Interpretable MRI-Based Deep Learning for Alzheimer's Risk and Progression.

ABSTRACT: Timely intervention for Alzheimer's disease (AD) requires early detection. The development of immunotherapies targeting amyloid-beta and tau underscores the need for accessible, time-efficient biomarkers for early diagnosis. Here, we directly applied our previously developed MRI-based deep learning model for AD to the large Chinese SILCODE cohort (722 participants, 1,105 brain MRI scans). The model - initially trained on North American data - demonstrated robust cross-ethnic generalization, without any retraining or fine-tuning, achieving an AUC of 91.3% in AD classification with a sensitivity of 95.2%. It successfully identified 86.7% of individuals at risk of AD progression more than 5 years in advance. Individuals identified as high-risk exhibited significantly shorter median progressio

SUBMITTER: Lu B 

PROVIDER: S-EPMC12083598 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature