Machine Learning Methods for Diagnosing Autism Spectrum Disorder and Attention- Deficit/Hyperactivity Disorder Using Functional and Structural MRI: A Survey.
Attention Deficit and Hyperactivity Disorder (ADHD) classification
Autism Spectrum Disorder (ASD) classification
deep learning
diagnosis
fMRI
machine learning
sMRI
survey
Journal
Frontiers in neuroinformatics
ISSN: 1662-5196
Titre abrégé: Front Neuroinform
Pays: Switzerland
ID NLM: 101477957
Informations de publication
Date de publication:
2020
2020
Historique:
received:
24
06
2020
accepted:
07
12
2020
entrez:
8
2
2021
pubmed:
9
2
2021
medline:
9
2
2021
Statut:
epublish
Résumé
Here we summarize recent progress in machine learning model for diagnosis of Autism Spectrum Disorder (ASD) and Attention-deficit/Hyperactivity Disorder (ADHD). We outline and describe the machine-learning, especially deep-learning, techniques that are suitable for addressing research questions in this domain, pitfalls of the available methods, as well as future directions for the field. We envision a future where the diagnosis of ASD, ADHD, and other mental disorders is accomplished, and quantified using imaging techniques, such as MRI, and machine-learning models.
Identifiants
pubmed: 33551784
doi: 10.3389/fninf.2020.575999
pmc: PMC7855595
doi:
Types de publication
Journal Article
Review
Langues
eng
Pagination
575999Subventions
Organisme : NIGMS NIH HHS
ID : R01 GM134384
Pays : United States
Informations de copyright
Copyright © 2021 Eslami, Almuqhim, Raiker and Saeed.
Déclaration de conflit d'intérêts
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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