  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.
