Toward Smart and Adaptive Endovascular Devices: The Role of Digital Twins in Precision Vascular Medicine

Endovascular implants, such as stents, flow diverters, and endografts, have reshaped cardiovascular therapy but still face trade-offs in restenosis, thrombosis, sizing, and fatigue. Conventional, prototype-driven development is expensive and ill-suited to broad design spaces or inter-patient variability. Emerging digital-twin (DT) approaches, combining imaging, FEA, CFD, and machine learning, enable patient-specific in-silico deployment, prediction of device-tissue interactions, and virtual trials. This review summarizes key design parameters, critiques the traditional pipeline, and surveys DT frameworks for stents and endografts, including reconstruction, simulation, and verification/validation. We outline opportunities for computational optimization, artificial intelligence, and additive manufacturing in personalized therapy.