Stability of AI-Enabled Diagnosis of Parkinson’s Disease: A Study Targeting Substantia Nigra in Quantitative Susceptibility Mapping Imaging
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 downstre...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-11-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2021.760975/full |
| _version_ | 1818723616357875712 |
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| author | Bin Xiao Bin Xiao Naying He Qian Wang Feng Shi Zenghui Cheng Ewart Mark Haacke Ewart Mark Haacke Fuhua Yan Dinggang Shen Dinggang Shen |
| author_facet | Bin Xiao Bin Xiao Naying He Qian Wang Feng Shi Zenghui Cheng Ewart Mark Haacke Ewart Mark Haacke Fuhua Yan Dinggang Shen Dinggang Shen |
| author_sort | Bin Xiao |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-17T21:13:21Z |
| format | Article |
| id | doaj.art-c32cdb6e17ba47f98aa1ad4a7ca3509f |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-12-17T21:13:21Z |
| publishDate | 2021-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-c32cdb6e17ba47f98aa1ad4a7ca3509f2022-12-21T21:32:25ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2021-11-011510.3389/fnins.2021.760975760975Stability of AI-Enabled Diagnosis of Parkinson’s Disease: A Study Targeting Substantia Nigra in Quantitative Susceptibility Mapping ImagingBin Xiao0Bin Xiao1Naying He2Qian Wang3Feng Shi4Zenghui Cheng5Ewart Mark Haacke6Ewart Mark Haacke7Fuhua Yan8Dinggang Shen9Dinggang Shen10School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, ChinaShanghai United Imaging Intelligence Co., Ltd., Shanghai, ChinaDepartment of Radiology, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, ChinaSchool of Biomedical Engineering, ShanghaiTech University, Shanghai, ChinaShanghai United Imaging Intelligence Co., Ltd., Shanghai, ChinaDepartment of Radiology, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, ChinaDepartment of Radiology, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, ChinaDepartment of Radiology, Wayne State University, Detroit, MI, United StatesDepartment of Radiology, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, ChinaShanghai United Imaging Intelligence Co., Ltd., Shanghai, ChinaSchool of Biomedical Engineering, ShanghaiTech University, Shanghai, ChinaPurpose: 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.https://www.frontiersin.org/articles/10.3389/fnins.2021.760975/fullParkinson’s diseasecomputer-assisted diagnosisdeep learningstabilityquantitative susceptibility mappingradiomics |
| spellingShingle | Bin Xiao Bin Xiao Naying He Qian Wang Feng Shi Zenghui Cheng Ewart Mark Haacke Ewart Mark Haacke Fuhua Yan Dinggang Shen Dinggang Shen Stability of AI-Enabled Diagnosis of Parkinson’s Disease: A Study Targeting Substantia Nigra in Quantitative Susceptibility Mapping Imaging Frontiers in Neuroscience Parkinson’s disease computer-assisted diagnosis deep learning stability quantitative susceptibility mapping radiomics |
| title | Stability of AI-Enabled Diagnosis of Parkinson’s Disease: A Study Targeting Substantia Nigra in Quantitative Susceptibility Mapping Imaging |
| title_full | Stability of AI-Enabled Diagnosis of Parkinson’s Disease: A Study Targeting Substantia Nigra in Quantitative Susceptibility Mapping Imaging |
| title_fullStr | Stability of AI-Enabled Diagnosis of Parkinson’s Disease: A Study Targeting Substantia Nigra in Quantitative Susceptibility Mapping Imaging |
| title_full_unstemmed | Stability of AI-Enabled Diagnosis of Parkinson’s Disease: A Study Targeting Substantia Nigra in Quantitative Susceptibility Mapping Imaging |
| title_short | Stability of AI-Enabled Diagnosis of Parkinson’s Disease: A Study Targeting Substantia Nigra in Quantitative Susceptibility Mapping Imaging |
| title_sort | stability of ai enabled diagnosis of parkinson s disease a study targeting substantia nigra in quantitative susceptibility mapping imaging |
| topic | Parkinson’s disease computer-assisted diagnosis deep learning stability quantitative susceptibility mapping radiomics |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2021.760975/full |
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