Development of an artificial intelligence approach that employs genomic and brain imaging features to improve the diagnosis of Alzheimer’s Disease (AD)

In the past 20 years, genome wide association studies (GWAS) and brain imaging analyses have identified various genetic risk variants and structural brain differences between AD and cognitively normal (CN) subjects1–3. However, due to the relatively large and colinear feature space, it remains a challenge to use these variants and features to classify AD status at an individual level for personalized diagnosis, treatment, and prevention4. In this study, we applied a novel artificial intelligence‐based technique to transform both genomic and brain imaging features into 2D artificial images, and further utilized a deep convolutional neural network (CNN) to classify these artificial images5,6. Together, we show that the approach significantly improves the classification accuracy of AD subjects.