Abstract #2896

Application of radiomics approach on predicting freezing of gait in Parkinson’s disease based on rs-fMRI indices

Miaoran Guo¹, Hu Liu¹, and Guoguang Fan¹

¹The First Hospital of China Medical University, Shenyang, China

Synopsis

Keywords: Machine Learning/Artificial Intelligence, fMRI (resting state), Parkinson’s disease, freezing of gait, feedforward neural network, receiver operating characteristic.

In the present investigation, we built a non-invasive and automatic classification model, by extracting radiomic features based on whole-brain functional alterations of rs-fMRI indices (mALFF, mReHo, and DC) combined with clinical scales (MoCA, and HAMD) using feedforward neural network (FNN) models, which is a representative of supervised learning classification methods. We found that these models can effectively differentiate PD-FOG and PD-nFOG and find potential biomarkers of PD-FOG, which might facilitate the individual diagnosis of PD-FOG patients.

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