ADMarker: A Multi-Modal Federated Learning System for Monitoring Digital Biomarkers of Alzheimer's Disease

Alzheimer's Disease (AD) and related dementia are a growing global health challenge due to the aging population. In this paper, we present ADMarker, the first end-to-end system that integrates multi-modal sensors and new federated learning algorithms for detecting multidimensional AD digital bi...

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Bibliographic Details
Published in:arXiv.org 2024-04
Main Authors: Ouyang, Xiaomin, Shuai, Xian, Yang, Li, Pan, Li, Zhang, Xifan, Fu, Heming, Cheng, Sitong, Wang, Xinyan, Cao, Shihua, Jiang, Xin, Mok, Hazel, Yan, Zhenyu, Fung Yu, Doris Sau, Kwok, Timothy, Xing, Guoliang
Format: Article
Language:English
Subjects:
Aging (natural)
Algorithms
Alzheimer's disease
Biomarkers
Heterogeneity
Machine learning
Public health
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Summary:Alzheimer's Disease (AD) and related dementia are a growing global health challenge due to the aging population. In this paper, we present ADMarker, the first end-to-end system that integrates multi-modal sensors and new federated learning algorithms for detecting multidimensional AD digital biomarkers in natural living environments. ADMarker features a novel three-stage multi-modal federated learning architecture that can accurately detect digital biomarkers in a privacy-preserving manner. Our approach collectively addresses several major real-world challenges, such as limited data labels, data heterogeneity, and limited computing resources. We built a compact multi-modality hardware system and deployed it in a four-week clinical trial involving 91 elderly participants. The results indicate that ADMarker can accurately detect a comprehensive set of digital biomarkers with up to 93.8% accuracy and identify early AD with an average of 88.9% accuracy. ADMarker offers a new platform that can allow AD clinicians to characterize and track the complex correlation between multidimensional interpretable digital biomarkers, demographic factors of patients, and AD diagnosis in a longitudinal manner.
ISSN:2331-8422