A novel convolutional neural network model based on voxel-based morphometry of imaging data in predicting the prognosis of patients with mild cognitive impairment [Voksel-tabanlı morfometri analizine dayanan yenilikçi yaklaşımlı evrişimsel sinir ağları ile HBB evresinde prognozun tahmin edilmesi]

Objective: nowadays,it is of great interest to identify neuroimaging biomarkers for the early detection of alzheimer's disease (ad). it is considered that approximately half of patients with a diagnosis of mild cognitive impairment (mci) eventually develop alzheimer's disease,and the other half remain stable. in this context,a novel convolutional neural network (cnn) based on voxel-based morphometric analysis is proposed to predict the prognosis of patients with mci using their baseline structural magnetic resonance (mr) images. methods: two groups of patients were identified among 305 patients with a diagnosis of mci,those who developed alzheimer’s disease during their follow-up (n=140),and those who remained stable in the mci state (n=165). the baseline structural mr images of the patients were used for training and evaluating the proposed prediction model. voxel-based morphometry generated from the baseline structural mr images was used to obtain significant volume of interests (vois) related with gray matter damage. then,a convolutional neural network was trained to extract prognostic features from mr images using a set of convolutional feature detectors acquired by the training of a patch-based autoencoder. results: this work achieved an accuracy of 78.7%,slightly superior (more than 4%) to a reference study,for predicting the risk of developing alzheimer's disease for patients with mci. conclusion: the results of this study show that the use of a convolutional neural network using significant topographic regions of the brain is successful in predicting the risk of developing alzheimer’s disease for patients with mci. © 2017,ege university press. all rights reserved.