Diagnosing autism spectrum disorder in children using conventional MRI and apparent diffusion coefficient based deep learning algorithms
| Autor: | Yunjun Yang, Wenjing Liu, Mengxing Dong, Xiaoqiang Wang, Jiangfen Wu, Weixiong Tan, Yueqin Chen, Hao Yu, Li Xu, Jiehuan Wang, Xiang Guo, Weijian Chen, Hengyan Li |
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| Rok vydání: | 2021 |
| Předmět: |
medicine.medical_specialty
Autism Spectrum Disorder Fluid-attenuated inversion recovery Typically developing Deep Learning medicine Humans Effective diffusion coefficient Radiology Nuclear Medicine and imaging Child Neuroradiology medicine.diagnostic_test business.industry Deep learning Magnetic resonance imaging Pattern recognition General Medicine medicine.disease Magnetic Resonance Imaging Diffusion Magnetic Resonance Imaging Autism spectrum disorder Test set Radiology Artificial intelligence business Algorithms |
| Zdroj: | European Radiology. 32:761-770 |
| ISSN: | 1432-1084 0938-7994 |
| DOI: | 10.1007/s00330-021-08239-4 |
| Popis: | 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. 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. 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. This study established the potential of DL models to distinguish ASD cases from TD controls based on cMRI and ADC images. • 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. |
| Databáze: | OpenAIRE |
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