Predicting Alzheimer Disease From Mild Cognitive Impairment With a Deep Belief Network Based on 18F-FDG-PET Images
Objective: Accurate diagnosis of early Alzheimer disease (AD) plays a critical role in preventing the progression of memory impairment. We aimed to develop a new deep belief network (DBN) framework using 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) metabolic imaging to identify pa...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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SAGE Publications
2019-09-01
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| Series: | Molecular Imaging |
| Online Access: | https://doi.org/10.1177/1536012119877285 |
| _version_ | 1797282158186856448 |
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| author | Ting Shen MsD Jiehui Jiang PhD Jiaying Lu MD Min Wang MsD Chuantao Zuo MD, PhD Zhihua Yu Zhuangzhi Yan PhD |
| author_facet | Ting Shen MsD Jiehui Jiang PhD Jiaying Lu MD Min Wang MsD Chuantao Zuo MD, PhD Zhihua Yu Zhuangzhi Yan PhD |
| author_sort | Ting Shen MsD |
| collection | DOAJ |
| description | Objective: Accurate diagnosis of early Alzheimer disease (AD) plays a critical role in preventing the progression of memory impairment. We aimed to develop a new deep belief network (DBN) framework using 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) metabolic imaging to identify patients at the mild cognitive impairment (MCI) stage with presymptomatic AD and to discriminate them from other patients with MCI. Methods: 18F-fluorodeoxyglucose-PET images of 109 patients recruited in the ongoing longitudinal Alzheimer’s Disease Neuroimaging Initiative study were included in this analysis. Patients were grouped into 2 classes: (1) stable mild cognitive impairment (n = 62) or (2) progressive mild cognitive impairment (n = 47). Our framework is composed of 4 steps: (1) image preprocessing: normalization and smoothing; (2) identification of regions of interest (ROIs); (3) feature learning using deep neural networks; and (4) classification by support vector machine with 3 kernels. All classification experiments were performed with a 5-fold cross-validation. Accuracy, sensitivity, and specificity were used to validate the results. Result: A total of 1103 ROIs were obtained. One hundred features were learned from ROIs using the DBN. The classification accuracy using linear, polynomial, and RBF kernels was 83.9%, 79.2%, and 86.6%, respectively. This method may be a powerful tool for personalized precision medicine in the population with prediction of early AD progression. |
| first_indexed | 2024-03-07T17:08:58Z |
| format | Article |
| id | doaj.art-95c7d40ef07a48d889320285aa1a999f |
| institution | Directory Open Access Journal |
| issn | 1536-0121 |
| language | English |
| last_indexed | 2024-03-07T17:08:58Z |
| publishDate | 2019-09-01 |
| publisher | SAGE Publications |
| record_format | Article |
| series | Molecular Imaging |
| spelling | doaj.art-95c7d40ef07a48d889320285aa1a999f2024-03-03T02:19:38ZengSAGE PublicationsMolecular Imaging1536-01212019-09-011810.1177/1536012119877285Predicting Alzheimer Disease From Mild Cognitive Impairment With a Deep Belief Network Based on 18F-FDG-PET ImagesTing Shen MsD0Jiehui Jiang PhD1Jiaying Lu MD2Min Wang MsD3Chuantao Zuo MD, PhD4Zhihua Yu5Zhuangzhi Yan PhD6 Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China PET Center, Huashan Hospital, Fudan University, Shanghai, China Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China PET Center, Huashan Hospital, Fudan University, Shanghai, China Shanghai Geriatric Institute of Chinese Medicine, Shanghai, China Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, ChinaObjective: Accurate diagnosis of early Alzheimer disease (AD) plays a critical role in preventing the progression of memory impairment. We aimed to develop a new deep belief network (DBN) framework using 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) metabolic imaging to identify patients at the mild cognitive impairment (MCI) stage with presymptomatic AD and to discriminate them from other patients with MCI. Methods: 18F-fluorodeoxyglucose-PET images of 109 patients recruited in the ongoing longitudinal Alzheimer’s Disease Neuroimaging Initiative study were included in this analysis. Patients were grouped into 2 classes: (1) stable mild cognitive impairment (n = 62) or (2) progressive mild cognitive impairment (n = 47). Our framework is composed of 4 steps: (1) image preprocessing: normalization and smoothing; (2) identification of regions of interest (ROIs); (3) feature learning using deep neural networks; and (4) classification by support vector machine with 3 kernels. All classification experiments were performed with a 5-fold cross-validation. Accuracy, sensitivity, and specificity were used to validate the results. Result: A total of 1103 ROIs were obtained. One hundred features were learned from ROIs using the DBN. The classification accuracy using linear, polynomial, and RBF kernels was 83.9%, 79.2%, and 86.6%, respectively. This method may be a powerful tool for personalized precision medicine in the population with prediction of early AD progression.https://doi.org/10.1177/1536012119877285 |
| spellingShingle | Ting Shen MsD Jiehui Jiang PhD Jiaying Lu MD Min Wang MsD Chuantao Zuo MD, PhD Zhihua Yu Zhuangzhi Yan PhD Predicting Alzheimer Disease From Mild Cognitive Impairment With a Deep Belief Network Based on 18F-FDG-PET Images Molecular Imaging |
| title | Predicting Alzheimer Disease From Mild Cognitive Impairment With a Deep Belief Network Based on 18F-FDG-PET Images |
| title_full | Predicting Alzheimer Disease From Mild Cognitive Impairment With a Deep Belief Network Based on 18F-FDG-PET Images |
| title_fullStr | Predicting Alzheimer Disease From Mild Cognitive Impairment With a Deep Belief Network Based on 18F-FDG-PET Images |
| title_full_unstemmed | Predicting Alzheimer Disease From Mild Cognitive Impairment With a Deep Belief Network Based on 18F-FDG-PET Images |
| title_short | Predicting Alzheimer Disease From Mild Cognitive Impairment With a Deep Belief Network Based on 18F-FDG-PET Images |
| title_sort | predicting alzheimer disease from mild cognitive impairment with a deep belief network based on 18f fdg pet images |
| url | https://doi.org/10.1177/1536012119877285 |
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