|
KMID : 0367320200310030097
|
|
Journal of the Korean Academy of Child and Adolescent Psychiatry
2020 Volume.31 No. 3 p.97 ~ p.104
|
|
|
Neuroimaging-Based Deep Learning in Autism Spectrum Disorder and Attention-Deficit/Hyperactivity Disorder
|
|
Song Jae-Won
Yoon Na-Rae
Jang Soo-Min
Lee Ga-Young
Kim Bung-Nyun
|
|
|
Abstract
|
|
|
|
Deep learning (DL) is a kind of machine learning technique that uses artificial intelligence to identify the characteristics of given data and efficiently analyze large amounts of information to perform tasks such as classification and prediction. In the field of neuroimaging of neurodevelopmental disorders, various biomarkers for diagnosis, classification, prognosis prediction, and treatment response prediction have been examined; however, they have not been efficiently combined to produce meaningful results. DL can be applied to overcome these limitations and produce clinically helpful results. Here, we review studies that combine neurodevelopmental disorder neuroimaging and DL techniques to explore the strengths, limitations, and future directions of this research area.
|
|
|
KEYWORD
|
|
|
Neuroimaging, Neurodevelopmental disorder, Autism spectrum disorder, Attention-deficit/hyperactivity disorder, Deep learning, Review
|
|
|
FullTexts / Linksout information
|
|
|
|
|
|
Listed journal information
|
|
  
|