The risk prediction of Alzheimer's disease based on the deep learning model of brain 18F-FDG positron emission tomography

Abstract

In order to predict the risks of Alzheimer's Disease (AD) based on the deep learning model of brain 18F-FDG positron emission tomography (PET), a total of 350 mild cognitive impairment (MCI) participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were selected as the research objects; in addition, the Convolutional Architecture for Fast Feature Embedding (CAFFE) was selected as the framework of the deep learning platform; the FDG PET image features of each participant were extracted by a deep convolution network model to construct the prediction and classification models; therefore, the MCI stage features were classified and the transformation was predicted. The results showed that in terms of the MCI transformation prediction, the sensitivity and specificity of conv3 classification were respectively 91.02% and 77.63%; in terms of the Late Mild Cognitive Impairment (LMCI) and Early Mild Cognitive Impairment (EMCI) classification, the accuracy of conv5 classification was 72.19%, and the sensitivity and specificity of conv5 were all 73% approximately. Thus, it was seen that the model constructed in the research could be used to solve the problems of MCI transformation prediction, which also had certain effects on the classifications of EMCI and LMCI. The risk prediction of AD based on the deep learning model of brain 18F-FDG PET discussed in the research matched the expected results. It provided a relatively accurate reference model for the prediction of AD. Despite the deficiencies of the research process, the research results have provided certain references and guidance for the future exploration of accurate AD prediction model; therefore, the research is of great significance.

Keywords: ANDI database; CAFFE; Deep convolution network model; MCI transformation prevention; Sensitivity.

Fig. 1

Brains of AD patients presented by tomography.

Fig. 2

The schematic diagram of the brain at the initial stage of AD.

Fig. 3

Brain scans of healthy people and AD patients (the left image shows the brain of healthy people; the right image shows the brain of AD patients).

Fig. 4

Operating principles of CAFFE framework.

Fig. 5

Convolution process of LeNet-5.

Fig. 6

The schematic diagram of ReLU function.

Fig. 7

The flowchart of transformation prediction and classification methods.

Fig. 8

Comparison of brain PET scans between healthy participants and MCI participants after tracer injections (The left showed the healthy human brain; the right showed the MCI brain).