Alzheimer's Disease Classification Through Imaging Genetic Data With IGnet

The application of deep learning techniques to the detection and automated classification of Alzheimer's disease (AD) has recently gained considerable attention. The rapid progress in neuroimaging and sequencing techniques has enabled the generation of large-scale imaging genetic data for AD research. In this study, we developed a deep learning approach, IGnet, for automated AD classification using both magnetic resonance imaging (MRI) data and genetic sequencing data. The proposed approach integrates computer vision (CV) and natural language processing (NLP) techniques, with a deep three-dimensional convolutional network (3D CNN) being used to handle the three-dimensional MRI input and a Transformer encoder being used to manage the genetic sequence input. The proposed approach has been applied to the Alzheimer's Disease Neuroimaging Initiative (ADNI) data set. Using baseline MRI scans and selected single-nucleotide polymorphisms on chromosome 19, it achieved a classification accuracy of 83.78% and an area under the receiver operating characteristic curve (AUC-ROC) of 0.924 with the test set. The results demonstrate the great potential of using multi-disciplinary AI approaches to integrate imaging genetic data for the automated classification of AD.