Advances in artificial intelligence-driven MRI research in Alzheimer's disease.
Authors
Affiliations (6)
Affiliations (6)
- Department of Radiology, The First People's Hospital of Neijiang, Neijaing, Sichuan, China.
- Department of Radiology, No. 926 Hospital, Joint Logistics Support Force of PLA, Kaiyuan, Yunnan, China.
- 7T Magnetic Resonance Imaging Translational Medical Center, Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China.
- Department of Ultrasound, The First People's Hospital of Neijiang, Neijiang, Sichuan, China.
- Department of Radiology, Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
- Department of Nuclear Medicine, The First People's Hospital of Neijiang, Neijiang, Sichuan, China.
Abstract
Alzheimer's disease (AD), the most prevalent neurodegenerative disorder, poses substantial challenges for early diagnosis and longitudinal monitoring-two cornerstones of effective clinical management and therapeutic intervention. Magnetic resonance imaging (MRI), owing to its non-invasiveness and high spatial resolution, has emerged as an indispensable tool in AD research. In recent years, artificial intelligence (AI), particularly deep learning (DL) and machine learning (ML) techniques, has demonstrated remarkable potential for automating MRI analysis, extracting subtle imaging features, and supporting objective disease prediction. This review comprehensively summarizes the latest advances in AI-driven MRI applications for AD diagnosis, staging, prognosis, and adjuvant therapeutic monitoring. It highlights key progress in AI model development, multimodal data processing, and clinical translational value. Furthermore, current bottlenecks, including data heterogeneity, model interpretability, and real-world deployment, are systematically discussed, and future directions are outlined to enhance the precision and clinical utility of AI-assisted MRI in AD. Overall, AI-MRI has evolved from laboratory validation toward early clinical adoption, with explainability and multi-center generalizability identified as critical priorities in the next research phase.