• Login
    View Item 
    •   Home
    • Computer Science
    • Faculty Research and Publications
    • Articles
    • View Item
    •   Home
    • Computer Science
    • Faculty Research and Publications
    • Articles
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of Effat University RepositoryCommunitiesPublication DateAuthorsTitlesSubjectsPublisherJournalTypeDepartmentThis CollectionPublication DateAuthorsTitlesSubjectsPublisherJournalTypeDepartmentProfilesView

    My Account

    Login

    Statistics

    Display statistics

    Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimag-ing: A review

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    2206.11233.pdf
    Size:
    1.663Mb
    Format:
    PDF
    Download
    Author
    Moridian, Parisa
    Ghassemi, Navid
    Jafari, Mahboobeh
    Salloum-Asfar, Salam
    Sadeghi, Delaram
    Khodatars, Marjane
    Shoeibi, Afshin
    Khosravi, Abbas
    Ling, Sai Ho
    Subasi, Abdulhamit cc
    Abdulla, Sara
    Alizadehsani, Roohallah
    Gorriz, Juan
    Acharya, U Rajendra
    Show allShow less
    Subject
    Autism Spectrum Disorder
    Magnetic Resonance Imaging
    Artificial Intelligence (AI)
    Machine Learning
    Date
    2022
    
    Metadata
    Show full item record
    Abstract
    Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and symptoms that appear in early childhood. ASD is also associated with communication deficits and repetitive behavior in affected individuals. Various ASD detection methods have been developed, including neuroimaging modalities and psychological tests. Among these methods, magnetic resonance imaging (MRI) imaging modalities are of paramount importance to physicians. Clinicians rely on MRI modalities to diagnose ASD accurately. The MRI modalities are non-invasive methods that include functional (fMRI) and structural (sMRI) neuroimaging methods. However, diagnosing ASD with fMRI and sMRI for specialists is often laborious and time-consuming; therefore, several computer-aided design systems (CADS) based on artificial intelligence (AI) have been developed to assist specialist physicians. Conventional machine learning (ML) and deep learning (DL) are the most popular schemes of AI used for diagnosing ASD. This study aims to review the automated detection of ASD using AI. We review several CADS that have been developed using ML techniques for the automated diagnosis of ASD using MRI modalities. There has been very limited work on the use of DL techniques to develop automated diagnostic models for ASD. A summary of the studies developed using DL is provided in the Supplementary Appendix. Then, the challenges encountered during the automated diagnosis of ASD using MRI and AI techniques are described in detail. Additionally, a graphical comparison of studies using ML and DL to diagnose ASD automatically is discussed. We suggest future approaches to detecting ASDs using AI techniques and MRI neuroimaging.
    Department
    Computer Science
    Journal title
    Frontiers in Molecular Neuroscience
    DOI
    https://doi.org/10.3389/fnmol.2022.999605
    ae974a485f413a2113503eed53cd6c53
    https://doi.org/10.3389/fnmol.2022.999605
    Scopus Count
    Collections
    Articles

    entitlement

     
    DSpace software (copyright © 2002 - 2023)  DuraSpace
    Quick Guide | Contact Us
    Open Repository is a service operated by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.