Predicting Alzheimer Disease From Mild Cognitive Impairment With a Deep Belief Network Based on 18F-FDG-PET Images.

Predicting Alzheimer Disease From Mild Cognitive Impairment With a Deep Belief Network Based on 18F-FDG-PET Images. Mol Imaging. 2019 Jan-Dec;18:1536012119877285 Authors: Shen T, Jiang J, Lu J, Wang M, Zuo C, Yu Z, Yan Z Abstract OBJECTIVE: Accurate diagnosis of early Alzheimer disease (AD) plays a critical role in preventing the progression of memory impairment. We aimed to develop a new deep belief network (DBN) framework using 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) metabolic imaging to identify patients at the mild cognitive impairment (MCI) stage with presymptomatic AD and to discriminate them from other patients with MCI. METHODS: 18F-fluorodeoxyglucose-PET images of 109 patients recruited in the ongoing longitudinal Alzheimer's Disease Neuroimaging Initiative study were included in this analysis. Patients were grouped into 2 classes: (1) stable mild cognitive impairment (n = 62) or (2) progressive mild cognitive impairment (n = 47). Our framework is composed of 4 steps: (1) image preprocessing: normalization and smoothing; (2) identification of regions of interest (ROIs); (3) feature learning using deep neural networks; and (4) classification by support vector machine with 3 kernels. All classification experiments were performed with a 5-fold cross-validation. Accuracy, sensitivity, and specificity were used to validate the results. RESULT: A total of 1103 ROIs were obtained. One hundred...
Source: Molecular Imaging - Category: Radiology Tags: Mol Imaging Source Type: research