Stability of AI-Enabled Diagnosis of Parkinson’s Disease: A Study Targeting Substantia Nigra in Quantitative Susceptibility Mapping Imaging

Frontiers in Neuroscience

Naying He¹  Zenghui Cheng¹  Fuhua Yan¹  Ewart Mark Haacke²  Qian Wang⁴  Dinggang Shen⁴  Bin Xiao⁵  Feng Shi⁵ 

[1] Department of Radiology, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China;Department of Radiology, Wayne State University, Detroit, MI, United States;School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China;School of Biomedical Engineering, ShanghaiTech University, Shanghai, China;Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China;

关键词: Parkinson’s disease;    computer-assisted diagnosis;    deep learning;    stability;    quantitative susceptibility mapping;    radiomics;   

DOI  :  10.3389/fnins.2021.760975

来源: DOAJ

【 摘 要 】

Purpose: Parkinson’s disease (PD) diagnosis algorithms based on quantitative susceptibility mapping (QSM) and image algorithms rely on substantia nigra (SN) labeling. However, the difference between SN labels from different experts (or segmentation algorithms) will have a negative impact on downstream diagnostic tasks, such as the decrease of the accuracy of the algorithm or different diagnostic results for the same sample. In this article, we quantify the accuracy of the algorithm on different label sets and then improve the convolutional neural network (CNN) model to obtain a high-precision and highly robust diagnosis algorithm.Methods: The logistic regression model and CNN model were first compared for classification between PD patients and healthy controls (HC), given different sets of SN labeling. Then, based on the CNN model with better performance, we further proposed a novel “gated pooling” operation and integrated it with deep learning to attain a joint framework for image segmentation and classification.Results: The experimental results show that, with different sets of SN labeling that mimic different experts, the CNN model can maintain a stable classification accuracy at around 86.4%, while the conventional logistic regression model yields a large fluctuation ranging from 78.9 to 67.9%. Furthermore, the “gated pooling” operation, after being integrated for joint image segmentation and classification, can improve the diagnosis accuracy to 86.9% consistently, which is statistically better than the baseline.Conclusion: The CNN model, compared with the conventional logistic regression model using radiomics features, has better stability in PD diagnosis. Furthermore, the joint end-to-end CNN model is shown to be suitable for PD diagnosis from the perspectives of accuracy, stability, and convenience in actual use.

【 授权许可】

Unknown