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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...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
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.
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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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