Yannet Interian

Abstract Deep learning (DL) has shown success in predicting Alzheimer’s disease (AD) diagnosis, yet continuous measures such as cognitive assessment remain critical for richer prognosis, trajectory tracking and clinical trial enrichment. Current neurocognitive batteries are time-consuming, and the few DL models predicting cognition require expensive multimodal neuroimaging and longitudinal data.