Novel Deep Learning Model To Assess Alzheimer’s Disease Progression Risk

In a recent study published in eClinical Medicine, researchers developed and validated a novel deep learning (DL) based model to assess the risk of progression through different stages of cognitive decline in Alzheimer’s disease (AD). The model was trained using clinical and T1 relaxation-weighted MRI data from 1,370 individuals in the AD Neuroimaging Initiative (ADNI) group and externally validated using data from the Australian Imaging Biomarkers and Lifestyle Study of Aging (AIBL) group. The team evaluated the model’s ability to identify clinically and physiologically significant trends in AD trajectories, and a Mendelian randomization study was also conducted to evaluate the causal relationships between the identified patterns and AD progression.