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Machine learning identified an Alzheimer’s disease-related FDG-PET pattern which is also expressed in Lewy body dementia and Parkinson’s disease dementia

Utilizing the publicly available neuroimaging database enabled by Alzheimer’s disease Neuroimaging Initiative (ADNI; http://adni.loni.usc.edu/), we have compared the performance of automated classification algorithms that differentiate AD vs. normal subjects using Positron Emission Tomography (PET)...

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Autores principales: Katako, Audrey, Shelton, Paul, Goertzen, Andrew L., Levin, Daniel, Bybel, Bohdan, Aljuaid, Maram, Yoon, Hyun Jin, Kang, Do Young, Kim, Seok Min, Lee, Chong Sik, Ko, Ji Hyun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6125295/
https://www.ncbi.nlm.nih.gov/pubmed/30185806
http://dx.doi.org/10.1038/s41598-018-31653-6
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author Katako, Audrey
Shelton, Paul
Goertzen, Andrew L.
Levin, Daniel
Bybel, Bohdan
Aljuaid, Maram
Yoon, Hyun Jin
Kang, Do Young
Kim, Seok Min
Lee, Chong Sik
Ko, Ji Hyun
author_facet Katako, Audrey
Shelton, Paul
Goertzen, Andrew L.
Levin, Daniel
Bybel, Bohdan
Aljuaid, Maram
Yoon, Hyun Jin
Kang, Do Young
Kim, Seok Min
Lee, Chong Sik
Ko, Ji Hyun
author_sort Katako, Audrey
collection PubMed
description Utilizing the publicly available neuroimaging database enabled by Alzheimer’s disease Neuroimaging Initiative (ADNI; http://adni.loni.usc.edu/), we have compared the performance of automated classification algorithms that differentiate AD vs. normal subjects using Positron Emission Tomography (PET) with fluorodeoxyglucose (FDG). General linear model, scaled subprofile modeling and support vector machines were examined. Among the tested classification methods, support vector machine with Iterative Single Data Algorithm produced the best performance, i.e., sensitivity (0.84) × specificity (0.95), by 10-fold cross-validation. We have applied the same classification algorithm to four different datasets from ADNI, Health Science Centre (Winnipeg, Canada), Dong-A University Hospital (Busan, S. Korea) and Asan Medical Centre (Seoul, S. Korea). Our data analyses confirmed that the support vector machine with Iterative Single Data Algorithm showed the best performance in prediction of future development of AD from the prodromal stage (mild cognitive impairment), and that it was also sensitive to other types of dementia such as Parkinson’s Disease Dementia and Dementia with Lewy Bodies, and that perfusion imaging using single photon emission computed tomography may achieve a similar accuracy to that of FDG-PET.
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spelling pubmed-61252952018-09-10 Machine learning identified an Alzheimer’s disease-related FDG-PET pattern which is also expressed in Lewy body dementia and Parkinson’s disease dementia Katako, Audrey Shelton, Paul Goertzen, Andrew L. Levin, Daniel Bybel, Bohdan Aljuaid, Maram Yoon, Hyun Jin Kang, Do Young Kim, Seok Min Lee, Chong Sik Ko, Ji Hyun Sci Rep Article Utilizing the publicly available neuroimaging database enabled by Alzheimer’s disease Neuroimaging Initiative (ADNI; http://adni.loni.usc.edu/), we have compared the performance of automated classification algorithms that differentiate AD vs. normal subjects using Positron Emission Tomography (PET) with fluorodeoxyglucose (FDG). General linear model, scaled subprofile modeling and support vector machines were examined. Among the tested classification methods, support vector machine with Iterative Single Data Algorithm produced the best performance, i.e., sensitivity (0.84) × specificity (0.95), by 10-fold cross-validation. We have applied the same classification algorithm to four different datasets from ADNI, Health Science Centre (Winnipeg, Canada), Dong-A University Hospital (Busan, S. Korea) and Asan Medical Centre (Seoul, S. Korea). Our data analyses confirmed that the support vector machine with Iterative Single Data Algorithm showed the best performance in prediction of future development of AD from the prodromal stage (mild cognitive impairment), and that it was also sensitive to other types of dementia such as Parkinson’s Disease Dementia and Dementia with Lewy Bodies, and that perfusion imaging using single photon emission computed tomography may achieve a similar accuracy to that of FDG-PET. Nature Publishing Group UK 2018-09-05 /pmc/articles/PMC6125295/ /pubmed/30185806 http://dx.doi.org/10.1038/s41598-018-31653-6 Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Katako, Audrey
Shelton, Paul
Goertzen, Andrew L.
Levin, Daniel
Bybel, Bohdan
Aljuaid, Maram
Yoon, Hyun Jin
Kang, Do Young
Kim, Seok Min
Lee, Chong Sik
Ko, Ji Hyun
Machine learning identified an Alzheimer’s disease-related FDG-PET pattern which is also expressed in Lewy body dementia and Parkinson’s disease dementia
title Machine learning identified an Alzheimer’s disease-related FDG-PET pattern which is also expressed in Lewy body dementia and Parkinson’s disease dementia
title_full Machine learning identified an Alzheimer’s disease-related FDG-PET pattern which is also expressed in Lewy body dementia and Parkinson’s disease dementia
title_fullStr Machine learning identified an Alzheimer’s disease-related FDG-PET pattern which is also expressed in Lewy body dementia and Parkinson’s disease dementia
title_full_unstemmed Machine learning identified an Alzheimer’s disease-related FDG-PET pattern which is also expressed in Lewy body dementia and Parkinson’s disease dementia
title_short Machine learning identified an Alzheimer’s disease-related FDG-PET pattern which is also expressed in Lewy body dementia and Parkinson’s disease dementia
title_sort machine learning identified an alzheimer’s disease-related fdg-pet pattern which is also expressed in lewy body dementia and parkinson’s disease dementia
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6125295/
https://www.ncbi.nlm.nih.gov/pubmed/30185806
http://dx.doi.org/10.1038/s41598-018-31653-6
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