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

ABSTRACT: Understanding the evolving nature of coronary hemodynamics is crucial for early disease detection and monitoring progression. We require digital twins that mimic a patient's circulatory system by integrating continuous physiological data and computing hemodynamic patterns over months. Current models match clinical flow measurements but are limited to single heartbeats. To this end, we introduced the longitudinal hemodynamic mapping framework (LHMF), designed to tackle critical challenges: (1) computational intractability of explicit methods; (2) boundary conditions reflecting varying activity states; and (3) accessible computing resources for clinical translation. We show negligible error (0.0002-0.004%) between LHMF and explicit data of 750 heartbeats. We deployed LHMF across traditional

SUBMITTER: Tanade C 

PROVIDER: S-EPMC11379815 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature