18F-FDG-PET-based deep learning for predicting cognitive decline in non-demented elderly across the Alzheimer’s disease clinical spectrum
The <sup>18</sup>F-FDG-PET-based DL model demonstrated the potential to improve cognitive decline prediction beyond clinical information, and conventional measures from <sup>18</sup>F-FDG-PET and amyloid PET and may prove useful for clinical trial recruitment and individualized management.
Authors
- Yoon Seong Choi (ORCID: https://orcid.org/0000-0002-6034-9912)
- Ling Ling Chan (ORCID: https://orcid.org/0000-0001-7603-7334)
- Beomseok Sohn (ORCID: https://orcid.org/0000-0002-6765-8056)
- Jeong Ryong Lee
- Dosik Hwang (ORCID: https://orcid.org/0000-0002-2217-2837)
- Seok Jong Chung (ORCID: https://orcid.org/0000-0001-6086-3199)
- Wanying Xie
Institutions
- National University of Singapore (SG)
- Yonsei University (KR)
- Singapore General Hospital (SG)
- Samsung Medical Center (KR)
- Duke-NUS Medical School (SG)
- Sungkyunkwan University (KR)
Publication Details
- Journal
- Radiology Advances
- Published
- 2024-08-10
- DOI
- https://doi.org/10.1093/radadv/umae021
- Citations
- 1
- Primary Topic
- Dementia and Cognitive Impairment Research
- Type
- article
- Field-Weighted Citation Impact
- 0.39
Funders
- Alzheimer's Disease Neuroimaging Initiative
- National Research Foundation
- National University of Singapore
- National Research Foundation of Korea
- Ministry of Science and ICT, South Korea
- Samsung
- National Bioscience Database Center