The use of back propagation neural networks and 18F-Florbetapir PET for early detection of Alzheimer's disease using Alzheimer's Disease Neuroimaging Initiative database.
Ilker OzsahinBoran SekerogluGreta S P MokPublished in: PloS one (2019)
Amyloid beta (Aβ) plaques aggregation is considered as the "start" of the degenerative process that manifests years before the clinical symptoms appear in Alzheimer's Disease (AD). The aim of this study is to use back propagation neural networks (BPNNs) in 18F-florbetapir PET data for automated classification of four patient groups including AD, late mild cognitive impairment (LMCI), early mild cognitive impairment (EMCI), and significant memory concern (SMC), versus normal control (NC) for early AD detection. Five hundred images for AD, LMCI, EMCI, SMC, and NC, i.e., 100 images for each group, were used from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The results showed that the automated classification of NC/AD produced a high accuracy of 87.9%, while the results for the prodromal stages of the disease were 66.4%, 60.0%, and 52.9% for NC/LCMI, NC/EMCI and NC/SMC, respectively. The proposed method together with the image preparation steps can be used for early AD detection and classification with high accuracy using Aβ PET dataset.
Keyphrases
- mild cognitive impairment
- deep learning
- cognitive decline
- machine learning
- neural network
- computed tomography
- positron emission tomography
- pet ct
- artificial intelligence
- emergency department
- quality improvement
- high throughput
- big data
- mass spectrometry
- case report
- working memory
- electronic health record
- parkinson disease
- data analysis
- simultaneous determination
- drug induced
- real time pcr