Digital twins and simulations in transcatheter coronary and structural heart interventions

TL;DR

Digital twin technology is being used in heart interventions to create virtual models for better planning and outcomes in procedures like stent placement and valve replacement.

Contribution

This paper reviews how digital twins are applied in coronary and structural heart interventions, highlighting their role in improving procedural accuracy and outcomes.

Findings

Digital twins help estimate physiological indices and guide stent placement in coronary interventions.

Simulation platforms model device-anatomy interactions and support valve sizing in structural heart procedures.

Challenges like model validation and workflow complexity limit broader adoption of digital twin technologies.

Abstract

Digital twin technology, which enables the creation of patient-specific virtual models, is increasingly applied in interventional cardiology to support personalized procedural planning and risk assessment. This review examines current applications of digital twins in coronary and structural heart interventions, including percutaneous coronary intervention (PCI), transcatheter aortic valve replacement (TAVR), transcatheter mitral valve replacement (TMVR), and left atrial appendage closure (LAAC). In coronary interventions, digital simulations based on computed tomography or angiography can estimate physiological indices, guide stent placement, and predict post-procedural hemodynamics. For structural interventions, simulation platforms generate 3D reconstructions from imaging data to model device–anatomy interactions, support valve sizing, and assess risks such as paravalvular leak or left ventricular outflow tract obstruction. Several tools are already integrated into clinical workflows, with growing evidence supporting their utility in improving planning accuracy and procedural outcomes. Nonetheless, broader adoption is limited by challenges related to model validation, data integration, workflow complexity, and regulatory constraints. In particular, validation remains difficult for procedures performed less frequently, such as TMVR. Ongoing developments in artificial intelligence and computational methods may enhance model speed and accuracy, enabling wider and more efficient clinical use. Digital twin technologies represent a promising direction for advancing precision medicine in transcatheter coronary and structural heart interventions.

Graphical AbstractExamples of Simulations of Transcatheter Coronary and Structural Heart interventions. The technologies depicted are representative examples; other platforms exist or are under development, but not all could be included within the scope of this review. CT, computed tomography; FFRangio, angiography-derived fractional flow reserve; FFRCT, fractional flow reserve derived from coronary CT angiography; LAAC, left atrial appendage closure; PCI, percutaneous coronary intervention; QFR, quantitative flow ratio; TAVR, transcatheter aortic valve replacement; TMVR, transcatheter mitral valve replacement; vFFR, virtual fractional flow reserve.

Examples of Simulations of Transcatheter Coronary and Structural Heart interventions. The technologies depicted are representative examples; other platforms exist or are under development, but not all could be included within the scope of this review. CT, computed tomography; FFRangio, angiography-derived fractional flow reserve; FFRCT, fractional flow reserve derived from coronary CT angiography; LAAC, left atrial appendage closure; PCI, percutaneous coronary intervention; QFR, quantitative flow ratio; TAVR, transcatheter aortic valve replacement; TMVR, transcatheter mitral valve replacement; vFFR, virtual fractional flow reserve.

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