Same journal

Data-Efficient and Explainable Multimodal Survival Prediction in NSCLC Using Deep Image Embeddings, Clinical Variables, and Gradient-Boosted Trees.

Diagnostics (Basel, Switzerland)·2026
Same journal

Anomalous Left Coronary Artery from the Pulmonary Artery: Cinematic Volume Rendering Technique for Enhanced Anatomic Visualization.

Diagnostics (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Jan 18, 2026

Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.7K

An Explainable Deep Learning Framework for Multimodal Autism Diagnosis Using XAI GAMI-Net and Hypernetworks.

Wajeeha Malik1, Muhammad Abuzar Fahiem1, Tayyaba Farhat2

  • 1Department of Computer Science, Lahore College for Women University, Lahore 54500, Pakistan.

Diagnostics (Basel, Switzerland)
|September 13, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel deep learning framework for Autism Spectrum Disorder (ASD) diagnosis, combining behavioral and neuroimaging data for highly accurate, personalized identification. The interpretable model significantly improves diagnostic accuracy, aiding clinicians in identifying ASD patterns.

Keywords:
Autism Spectrum Disorder (ASD)Convolutional Neural Network (CNN)Explainable artificial intelligence (XAI)GAMI-NetGraph Neural Network (GNN)Hypernetworks

More Related Videos

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
11:14

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

11.4K

Related Experiment Videos

Last Updated: Jan 18, 2026

Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.7K
A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
11:14

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

11.4K

Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Autism Spectrum Disorder (ASD) presents complex diagnostic challenges due to heterogeneous behavioral and neurological patterns.
  • Current ASD diagnosis relies on skilled professionals and thorough examinations, which can be time-consuming and subject to individual expertise.
  • Deep learning offers potential to enhance ASD diagnosis by automating the identification and classification of behavioral and neuroimaging patterns.

Purpose of the Study:

  • To develop a novel multimodal diagnostic framework for Autism Spectrum Disorder (ASD).
  • To integrate structured behavioral phenotypes and structural magnetic resonance imaging (sMRI) data into an interpretable and personalized system.
  • To improve the accuracy and efficiency of ASD diagnosis through advanced machine learning techniques.

Main Methods:

  • A Generalized Additive Model with Interactions (GAMI-Net) was employed for transparent embedding of clinical behavioral phenotypes.
  • A hybrid Convolutional Neural Network-Graph Neural Network (CNN-GNN) model extracted structural brain characteristics from sMRI data.
  • An Autoencoder fused cross-modal embeddings into a common latent space, followed by a Hyper Network-based MLP classifier for personalized classification.

Main Results:

  • The multimodal system achieved high diagnostic accuracy on a held-out test set from the ABIDE-I dataset (99.40% accuracy, 99.99% ROC-AUC).
  • Generalizability testing via five-fold cross-validation demonstrated robust performance (98.56% mean accuracy, 99.62% ROC-AUC).
  • The framework successfully combined behavioral and neuroimaging data for precise ASD classification.

Conclusions:

  • Interpretable and personalized multimodal fusion shows significant promise in aiding clinicians with accurate ASD diagnosis.
  • The developed framework offers a powerful tool for enhancing diagnostic capabilities in clinical practice.
  • Further validation on larger, multi-site datasets is recommended to ensure robustness across diverse populations.