Identification of Alzheimer's disease using a convolutional neural network model based on T1-weighted magnetic resonance imaging
Abstract The classification of Alzheimer’s disease (AD) using deep learning methods has shown promising results, but successful application in clinical settings requires a combination of high accuracy, short processing time, and generalizability to various populations. In this study, we developed a...
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Nature Portfolio
2020
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oai:doaj.org-article:371364b6586240818c2f135e1d8b7cbd2021-12-02T13:58:16ZIdentification of Alzheimer's disease using a convolutional neural network model based on T1-weighted magnetic resonance imaging10.1038/s41598-020-79243-92045-2322https://doaj.org/article/371364b6586240818c2f135e1d8b7cbd2020-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-79243-9https://doaj.org/toc/2045-2322Abstract The classification of Alzheimer’s disease (AD) using deep learning methods has shown promising results, but successful application in clinical settings requires a combination of high accuracy, short processing time, and generalizability to various populations. In this study, we developed a convolutional neural network (CNN)-based AD classification algorithm using magnetic resonance imaging (MRI) scans from AD patients and age/gender-matched cognitively normal controls from two populations that differ in ethnicity and education level. These populations come from the Seoul National University Bundang Hospital (SNUBH) and Alzheimer’s Disease Neuroimaging Initiative (ADNI). For each population, we trained CNNs on five subsets using coronal slices of T1-weighted images that cover the medial temporal lobe. We evaluated the models on validation subsets from both the same population (within-dataset validation) and other population (between-dataset validation). Our models achieved average areas under the curves of 0.91–0.94 for within-dataset validation and 0.88–0.89 for between-dataset validation. The mean processing time per person was 23–24 s. The within-dataset and between-dataset performances were comparable between the ADNI-derived and SNUBH-derived models. These results demonstrate the generalizability of our models to different patients with different ethnicities and education levels, as well as their potential for deployment as fast and accurate diagnostic support tools for AD.Jong Bin BaeSubin LeeWonmo JungSejin ParkWeonjin KimHyunwoo OhJi Won HanGrace Eun KimJun Sung KimJae Hyoung KimKi Woong KimNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-10 (2020) |
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Medicine R Science Q Jong Bin Bae Subin Lee Wonmo Jung Sejin Park Weonjin Kim Hyunwoo Oh Ji Won Han Grace Eun Kim Jun Sung Kim Jae Hyoung Kim Ki Woong Kim Identification of Alzheimer's disease using a convolutional neural network model based on T1-weighted magnetic resonance imaging |
| description |
Abstract The classification of Alzheimer’s disease (AD) using deep learning methods has shown promising results, but successful application in clinical settings requires a combination of high accuracy, short processing time, and generalizability to various populations. In this study, we developed a convolutional neural network (CNN)-based AD classification algorithm using magnetic resonance imaging (MRI) scans from AD patients and age/gender-matched cognitively normal controls from two populations that differ in ethnicity and education level. These populations come from the Seoul National University Bundang Hospital (SNUBH) and Alzheimer’s Disease Neuroimaging Initiative (ADNI). For each population, we trained CNNs on five subsets using coronal slices of T1-weighted images that cover the medial temporal lobe. We evaluated the models on validation subsets from both the same population (within-dataset validation) and other population (between-dataset validation). Our models achieved average areas under the curves of 0.91–0.94 for within-dataset validation and 0.88–0.89 for between-dataset validation. The mean processing time per person was 23–24 s. The within-dataset and between-dataset performances were comparable between the ADNI-derived and SNUBH-derived models. These results demonstrate the generalizability of our models to different patients with different ethnicities and education levels, as well as their potential for deployment as fast and accurate diagnostic support tools for AD. |
| format |
article |
| author |
Jong Bin Bae Subin Lee Wonmo Jung Sejin Park Weonjin Kim Hyunwoo Oh Ji Won Han Grace Eun Kim Jun Sung Kim Jae Hyoung Kim Ki Woong Kim |
| author_facet |
Jong Bin Bae Subin Lee Wonmo Jung Sejin Park Weonjin Kim Hyunwoo Oh Ji Won Han Grace Eun Kim Jun Sung Kim Jae Hyoung Kim Ki Woong Kim |
| author_sort |
Jong Bin Bae |
| title |
Identification of Alzheimer's disease using a convolutional neural network model based on T1-weighted magnetic resonance imaging |
| title_short |
Identification of Alzheimer's disease using a convolutional neural network model based on T1-weighted magnetic resonance imaging |
| title_full |
Identification of Alzheimer's disease using a convolutional neural network model based on T1-weighted magnetic resonance imaging |
| title_fullStr |
Identification of Alzheimer's disease using a convolutional neural network model based on T1-weighted magnetic resonance imaging |
| title_full_unstemmed |
Identification of Alzheimer's disease using a convolutional neural network model based on T1-weighted magnetic resonance imaging |
| title_sort |
identification of alzheimer's disease using a convolutional neural network model based on t1-weighted magnetic resonance imaging |
| publisher |
Nature Portfolio |
| publishDate |
2020 |
| url |
https://doaj.org/article/371364b6586240818c2f135e1d8b7cbd |
| work_keys_str_mv |
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