Deep learning application for the classification of Alzheimer's disease using ¹⁸F-flortaucipir (AV-1451) tau positron emission tomography.

ABSTRACT: The positron emission tomography (PET) with ¹⁸F-flortaucipir can distinguish individuals with mild cognitive impairment (MCI) and Alzheimer's disease (AD) from cognitively unimpaired (CU) individuals. This study aimed to evaluate the utility of ¹⁸F-flortaucipir-PET images and multimodal data integration in the differentiation of CU from MCI or AD through DL. We used cross-sectional data (¹⁸F-flortaucipir-PET images, demographic and neuropsychological score) from the ADNI. All data for subjects (138 CU, 75 MCI, 63 AD) were acquired at baseline. The 2D convolutional neural network (CNN)-long short-term memory (LSTM) and 3D CNN were conducted. Multimodal learning was conducted by adding the clinical data with imaging data. Transfer learning was performed for c

SUBMITTER: Park SW 

PROVIDER: S-EPMC10198973 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature