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The Feasibility of Differentiating Lewy Body Dementia and Alzheimer’s Disease by Deep Learning Using ECD SPECT Images
The correct differential diagnosis of dementia has an important impact on patient treatment and follow-up care strategies. Tc-99m-ECD SPECT imaging, which is low cost and accessible in general clinics, is used to identify the two common types of dementia, Alzheimer’s disease (AD) and Lewy body demen...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8624770/ https://www.ncbi.nlm.nih.gov/pubmed/34829438 http://dx.doi.org/10.3390/diagnostics11112091 |
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author | Ni, Yu-Ching Tseng, Fan-Pin Pai, Ming-Chyi Hsiao, Ing-Tsung Lin, Kun-Ju Lin, Zhi-Kun Lin, Chia-Yu Chiu, Pai-Yi Hung, Guang-Uei Chang, Chiung-Chih Chang, Ya-Ting Chuang, Keh-Shih |
author_facet | Ni, Yu-Ching Tseng, Fan-Pin Pai, Ming-Chyi Hsiao, Ing-Tsung Lin, Kun-Ju Lin, Zhi-Kun Lin, Chia-Yu Chiu, Pai-Yi Hung, Guang-Uei Chang, Chiung-Chih Chang, Ya-Ting Chuang, Keh-Shih |
author_sort | Ni, Yu-Ching |
collection | PubMed |
description | The correct differential diagnosis of dementia has an important impact on patient treatment and follow-up care strategies. Tc-99m-ECD SPECT imaging, which is low cost and accessible in general clinics, is used to identify the two common types of dementia, Alzheimer’s disease (AD) and Lewy body dementia (LBD). Two-stage transfer learning technology and reducing model complexity based on the ResNet-50 model were performed using the ImageNet data set and ADNI database. To improve training accuracy, the three-dimensional image was reorganized into three sets of two-dimensional images for data augmentation and ensemble learning, then the performance of various deep learning models for Tc-99m-ECD SPECT images to distinguish AD/normal cognition (NC), LBD/NC, and AD/LBD were investigated. In the AD/NC, LBD/NC, and AD/LBD tasks, the AUC values were around 0.94, 0.95, and 0.74, regardless of training models, with an accuracy of 90%, 87%, and 71%, and F1 scores of 89%, 86%, and 76% in the best cases. The use of transfer learning and a modified model resulted in better prediction results, increasing the accuracy by 32% for AD/NC. The proposed method is practical and could rapidly utilize a deep learning model to automatically extract image features based on a small number of SPECT brain perfusion images in general clinics to objectively distinguish AD and LBD. |
format | Online Article Text |
id | pubmed-8624770 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-86247702021-11-27 The Feasibility of Differentiating Lewy Body Dementia and Alzheimer’s Disease by Deep Learning Using ECD SPECT Images Ni, Yu-Ching Tseng, Fan-Pin Pai, Ming-Chyi Hsiao, Ing-Tsung Lin, Kun-Ju Lin, Zhi-Kun Lin, Chia-Yu Chiu, Pai-Yi Hung, Guang-Uei Chang, Chiung-Chih Chang, Ya-Ting Chuang, Keh-Shih Diagnostics (Basel) Article The correct differential diagnosis of dementia has an important impact on patient treatment and follow-up care strategies. Tc-99m-ECD SPECT imaging, which is low cost and accessible in general clinics, is used to identify the two common types of dementia, Alzheimer’s disease (AD) and Lewy body dementia (LBD). Two-stage transfer learning technology and reducing model complexity based on the ResNet-50 model were performed using the ImageNet data set and ADNI database. To improve training accuracy, the three-dimensional image was reorganized into three sets of two-dimensional images for data augmentation and ensemble learning, then the performance of various deep learning models for Tc-99m-ECD SPECT images to distinguish AD/normal cognition (NC), LBD/NC, and AD/LBD were investigated. In the AD/NC, LBD/NC, and AD/LBD tasks, the AUC values were around 0.94, 0.95, and 0.74, regardless of training models, with an accuracy of 90%, 87%, and 71%, and F1 scores of 89%, 86%, and 76% in the best cases. The use of transfer learning and a modified model resulted in better prediction results, increasing the accuracy by 32% for AD/NC. The proposed method is practical and could rapidly utilize a deep learning model to automatically extract image features based on a small number of SPECT brain perfusion images in general clinics to objectively distinguish AD and LBD. MDPI 2021-11-12 /pmc/articles/PMC8624770/ /pubmed/34829438 http://dx.doi.org/10.3390/diagnostics11112091 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Ni, Yu-Ching Tseng, Fan-Pin Pai, Ming-Chyi Hsiao, Ing-Tsung Lin, Kun-Ju Lin, Zhi-Kun Lin, Chia-Yu Chiu, Pai-Yi Hung, Guang-Uei Chang, Chiung-Chih Chang, Ya-Ting Chuang, Keh-Shih The Feasibility of Differentiating Lewy Body Dementia and Alzheimer’s Disease by Deep Learning Using ECD SPECT Images |
title | The Feasibility of Differentiating Lewy Body Dementia and Alzheimer’s Disease by Deep Learning Using ECD SPECT Images |
title_full | The Feasibility of Differentiating Lewy Body Dementia and Alzheimer’s Disease by Deep Learning Using ECD SPECT Images |
title_fullStr | The Feasibility of Differentiating Lewy Body Dementia and Alzheimer’s Disease by Deep Learning Using ECD SPECT Images |
title_full_unstemmed | The Feasibility of Differentiating Lewy Body Dementia and Alzheimer’s Disease by Deep Learning Using ECD SPECT Images |
title_short | The Feasibility of Differentiating Lewy Body Dementia and Alzheimer’s Disease by Deep Learning Using ECD SPECT Images |
title_sort | feasibility of differentiating lewy body dementia and alzheimer’s disease by deep learning using ecd spect images |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8624770/ https://www.ncbi.nlm.nih.gov/pubmed/34829438 http://dx.doi.org/10.3390/diagnostics11112091 |
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