Deep Learning for AD Risk Screening and Progression Prediction

ABSTRACT The rise of Alzheimer's disease (AD) immunotherapies underscores the need for accessible, time‐efficient biomarkers for early diagnosis. MRI‐based deep learning shows promise in this regard, yet few models have undergone comprehensive clinical validation. Using the SILCODE cohort (722 Chinese participants, 1,105 scans), we test the transportability of our classifier (pretrained on Western samples) to an independent Chinese cohort without retraining. The associations between risk scores and disease progression, cognitive performance, and plasma biomarkers are examined. In addition, an interpretable framework is developed to generate individualized brain risk maps and identify subtypes. The model classifies AD with an AUC of 0.913 and 95.2% sensitivity, identifying 90.5% of high‐risk individuals 2–3 years before clinical diagnosis. Risk scores correlate significantly with cognitive and plasma indices, particularly p‐tau 217. Brain risk maps reveal AD subtypes, including a subgroup with rapid cognitive decline. These findings establish a population‐robust, clinically relevant MRI‐based model for early AD detection and risk stratification.