Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review
Metadatos
Mostrar el registro completo del ítemEditorial
Frontiers
Materia
ASD diagnosis Neuroimaging MRI modalities Machine learning Deep learning Inteligencia artificial Artificial intelligence
Fecha
2022-10-04Referencia bibliográfica
Moridian P... [et al.] (2022) Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review. Front. Mol. Neurosci. 15:999605. doi: [10.3389/fnmol.2022.999605]
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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.