Digital Twin-Enabled Personalized Cardiovascular Risk Assessment Using Wearable Sensor Data
Keywords:
digital twin; cardiovascular risk assessment; wearable sensors; data governance; machine learning; fairness; regulatory policyAbstract
Cardiovascular risk assessment has traditionally depended on population-derived statistical models that summarize risk factors over fixed time horizons. Wearable sensors now generate continuous physiological data streams, creating the possibility of a digital twin that reflects individual cardiovascular state and its temporal evolution. This article examines the system-level design of digital twin-enabled personalized cardiovascular risk assessment. It discusses the limitations of conventional risk scoring, the requirements of wearable data ingestion and signal processing, and the architectural choices that shape real-time and longitudinal model updating. The analysis extends beyond algorithmic performance to consider data governance, privacy, federated learning, clinical validation, robustness, fairness, and regulatory policy. A central argument is that personalized cardiovascular digital twins must be treated as socio-technical infrastructures whose value depends on organizational coordination, data quality, computational sustainability, and equity. The article identifies structural trade-offs between personalization and generalizability, responsiveness and resource consumption, and innovation and patient safety. It closes by outlining research directions that integrate systems engineering, clinical evaluation, and policy design.
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