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Open Access 01-12-2021 | Alzheimer's Disease | Original research

Deep learning-based amyloid PET positivity classification model in the Alzheimer’s disease continuum by using 2-[¹⁸F]FDG PET

Authors: Suhong Kim, Peter Lee, Kyeong Taek Oh, Min Soo Byun, Dahyun Yi, Jun Ho Lee, Yu Kyeong Kim, Byoung Seok Ye, Mi Jin Yun, Dong Young Lee, Yong Jeong, the Alzheimer’s Disease Neuroimaging Initiative, the KBASE Research Group

Published in: EJNMMI Research | Issue 1/2021

Abstract

Background

Considering the limited accessibility of amyloid position emission tomography (PET) in patients with dementia, we proposed a deep learning (DL)-based amyloid PET positivity classification model from PET images with 2-deoxy-2-[fluorine-18]fluoro-D-glucose (2-[¹⁸F]FDG).

Methods

We used 2-[¹⁸F]FDG PET datasets from the Alzheimer's Disease Neuroimaging Initiative and Korean Brain Aging Study for the Early diagnosis and prediction of Alzheimer’s disease for model development. Moreover, we used an independent dataset from another hospital. A 2.5-D deep learning architecture was constructed using 291 submodules and three axes images as the input. We conducted the voxel-wise analysis to assess the regions with substantial differences in glucose metabolism between the amyloid PET-positive and PET-negative participants. This facilitated an understanding of the deep model classification. In addition, we compared these regions with the classification probability from the submodules.

Results

There were 686 out of 1433 (47.9%) and 50 out of 100 (50%) amyloid PET-positive participants in the training and internal validation datasets and the external validation datasets, respectively. With 50 times iterations of model training and validation, the model achieved an AUC of 0.811 (95% confidence interval (CI) of 0.803–0.819) and 0.798 (95% CI, 0.789–0.807) on the internal and external validation datasets, respectively. The area under the curve (AUC) was 0.860 when tested with the model with the highest value (0.864) on the external validation dataset. Moreover, it had 75.0% accuracy, 76.0% sensitivity, 74.0% specificity, and 75.0% F1-score. We found an overlap between the regions within the default mode network, thus generating high classification values.

Conclusion

The proposed model based on the 2-[¹⁸F]FDG PET imaging data and a DL framework might successfully classify amyloid PET positivity in clinical practice, without performing amyloid PET, which have limited accessibility.

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Title: Deep learning-based amyloid PET positivity classification model in the Alzheimer’s disease continuum by using 2-[18F]FDG PET
Authors: Suhong Kim
Peter Lee
Kyeong Taek Oh
Min Soo Byun
Dahyun Yi
Jun Ho Lee
Yu Kyeong Kim
Byoung Seok Ye
Mi Jin Yun
Dong Young Lee
Yong Jeong
the Alzheimer’s Disease Neuroimaging Initiative
the KBASE Research Group
Publication date: 01-12-2021
Publisher: Springer Berlin Heidelberg
Keywords: Alzheimer's Disease
Dementia
Alzheimer's Disease
Dementia
Mild Cognitive Impairment
Published in: EJNMMI Research / Issue 1/2021
Electronic ISSN: 2191-219X
DOI: https://doi.org/10.1186/s13550-021-00798-3

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Deep learning-based amyloid PET positivity classification model in the Alzheimer’s disease continuum by using 2-[¹⁸F]FDG PET

Abstract

Background

Methods

Results

Conclusion

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Abstract

Background

Methods

Results

Conclusion

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