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Investigation of Underlying Association Between Whole Brain Regions and Alzheimer's Disease: A Research Based on an Artificial Intelligence Model.
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- Author(s): Liu, Shui; Jie, Chen; Zheng, Weimin; Cui, Jingjing; Wang, Zhiqun
- Source:
Frontiers in Aging Neuroscience; 6/7/2022, Vol. 14, p1-11, 11p- Subject Terms:
ALZHEIMER'S disease diagnosis; ALZHEIMER'S disease risk factors; BRAIN; BIOMARKERS; THREE-dimensional imaging; RESEARCH methodology; MAGNETIC resonance imaging; ARTIFICIAL intelligence; FISHER exact test; MANN Whitney U Test; RISK assessment; COMPARATIVE studies; PSYCHOLOGICAL tests; RESEARCH funding; DESCRIPTIVE statistics; COMPUTER-aided diagnosis; PREDICTION models; LOGISTIC regression analysis; STATISTICAL sampling; DATA analysis software; RECEIVER operating characteristic curves; NEURORADIOLOGY; ALGORITHMS - Source:
- Additional Information
- Subject Terms:
- Abstract: Alzheimer's disease (AD) is the most common form of dementia, causing progressive cognitive decline. Radiomic features obtained from structural magnetic resonance imaging (sMRI) have shown a great potential in predicting this disease. However, radiomic features based on the whole brain segmented regions have not been explored yet. In our study, we collected sMRI data that include 80 patients with AD and 80 healthy controls (HCs). For each patient, the T1 weighted image (T1WI) images were segmented into 106 subregions, and radiomic features were extracted from each subregion. Then, we analyzed the radiomic features of specific brain subregions that were most related to AD. Based on the selective radiomic features from specific brain subregions, we built an integrated model using the best machine learning algorithms, and the diagnostic accuracy was evaluated. The subregions most relevant to AD included the hippocampus, the inferior parietal lobe, the precuneus, and the lateral occipital gyrus. These subregions exhibited several important radiomic features that include shape, gray level size zone matrix (GLSZM), and gray level dependence matrix (GLDM), among others. Based on the comparison among different algorithms, we constructed the best model using the Logistic regression (LR) algorithm, which reached an accuracy of 0.962. Conclusively, we constructed an excellent model based on radiomic features from several specific AD-related subregions, which could give a potential biomarker for predicting AD. [ABSTRACT FROM AUTHOR]
- Abstract: Copyright of Frontiers in Aging Neuroscience is the property of Frontiers Media S.A. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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