Abstract #0160

Developing a Graph Convolutional Network of Differentiable Graph Module for Multi-Modal MRI Data: An Application to Parkinson's Disease

Fanshi Li^1,2, Jun Li³, Yifan Guo^2,4, Zhihui Wang^1,2, Zhilin Zhang^2,4, Xin Liu^1,2, Hairong Zheng^1,2, Yanjie Zhu^1,2, Liang Dong^2,4, and Haifeng Wang^1,2

¹Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China, ²University of Chinese Academy of Sciences, Beijing, China, ³The Second People’s Hospital of Shenzhen, Shenzhen, China, ⁴Research Center for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Parkinson's DiseaseWith the ageing of the population, Parkinson's disease (PD) has presented a severe challenge to public health. Here, a deep-learning framework named the AMDGM model was proposed to predict PD patients at an early stage. Firstly, multi-modal image-based models were respectively generated using the AMDGM model. Then, a weighted ensemble network was created as the final model. The proposed method achieved the best AUC performance of 0.872 in the testing cohort, better than others. And the proposed method can predict PD patients early to help clinical radiologists formulate more targeted treatments in the future.

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