336 research outputs found
Automatic Autism Spectrum Disorder Detection Using Artificial Intelligence Methods with MRI Neuroimaging: A Review
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, the process of 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 the 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
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 conclude by suggesting future approaches to detecting ASDs
using AI techniques and MRI neuroimaging
Contributions to the study of Austism Spectrum Brain conectivity
164 p.Autism Spectrum Disorder (ASD) is a largely prevalent neurodevelopmental condition with a big social and economical impact affecting the entire life of families. There is an intense search for biomarkers that can be assessed as early as possible in order to initiate treatment and preparation of the family to deal with the challenges imposed by the condition. Brain imaging biomarkers have special interest. Specifically, functional connectivity data extracted from resting state functional magnetic resonance imaging (rs-fMRI) should allow to detect brain connectivity alterations. Machine learning pipelines encompass the estimation of the functional connectivity matrix from brain parcellations, feature extraction and building classification models for ASD prediction. The works reported in the literature are very heterogeneous from the computational and methodological point of view. In this Thesis we carry out a comprehensive computational exploration of the impact of the choices involved while building these machine learning pipelines
Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review
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.Qatar National
Librar
Multi-modal Multi-kernel Graph Learning for Autism Prediction and Biomarker Discovery
Due to its complexity, graph learning-based multi-modal integration and
classification is one of the most challenging obstacles for disease prediction.
To effectively offset the negative impact between modalities in the process of
multi-modal integration and extract heterogeneous information from graphs, we
propose a novel method called MMKGL (Multi-modal Multi-Kernel Graph Learning).
For the problem of negative impact between modalities, we propose a multi-modal
graph embedding module to construct a multi-modal graph. Different from
conventional methods that manually construct static graphs for all modalities,
each modality generates a separate graph by adaptive learning, where a function
graph and a supervision graph are introduced for optimization during the
multi-graph fusion embedding process. We then propose a multi-kernel graph
learning module to extract heterogeneous information from the multi-modal
graph. The information in the multi-modal graph at different levels is
aggregated by convolutional kernels with different receptive field sizes,
followed by generating a cross-kernel discovery tensor for disease prediction.
Our method is evaluated on the benchmark Autism Brain Imaging Data Exchange
(ABIDE) dataset and outperforms the state-of-the-art methods. In addition,
discriminative brain regions associated with autism are identified by our
model, providing guidance for the study of autism pathology
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