Alzheimer's Disease Progression Prediction

This field predicts how Alzheimer’s disease and related cognitive impairment may change, including conversion from mild cognitive impairment to dementia, future cognitive scores, and brain imaging changes. Common approaches combine longitudinal MRI, cognitive tests, biomarkers, and clinical records using recurrent networks or Transformers; continuous-time models such as Neural ODEs address irregular visits, while survival models estimate time-to-conversion and risk. Researchers also use Bayesian methods, disease-staging models, and personalized digital twins to represent uncertainty and individual trajectories. A central challenge is making predictions reliable across cohorts despite missing data and sparse visits, while addressing calibration, fairness, and data leakage.