Xiang Guo1, Jiehuan Wang1, Xiaoqiang Wang1, Wenjing Liu2, Hao Yu1, Li Xu1, Hengyan Li1, Jiangfen Wu3, Mengxing Dong3, Weixiong Tan3, Weijian Chen4, Yunjun Yang4, Yueqin Chen5. 1. Department of Radiology, the Affiliated Hospital of Jining Medical University, Jining, China. 2. Children Rehabilitation Center, the Affiliated Hospital of Jining Medical University, Jining, China. 3. Infervision, Beijing, China. 4. Department of Medical Imaging, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China. 5. Department of Radiology, the Affiliated Hospital of Jining Medical University, Jining, China. sdjnchenyueqin@163.com.
Abstract
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.
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.
Authors: Kristen Lyall; Lisa Croen; Julie Daniels; M Daniele Fallin; Christine Ladd-Acosta; Brian K Lee; Bo Y Park; Nathaniel W Snyder; Diana Schendel; Heather Volk; Gayle C Windham; Craig Newschaffer Journal: Annu Rev Public Health Date: 2016-12-21 Impact factor: 21.981
Authors: Tony Charman; Simon Baron-Cohen; John Swettenham; Gillian Baird; Auriol Drew; Antony Cox Journal: Int J Lang Commun Disord Date: 2003 Jul-Sep Impact factor: 3.020
Authors: Matthew J Maenner; Kelly A Shaw; Jon Baio; Anita Washington; Mary Patrick; Monica DiRienzo; Deborah L Christensen; Lisa D Wiggins; Sydney Pettygrove; Jennifer G Andrews; Maya Lopez; Allison Hudson; Thaer Baroud; Yvette Schwenk; Tiffany White; Cordelia Robinson Rosenberg; Li-Ching Lee; Rebecca A Harrington; Margaret Huston; Amy Hewitt; Amy Esler; Jennifer Hall-Lande; Jenny N Poynter; Libby Hallas-Muchow; John N Constantino; Robert T Fitzgerald; Walter Zahorodny; Josephine Shenouda; Julie L Daniels; Zachary Warren; Alison Vehorn; Angelica Salinas; Maureen S Durkin; Patricia M Dietz Journal: MMWR Surveill Summ Date: 2020-03-27