Artificial neural network application to characterize patients with Alzheimer ’s disease and Mild Cognitive Impairment from Cognitive Normal.

Abstract

AbstractBackgroundThis study aimed to build an artificial neural network model to differentiate patients with various diagnosis, including Alzheimer’s disease (AD), mild cognitive impairment (MCI) from cognitive normal (CN) using structural MRI images.MethodsMRI T1 image of 580 participants (97 AD, 291 MCI and 192 CN) were downloaded from ADNI, which is a publicly available database. The MPRAGE T1W MRI were pre‐processed by SPM software, including re‐orientation, segmentation and normalized to the whole brain mask template. 3D slicer software with pyradiomics extension were employed to extract the 107 radiomics features within the brain. Also, 45 brain regional volumes were segmented and calculated using Freesurfer. The AD, MCI and CN groups were divided into a training group and a validation group based on 70:30 ratio. Ten‐fold cross validation were performed to minimize the chance of overfitting. Three feed forward neural network model were built using the neural network toolbox of MATLAB, with one model using 107 radiomics alone (R model), one with 45 brain regional volumes (V Model) and the last one with both radiomics and volumes features (R+V model).ResultsThe receiver operating characteristics (ROC) curves showed that the V model and the R+V model achieved excellent accuracy, with 96.6% and 94.8% respectively. The area under the curve (AUC) was 99.6% and 97.9% respectively.ConclusionThe neural network model can accurately differentiate AD, MCI from HC. The application is practical and can be fully automatic, which can be used for dementia screening.