Artificial Intelligence-based MRI Images for Brain in Prediction of Alzheimer's Disease
The study aimed to explore the accuracy and stability of Deep metric learning (DML) algorithm in Magnetic Resonance Imaging (MRI) examination of Alzheimer's Disease (AD) patients. In this study, MRI data of patients obtained were from Alzheimer's Disease Neuroimaging Initiative (ADNI) data...
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ftpubmed:oai:pubmedcentral.nih.gov:8548180 2023-05-15T16:01:12+02:00 Artificial Intelligence-based MRI Images for Brain in Prediction of Alzheimer's Disease Bi, Xiaowang Liu, Wei Liu, Huaiqin Shang, Qun 2021-10-19 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8548180/ https://doi.org/10.1155/2021/8198552 en eng Hindawi http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8548180/ http://dx.doi.org/10.1155/2021/8198552 Copyright © 2021 Xiaowang Bi et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. CC-BY J Healthc Eng Research Article Text 2021 ftpubmed https://doi.org/10.1155/2021/8198552 2021-10-31T01:00:38Z The study aimed to explore the accuracy and stability of Deep metric learning (DML) algorithm in Magnetic Resonance Imaging (MRI) examination of Alzheimer's Disease (AD) patients. In this study, MRI data of patients obtained were from Alzheimer's Disease Neuroimaging Initiative (ADNI) database (A total of 180 AD cases, 88 women, 92 men; 188 samples in healthy conditions (HC), including 90 females and 98 males. 210 samples of mild cognitive impairment (MCI), 104 females and 106 males). On the basis of deep learning, an early AD diagnosis system was constructed using CNN (Convolutional Neural Network) and DML algorithms. Then, the system was used to classify AD, HC, and MCI, and the two algorithms were compared for the accuracy and stability of in classification of MRI images. It was found that in the classification of AD and HC, the classification accuracy and sensitivity of the deep measurement learning model are both 0.83, superior to the CNN model; in terms of specificity, the classification specificity of the DML model was 0.82, slightly lower than that of the CNN model; and that in the classification of MCI and HC, the classification accuracy and sensitivity of the DML model was 0.65, superior to the CNN model; and in terms of specificity, the classification specificity of the DML model was 0.66, slightly lower than that of the CNN model. It suggested that the DML model demonstrated better classification effects on early AD patients. The loss curve analysis results showed that, for classification of AD and HC or MCI and HC, the DML algorithm can improve the convergence speed of the AD early prediction model. Therefore, the DML algorithm can significantly improve the clarity and quality of MRI images, elevate the classification accuracy and stability of early AD patients, and accelerate the convergence of the model, providing a new way for early prediction of AD. Text DML PubMed Central (PMC) Journal of Healthcare Engineering 2021 1 7 |
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Research Article Bi, Xiaowang Liu, Wei Liu, Huaiqin Shang, Qun Artificial Intelligence-based MRI Images for Brain in Prediction of Alzheimer's Disease |
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Research Article |
description |
The study aimed to explore the accuracy and stability of Deep metric learning (DML) algorithm in Magnetic Resonance Imaging (MRI) examination of Alzheimer's Disease (AD) patients. In this study, MRI data of patients obtained were from Alzheimer's Disease Neuroimaging Initiative (ADNI) database (A total of 180 AD cases, 88 women, 92 men; 188 samples in healthy conditions (HC), including 90 females and 98 males. 210 samples of mild cognitive impairment (MCI), 104 females and 106 males). On the basis of deep learning, an early AD diagnosis system was constructed using CNN (Convolutional Neural Network) and DML algorithms. Then, the system was used to classify AD, HC, and MCI, and the two algorithms were compared for the accuracy and stability of in classification of MRI images. It was found that in the classification of AD and HC, the classification accuracy and sensitivity of the deep measurement learning model are both 0.83, superior to the CNN model; in terms of specificity, the classification specificity of the DML model was 0.82, slightly lower than that of the CNN model; and that in the classification of MCI and HC, the classification accuracy and sensitivity of the DML model was 0.65, superior to the CNN model; and in terms of specificity, the classification specificity of the DML model was 0.66, slightly lower than that of the CNN model. It suggested that the DML model demonstrated better classification effects on early AD patients. The loss curve analysis results showed that, for classification of AD and HC or MCI and HC, the DML algorithm can improve the convergence speed of the AD early prediction model. Therefore, the DML algorithm can significantly improve the clarity and quality of MRI images, elevate the classification accuracy and stability of early AD patients, and accelerate the convergence of the model, providing a new way for early prediction of AD. |
format |
Text |
author |
Bi, Xiaowang Liu, Wei Liu, Huaiqin Shang, Qun |
author_facet |
Bi, Xiaowang Liu, Wei Liu, Huaiqin Shang, Qun |
author_sort |
Bi, Xiaowang |
title |
Artificial Intelligence-based MRI Images for Brain in Prediction of Alzheimer's Disease |
title_short |
Artificial Intelligence-based MRI Images for Brain in Prediction of Alzheimer's Disease |
title_full |
Artificial Intelligence-based MRI Images for Brain in Prediction of Alzheimer's Disease |
title_fullStr |
Artificial Intelligence-based MRI Images for Brain in Prediction of Alzheimer's Disease |
title_full_unstemmed |
Artificial Intelligence-based MRI Images for Brain in Prediction of Alzheimer's Disease |
title_sort |
artificial intelligence-based mri images for brain in prediction of alzheimer's disease |
publisher |
Hindawi |
publishDate |
2021 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8548180/ https://doi.org/10.1155/2021/8198552 |
genre |
DML |
genre_facet |
DML |
op_source |
J Healthc Eng |
op_relation |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8548180/ http://dx.doi.org/10.1155/2021/8198552 |
op_rights |
Copyright © 2021 Xiaowang Bi et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
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CC-BY |
op_doi |
https://doi.org/10.1155/2021/8198552 |
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Journal of Healthcare Engineering |
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2021 |
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1 |
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7 |
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1766397163635474432 |