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...

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Autores principales:	Bin Xiao, Naying He, Qian Wang, Feng Shi, Zenghui Cheng, Ewart Mark Haacke, Fuhua Yan, Dinggang Shen
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	Parkinson’s disease computer-assisted diagnosis deep learning stability quantitative susceptibility mapping radiomics Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/c32cdb6e17ba47f98aa1ad4a7ca3509f
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spelling	oai:doaj.org-article:c32cdb6e17ba47f98aa1ad4a7ca3509f2021-11-30T14:41:40ZStability of AI-Enabled Diagnosis of Parkinson’s Disease: A Study Targeting Substantia Nigra in Quantitative Susceptibility Mapping Imaging1662-453X10.3389/fnins.2021.760975https://doaj.org/article/c32cdb6e17ba47f98aa1ad4a7ca3509f2021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fnins.2021.760975/fullhttps://doaj.org/toc/1662-453XPurpose: 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.Bin XiaoBin XiaoNaying HeQian WangFeng ShiZenghui ChengEwart Mark HaackeEwart Mark HaackeFuhua YanDinggang ShenDinggang ShenFrontiers Media S.A.articleParkinson’s diseasecomputer-assisted diagnosisdeep learningstabilityquantitative susceptibility mappingradiomicsNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENFrontiers in Neuroscience, Vol 15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Parkinson’s disease computer-assisted diagnosis deep learning stability quantitative susceptibility mapping radiomics Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	Parkinson’s disease computer-assisted diagnosis deep learning stability quantitative susceptibility mapping radiomics Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 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
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.
format	article
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
title	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_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_sort	stability of ai-enabled diagnosis of parkinson’s disease: a study targeting substantia nigra in quantitative susceptibility mapping imaging
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/c32cdb6e17ba47f98aa1ad4a7ca3509f
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Stability of AI-Enabled Diagnosis of Parkinson’s Disease: A Study Targeting Substantia Nigra in Quantitative Susceptibility Mapping Imaging

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