Genetic Algorithms for Optimized Diagnosis of Alzheimer’s Disease and Frontotemporal Dementia Using Fluorodeoxyglucose Positron Emission Tomography Imaging - Díaz-Álvarez, Josefa; Matias-Guiu, Jordi A.; Cabrera-Martín, María Nieves; Pytel, Vanesa; Segovia-Ríos, Ignacio; García-Gutiérrez, Fernando; Hernández-Lorenzo, Laura; Matias-Guiu, Jorge; Carreras, José Luis; Ayala, José L.; , Alzheimer’s Disease Neuroimaging Initiative (Frontiers in Aging Neuroscience, 2022)
Genetic algorithms have a proven capability to explore a large space of solutions, and deal with very large numbers of input features. We hypothesised that the application these algorithms to 18F-Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) may help in diagnosis of Alzheimer’s disease (AD) and Frontotemporal Dementia (FTD) by selecting the most meaningful features and automating diagnosis. We aimed to develop algorithms for the 3 main issues in the diagnosis: discrimination between patients with AD or FTD and healthy controls (HC), differential diagnosis between behavioural FTD (bvFTD) and AD, and differential diagnosis between primary progressive aphasia (PPA) variants. Genetic algorithms, customised with K-Nearest Neighbor and BayesNet Naives as the fitness function, were developed and compared with Principal Component Analysis. K-fold cross validation within the same sample and external validation with ADNI-3 samples were performed. External validation was performed for the algorithms distinguishing AD and HC. Our study supports the use of FDG-PET imaging, which allowed a very high accuracy rate for the diagnosis of AD, FTD, and related disorders. Genetic algorithms identified the most meaningful features with the minimum set of features, which may be relevant for automated assessment of brain FDG-PET images. Overall, our study contributes to the development of an automated, and optimised diagnosis of neurodegenerative disorders using brain metabolism.