Diagnosing autism spectrum disorder in children using conventional MRI and apparent diffusion coefficient based deep learning algorithms

Objective To develop and validate deep learning (DL) methods for diagnosing autism spectrum disorder (ASD) based on conventional MRI (cMRI) and apparent diffusion coefficient (ADC) images. Methods A total of 151 ASD children and 151 age-matched typically developing (TD) controls were included in thi...

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Veröffentlicht in:	European radiology Jg. 32; H. 2; S. 761 - 770
Hauptverfasser:	Guo, Xiang, Wang, Jiehuan, Wang, Xiaoqiang, Liu, Wenjing, Yu, Hao, Xu, Li, Li, Hengyan, Wu, Jiangfen, Dong, Mengxing, Tan, Weixiong, Chen, Weijian, Yang, Yunjun, Chen, Yueqin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2022 Springer Nature B.V
Schlagworte:	Algorithms Autism Autism Spectrum Disorder - diagnostic imaging Child Children Data acquisition Deep Learning Diagnostic Radiology Diagnostic systems Diffusion Diffusion coefficient Diffusion Magnetic Resonance Imaging Humans Imaging Imaging Informatics and Artificial Intelligence Internal Medicine Interventional Radiology Learning algorithms Machine learning Magnetic Resonance Imaging Medical diagnosis Medical imaging Medicine Medicine & Public Health Neuroimaging Neuroradiology Radiology Test sets Ultrasound Deep learning Computational neural networks Magnetic resonance imaging Autism spectrum disorder
ISSN:	0938-7994, 1432-1084, 1432-1084
Online-Zugang:	Volltext
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