Feature Integration of [ <sup>18</sup> F]FDG PET Brain Imaging Using Deep Learning for Sensitive Cognitive Decline Detection
Combining PET imaging with region-level quantification and deep learning improves diagnostic performance over single-feature based models. Notably, fusion-based approaches enhanced sensitivity to cognitive decline. This multimodal strategy offers a more data-efficient and accurate approach for classifying cognitive decline and supports broader PET application in clinical settings.
Authors
- Sangil Kim
- S. H. Kim
- Yeona Kang (ORCID: https://orcid.org/0000-0003-3384-435X)
- Yeji Lee (ORCID: https://orcid.org/0000-0001-6795-7542)
- Alzheimer's Disease Neuroimaging Initiative
Institutions
- Howard University (US)
- Pusan National University (KR)
- Defiance College (US)
Publication Details
- Journal
- medRxiv
- Published
- 2026-01-28
- DOI
- https://doi.org/10.64898/2026.01.22.26344669
- Primary Topic
- Dementia and Cognitive Impairment Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- U.S. Department of Defense
- Alzheimer's Association
- Bristol-Myers Squibb
- Eli Lilly and Company
- Pfizer
- Biogen
- University of Southern California
- Meso Scale Diagnostics
- BioClinica
- Novartis Pharmaceuticals Corporation
- Northern California Institute for Research and Education
- National Research Foundation
- National Research Foundation of Korea
- Eisai
- Servier
- H. Lundbeck A/S
- Ministry of Science and ICT, South Korea
- IXICO
- National Institutes of Health
- Genentech
- Canadian Institutes of Health Research