Abstract #3178

Prediction of Drug Treatment Outcome among Epilepsy Children with Tuberous Sclerosis Complex based on Deep Neural Network and Multi-contrast MRI

Dian Jiang^1,2, Zhanqi Hu³, Cailei Zhao⁴, Xia Zhao³, Jun Yang^1,2, Yanjie Zhu^2,5, Jianxiang Liao³, Dong Liang^1,2,5, and Haifeng Wang^2,5

¹Research Centre for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China, ²University of Chinese Academy of Sciences, Beijing, China, ³Department of Neurology, Shenzhen Children’s Hospital, Shenzhen, China, ⁴Department of Radiology, Shenzhen Children’s Hospital, Shenzhen, China, ⁵Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

Synopsis

Distinguishing epilepsy drug treatment outcomes is crucial for treating children with tuberous sclerosis complex (TSC). Here, a deep-learning framework named AE-net was proposed to analyze epilepsy drug treatment outcomes using multi-contrast MRI data. Firstly, multi-contrast image-based models were respectively generated using the EfficientNet3D-B0 networks. Then, an averaging ensemble network was created as the final model. The proposed AE-net achieved the best AUC performance of 0.800 and sub-optimal AUC performance of 0.763 in the testing cohort, better than others. And the proposed method can predict epilepsy drug treatment outcomes to help clinical radiologists formulate more targeted treatments in the future.

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