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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 \u2014 initially trained on North American data \u2014 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 progression times. By integrating an interpretable deep learning brain risk map approach, we identified AD brain subtypes, including an MCI subtype associated with rapid cognitive decline. The model\u2019s risk scores showed significant correlations with cognitive measures and plasma biomarkers, such as tau proteins and neurofilament light chain (NfL). These findings underscore the exceptional generalizability and clinical utility of MRI-based deep learning models, especially in large and diverse populations, offering valuable tools for early therapeutic intervention. The model has been made open-source and deployed to a free online website for AD risk prediction, to assist in early screening and intervention.<\/jats:p>","DOI":"10.1101\/2025.05.06.25326606","type":"posted-content","created":{"date-parts":[[2025,5,7]],"date-time":"2025-05-07T22:00:23Z","timestamp":1746655223000},"source":"Crossref","is-referenced-by-count":1,"title":["Interpretable MRI-Based Deep Learning for Alzheimer\u2019s Risk and 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