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

• Published in: European radiology Vol. 32; no. 2; pp. 761 - 770
• Main Authors: 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
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2022; Springer Nature B.V
• Subjects: 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
• DOI: 10.1007/s00330-021-08239-4

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 this study. The data from these subjects were assigned to training and validation datasets. An additional 20 ASD children and 25 TD controls were acquired, whose data were utilized in an independent test set. All subjects underwent cMRI and diffusion-weighted imaging examination of the brain. We developed a series of DL models to separate ASD from TD based on the cMRI and ADC data. The seven models used include five single-sequence models (SSMs), one dominant-sequence model (DSM), and one all-sequence model (ASM). To enhance the feature detection of the models, we embed an attention mechanism module. Results The highest AUC (0.824 ~ 0.850) was achieved when applying the SSM based on either FLAIR or ADC to the validation and independent test sets. A DSM using the combination of FLAIR and ADC showed an improved AUC in the validation (0.873) and independent test sets (0.876). The ASM also showed better diagnostic value in the validation (AUC = 0.838) and independent test sets (AUC = 0.836) compared to the SSMs. Among the models with attention mechanism, the DSM achieved the highest diagnostic performance with an AUC, accuracy, sensitivity, and specificity of 0.898, 84.4%, 85.0%, and 84.0% respectively. Conclusions This study established the potential of DL models to distinguish ASD cases from TD controls based on cMRI and ADC images. Key Points • Deep learning models based on conventional MRI and ADC can be used to diagnose ASD. • The model (DSM) based on the FLAIR and ADC sequence achieved the best diagnostic performance with an AUC of 0.836 in the independent test sets. • The attention mechanism further improved the diagnostic performance of the models.