Deep Learning for AD Risk Screening and Progression Prediction.
Authors
Affiliations (14)
Affiliations (14)
- State Key Laboratory of Cognitive Science and Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, People's Republic of China.
- Department of Psychology, University of the Chinese Academy of Sciences, Beijing, People's Republic of China.
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands.
- Department of Psychology and Neuroscience, University of North Carolina At Chapel Hill, Chapel Hill, North Carolina, USA.
- Department of Neurology, National Clinical Research Center for Geriatric Diseases, XuanWu Hospital of Capital Medical University, Beijing, People's Republic of China.
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, People's Republic of China.
- Department of Neurology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, People's Republic of China.
- Department of Neurology of the Affiliated Hospital of Hangzhou Normal University, Hangzhou, People's Republic of China.
- Department of Geriatrics of Changji People's Hospital, Changji, People's Republic of China.
- Department of Child and Adolescent Psychiatry, New York University Grossman School of Medicine, New York, New York, USA.
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine, University of Southern California, Los Angeles, California, USA.
- School of Biomedical Engineering, Hainan University, Haikou, People's Republic of China.
- Department of Psychological and Cognitive Sciences, Tsinghua University, Beijing, People's Republic of China.
- Research Center for Culture and Psychology, Tsinghua University, Beijing, People's Republic of China.
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.