ADMarker: A Multi-Modal Federated Learning System for Monitoring Digital Biomarkers of Alzheimer's Disease
This provides a privacy-preserving platform for AD clinicians to monitor and correlate digital biomarkers with diagnosis, addressing real-world challenges like data heterogeneity and limited labels, though it appears incremental as it builds on existing federated learning and multi-modal approaches.
The authors tackled the problem of detecting Alzheimer's disease digital biomarkers in natural environments by developing ADMarker, a multi-modal federated learning system, achieving up to 93.8% accuracy for biomarker detection and 88.9% accuracy for early AD identification in a clinical trial with 91 participants.
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.