Integrative approaches and clinical implications of harnessing multimodal digital technologies in early diagnosis of Alzheimer's disease
10.3760/cma.j.cn371468-20241115-00544
- VernacularTitle:多模态数字化技术在阿尔茨海默病早期识别中的整合应用
- Author:
Wenyuan ZHAO
1
;
Limin LIU
1
;
Hongming LIU
1
;
Jiayuan CHEN
1
;
Jing XIONG
1
Author Information
1. 昆明医科大学第二附属医院神经内科，昆明 650031
- Publication Type:Journal Article
- Keywords:
Alzheimer's disease;
Digital technologies;
Artificial intelligence;
Machine learning;
Early detection
- From:
Chinese Journal of Behavioral Medicine and Brain Science
2025;34(6):565-571
- CountryChina
- Language:Chinese
-
Abstract:
Alzheimer's disease (AD) is a progressive neurodegenerative disorder that seriously affects the health of the elderly, and the early diagnosis is crucial to slow down the progression of the disease. This review systematically examines the integrative applications of multimodal digital technologies in early AD identification, encompassing cognitive assessment, neuroimaging analysis, biomarker detection, and polygenic risk prediction, with the goal of enhancing diagnostic accuracy and operational efficiency. It was found that artificial intelligence-driven digital tools significantly improved screening efficiency by capturing subtle behavioral patterns and physiological signatures. Machine learning algorithms integrated with multimodal neuroimaging data optimize sensitivity in detecting structural brain abnormalities, while combinatorial analysis of digital biomarkers enables high-precision staging of AD pathology. Recent advancements highlight the critical role of digital technologies in facilitating multimodal biomarker integration and streamlining diagnostic workflows. The convergence of these innovative approaches provides a robust framework for early AD screening, offering patients accessible and efficient diagnostic pathways.
