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Establishing the longitudinal hemodynamic mapping framework for wearable-driven coronary digital twins.

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This study introduces a new framework for creating patient-specific cardiovascular digital twins. It enables long-term hemodynamic mapping over millions of heartbeats for improved disease monitoring.

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Area of Science:

  • Cardiovascular physiology
  • Computational fluid dynamics
  • Medical imaging and modeling

Background:

  • Accurate coronary hemodynamics monitoring is vital for early cardiovascular disease detection and progression tracking.
  • Existing computational models are limited to single heartbeats, hindering longitudinal analysis.
  • There is a need for efficient digital twin frameworks that integrate continuous physiological data for long-term hemodynamic pattern analysis.

Purpose of the Study:

  • To introduce the longitudinal hemodynamic mapping framework (LHMF) for creating patient-specific cardiovascular digital twins.
  • To address computational challenges, dynamic boundary conditions, and resource accessibility for clinical translation.
  • To enable the reconstruction of longitudinal hemodynamic maps (LHMs) over extended periods.

Main Methods:

  • Development of the longitudinal hemodynamic mapping framework (LHMF).
  • Validation against explicit data for 750 heartbeats, showing negligible error (0.0002-0.004%).
  • Deployment on traditional and cloud platforms for high-throughput simulations.
  • Introduction of LHMF_C for clustering similar heartbeats to optimize simulations.

Main Results:

  • LHMF demonstrated high accuracy and negligible error compared to explicit methods.
  • Successful deployment and high-throughput simulation capabilities on heterogeneous computing systems.
  • LHMF_C accurately reconstructed longitudinal hemodynamic maps by clustering similar heartbeats.
  • The framework successfully captured 3D hemodynamics over 4.5 million heartbeats.

Conclusions:

  • The longitudinal hemodynamic mapping framework (LHMF) offers a computationally tractable and accessible approach for cardiovascular digital twins.
  • LHMF enables accurate, long-term hemodynamic simulations crucial for disease monitoring.
  • This work paves the way for advanced clinical translation of patient-specific cardiovascular digital twins.