Abstract #2512

The evaluation of Synthetic MRI and machine learning in differentiating autism and developmental language disorders

Yanyong Shen¹, Xin Zhao¹, Kaiyu Wang², Qingna Xing¹, Honglei Shang¹, Hongrui Ren¹, Yongbing Sun³, and Xiaoan Zhang¹

¹Department of Radiology, the Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China, ²MR Research China, GE Healthcare, Beijing 100000, PR China, Beijing, China, ³Department of Medical Imaging of Henan Provincial People’s Hospital, Zhengzhou University People’s Hospital, Zhengzhou, China

Synopsis

Keywords: Neuro, Quantitative Imaging, Autism Spectrum DisorderDistinguishing early Autism Spectrum Disorder (ASD) from Developmental language disorder (DLD) in clinical practice is challenging as they are usually diagnose by behavioral tests and subjective observation. The emerging technique Synthetic MRI can be used to quantify the changed in biological tissues. Machine learning is also widely used for improvement of diagnostic performance. This study was aimed to identify ASD from DLD by using Synthetic MRI in combination with machine learning methods. Results show that T1 mapping in Synthetic MRI can be used for differentiation of the two diseases and the SVM model with linear kernel have the best performance.

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