Digital Twin Technology in Cardiovascular Care: Transforming Patient Monitoring and Surgical Planning Through Artificial Intelligence

Abstract

Digital twin technology—the bidirectional coupling of high-fidelity computational models with continuously assimilated patient data—has emerged as a pragmatic pathway toward precision cardiovascular care. By integrating physics-based heart and vascular models with multimodal data streams (ECG/PPG, wearable telemetry, imaging, labs, and EHR), digital twins enable individualized state estimation, prospective risk stratification, and closed-loop decision support. In patient monitoring, twin-in-the-loop filters can detect latent decompensation and therapy drift while quantifying uncertainty. In procedural planning, AI-augmented electromechanical and hemodynamic simulators support target selection and lesion-set optimization for electrophysiology and endovascular interventions, with growing evidence of concordance between simulated and invasive substrates. Methodologically, recent work couples Bayesian/PDE-constrained inference and surrogate neural operators for real-time personalization, and leverages cohort-level twin populations for virtual trials and outcome prediction. Yet translation at scale still hinges on verifiable model validity, data governance, computational tractability at the bedside, and prospective demonstration of clinical and health-economic utility. This paper synthesizes current advances across sensing, modeling, and machine learning that operationalize cardiovascular digital twins for continuous monitoring and surgical planning, outlines validation and regulatory considerations, and proposes a research agenda emphasizing hybrid mechanistic–statistical modeling, prospective multi-site studies, and interoperable, privacy-preserving deployment.