AccScience Publishing / AIH / Online First / DOI: 10.36922/AIH025140026
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ORIGINAL RESEARCH ARTICLE

Deep vision transformers in neurodegenerative disease diagnosis using 18F-fluorodeoxyglucose positron emission tomography scans and anatomical brain atlas

Pooriya Khorramyar1* ,  Amira Soliman1 ,  Farzaneh Etminani1 ,  Stefan Byttner1
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1 Center for Applied Intelligent Systems Research in Health (CAISR Health), The School of Information Technology, Halmstad University, Halmstad, Halland, Sweden
AIH, 025140026 https://doi.org/10.36922/AIH025140026
Received: 31 March 2025 | Revised: 22 May 2025 | Accepted: 26 May 2025 | Published online: 18 June 2025
(This article belongs to the Special Issue Artificial intelligence for diagnosing brain diseases)
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

This research explores adapting vision transformers (ViTs) to classify neurodegenerative diseases while ensuring their decision-making process is interpretable. We developed a model to classify 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) brain scans into three categories: cognitively normal, mild cognitive impairment, and Alzheimer’s disease (AD). The dataset utilized in this research contains 580 samples of 18F-FDG PET scans obtained from the AD neuroimaging initiative. The proposed model obtained an F1 score of 81% (macro-average of all classes) on the test dataset, a significant performance improvement compared to the literature. Furthermore, we combined the model’s attention maps with the Automated Anatomical Atlas 3, which represents a digital brain map, to identify the most influential areas on the model’s predictions and to conduct a regions’ importance study as a step toward explainability. We demonstrated that ViTs can achieve competitive performance compared to convolutional neural networks while enabling the development of explainable models without extra computations due to the attention mechanism.

Keywords
Vision transformer
Neurodegenerative disease
18F-FDG PET
Medical image analysis
Brain scan
Deep neural network
Funding
Data collection and sharing for this project was funded by the ADNI (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Dec 5, 2024 12:30 PM Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.;Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research provided funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (https://www.fnih. org/). The grantee organization is the Northern California Institute for Research and Education, and the study was coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data were disseminated by the Laboratory for Neuro Imaging at the University of Southern California. Farzaneh Etminani and Amira Soliman are supported by Center for Applied Intelligent Systems Research in Health (CAISR Health) funded by Knowledge Foundation (grant no.: 20200208 01H).
Conflict of interest
The authors declare no conflicts of interest.
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Artificial Intelligence in Health, Electronic ISSN: 3029-2387 Print ISSN: 3041-0894, Published by AccScience Publishing
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