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

TL;DR

ADMarker is an innovative multi-modal federated learning system designed to monitor digital biomarkers of Alzheimer's Disease in natural environments, addressing privacy, data heterogeneity, and limited labels, with high accuracy demonstrated in clinical trials.

Contribution

The paper introduces ADMarker, the first end-to-end multi-modal federated learning system for Alzheimer's digital biomarkers, integrating novel algorithms and hardware for real-world clinical application.

Findings

Achieved up to 93.8% accuracy in biomarker detection

Identified early AD with 88.9% accuracy

Validated system in a four-week clinical trial with 91 participants

Abstract

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.