A 3D convolutional neural network to classify subjects as Alzheimer's disease, frontotemporal dementia or healthy controls using brain 18F-FDG PET
Year: 2024
Type: article
Abstract: With the arrival of disease-modifying drugs, neurodegenerative diseases will require an accurate diagnosis for optimal treatment. Convolutional neural networks are powerful deep learning techniques that can provide great help to physicians in image analysis. The purpose of this study is to introduce and validate a 3D neural network for classification of Alzheimer's disease (AD), frontotemporal dementia (FTD) or cognitively normal (CN) subjects based on brain glucose metabolism. Retrospective [18F]-FDG-PET scans of 199 CE, 192 FTD and 200 CN subjects were collected from our local database, Alzheimer's disease and frontotemporal lobar degeneration neuroimaging initiatives. Training and test sets were created using randomization on a 90 %-10 % basis, and training of a 3D VGG16-like neural network was performed using data augmentation and cross-validation. Performance was compared to clinical interpretation by three specialists in the independent test set. Regions determining classification were identified in an occlusion experiment and Gradient-weighted Class Activation Mapping. Test set subjects were age- and sex-matched across categories. The model achieved an overall 89.8 % accuracy in predicting the class of test scans. Areas under the ROC curves were 93.3 % for AD, 95.3 % for FTD, and 99.9 % for CN. The physicians' consensus showed a 69.5 % accuracy, and there was substantial agreement between them (kappa = 0.61, 95 % CI: 0.49-0.73). To our knowledge, this is the first study to introduce a deep learning model able to discriminate AD and FTD based on [18F]-FDG PET scans, and to isolate CN subjects with excellent accuracy. These initial results are promising and hint at the potential for generalization to data from other centers.
Source: NeuroImage
Institutions: University College London Hospitals NHS Foundation Trust, Centre Hospitalier Universitaire de Lille, University College London, Centre National de la Recherche Scientifique, Inserm, Université de Lille, Institut Pasteur de Lille, Plateformes Lilloises en Biologie et Santé, Lille Neurosciences & Cognition, OncoThAI
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,450
Funders: U.S. Department of Defense, Alzheimer's Association, Bristol-Myers Squibb, Eli Lilly and Company, Biogen, University of Southern California, F. Hoffmann-La Roche, Alzheimer's Disease Neuroimaging Initiative, BioClinica, Eisai, IXICO, National Institutes of Health, Genentech, University of California, San Francisco, National Institute on Aging, National Institute of Biomedical Imaging and Bioengineering
Awards: G1012222616, G104586243, G1554142629, G1650819308, G2189010064, G2340207221, G2522063444, G3131374986, G3553156159, G3710456697, G3870774570, G3966393351, G4119534169, G43180419, G4475667458, G4501223628, G4800177743, G5207404921, G5365730499, EFFECTS OF TRAUMATIC BRAIN INJURY (TBI) AND POST TRAUMATIC STRESS DISORDER (PTSD) ON ALZHEIMER'S DISEASE (AD) IN VETERANS USING IMAGING AND BIOMARKERS IN THE AD NEUROIMAGING INITIATIVE (ADNI)", G5654639750, Alzheimer's Disease Neuroimaging Initiative, G5850682332, G6210868510, G6371410416, G6412110892, G6719828067, G6792506723, G6899163935, The Frontotemporal Lobar Degeneration Neuroimaging Initiative, G6948409098, G7298697282, G764699806, G781725486, G8228018387, G8895907669, G8897063270, G8957659042, G9000039018, G9000122335
OpenAlex ID: W4391512868