Recognition of Parkinson’s Disease by Machine Learning Model Based on
MR Diffusion Tensor Imaging
LI Jing1,2，FAN Wenliang1,2，LEI Ziqiao1,2，YU Jianming1,2
1. Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology,
Wuhan Hubei 430022, China丨 2. Hubei Province Key Laboratory of Molecular Imaging, Wuhan Hubei 430022, China
Abstract：Objective To establish a machine learning model that can distinguish Parkinson’s disease (PD) patients from healthy
controls based on the data of MR diffusion tensor imaging (DTI), and to explore the brain imaging and biological markers of PD
patients. Methods A total of 289 patients with PD in our hospital from June 2015 to December 2019 were collected as PD group, and
131 healthy controls were recruited as control group. All subjects were divided into training set and validation set according to the
ratio of 7:3. Vectors which included parameters such as fractional anisotropy, mean diffusivity, axial and radial diffusion coefficient
was constructed based on the data of DTI. The dimension of vectors was reduced based on machine learning methods to build a
classification model, and the model evaluation was performed by receiver operating characteristic. Results Among the five machine
learning classification models constructed, the SVM_Linear model using twelve parameters of DTI for brain region had the best
classification performance. The results of model evaluation showed that the area under curve (AUC) in the training set was 0.897, the
sensitivity was 83.3%, and the specificity was 89.0%. The AUC in the validation set was 0.878, the sensitivity was 79.3%, and the
specificity was 88.4%. Conclusion The machine learning classification model based on the data of DTI can effectively distinguish
PD patients from healthy controls. The DTI parameters of corpus callosum, cingulate gyrus, fornix and other brain regions have the
potential to be used as imaging markers of PD.
李静1,2，范文亮1,2，雷子乔1,2，余建明1,2. 基于磁共振扩散张量成像的机器学习模型对帕金森病人的识别[J]. 中国医疗设备, 2021, 36(10): 32-35.
LI Jing1,2，FAN Wenliang1,2，LEI Ziqiao1,2，YU Jianming1,2. Recognition of Parkinson’s Disease by Machine Learning Model Based on
MR Diffusion Tensor Imaging. China Medical Devices, 2021, 36(10): 32-35.
