Automatic diagnosis of Parkinson's disease using artificial intelligence base on routine T1-weighted MRI

Chang Li; Dongming Hui; Faqi Wu; Yuwei Xia; Feng Shi; Mingguang Yang; Jinrui Zhang; Chao Peng; Junbang Feng; Chuanming Li

doi:10.3389/fmed.2023.1303501

Automatic diagnosis of Parkinson's disease using artificial intelligence base on routine T1-weighted MRI

Front Med (Lausanne). 2024 Jan 5:10:1303501. doi: 10.3389/fmed.2023.1303501. eCollection 2023.

Authors

Chang Li^#¹, Dongming Hui^#², Faqi Wu³, Yuwei Xia⁴, Feng Shi⁴, Mingguang Yang¹, Jinrui Zhang¹, Chao Peng¹, Junbang Feng¹, Chuanming Li¹

Affiliations

¹ Bioengineering College of Chongqing University, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing, China.
² Department of Radiology, Chongqing Western Hospital, Chongqing, China.
³ Department of Medical Service, Yanzhuang Central Hospital of Gangcheng District, Chongqing, China.
⁴ Department of Research and Development, Shanghai United Imaging Intelligence, Co., Ltd. Shanghai, China.

^# Contributed equally.

Abstract

Background: Parkinson's disease (PD) is the second most common neurodegenerative disease. An objective diagnosis method is urgently needed in clinical practice. In this study, deep learning and radiomics techniques were studied to automatically diagnose PD from healthy controls (HCs).

Methods: 155 PD patients and 154 HCs were randomly divided into a training set (246 patients) and a testing set (63 patients). The brain subregions identification and segmentation were automatically performed with a VB-net, and radiomics features of billateral thalamus, caudatum, putamen and pallidum were extracted. Five independent machine learning classifiers [Support Vector Machine (SVM), Stochastic gradient descent (SGD), random forest (RF), quadratic discriminant analysis (QDA) and decision tree (DT)] were trained on the training set, and validated on the testing. Delong test was used to compare the performance of different models.

Results: Our VB-net could automatically identify and segment the brain into 109 regions. 2,264 radiomics features were automatically extracted from the billateral thalamus, caudatum, putamen or pallidum of each patient. After four step of features dimensionality reduction, Delong tests showed that the SVM model based on combined features had the best performance, with AUCs of 0.988 (95% CI: 0.979 ~ 0.998, specificity = 91.1%, sensitivity =100%, accuracy = 89.4% and precision = 88.2%) and 0.976 (95% CI: 0.942 ~ 1.000, specificity = 100%, sensitivity = 87.1%, accuracy = 93.5% and precision = 88.6%) in the training set and testing set, respectively. Decision curve analysis showed that the clinical benefit of the line graph model was high.

Conclusion: The SVM model based on combined features could be used to diagnose PD with high accuracy. Our fully automatic model could rapidly process the MRI data and distinguish PD and HCs in one minute. It greatly improved the diagnostic efficiency and has a great potential value in clinical practice to help the early diagnosis of PD.

Keywords: MRI; PD; T1-weighted; artificial intelligence; radiomics.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by the Fundamental Research Funds for the Central Universities (project no. 2022CDJYGRH-004); Natural Science Foundation Project of Chongqing (cstc2020jcyj-msxm0030, cstb2023nscq-bhx0074); and Chongqing Medical Scientific Research Project (Joint project of Chongqing Health Commission and Science and Technology Bureau) (2022QNXM013).