How can computational modeling and virtual prototyping be utilized to design low-cost, efficient blood flow sensors for early detection of cardiovascular blockages?

Background: Cardiovascular diseases (CVDs) are a leading global cause of death, necessitating early diagnosis and real-time monitoring. Drug-eluting stents (DES) have improved coronary artery disease treatment but lack integrated diagnostics for monitoring blood flow and complications like restenosis. This project develops a computational model for a blood flow sensor embedded in DES to detect flow rate abnormalities indicative of arterial blockages.
Methods: We simulated blood flow in a stented artery using the Navier-Stokes equations, incorporating physiological parameters such as flow speed and pressure gradients. A Python-based framework utilizing libraries like PyGame, Matplotlib, and NumPy was created to prototype and simulate sensor functionalities. Data were collected to evaluate the sensor's performance detecting flow rate changes.
Results: The model revealed critical turbulence and pressure fluctuations in regions associated with arterial blockages. The virtual sensor prototype accurately detected flow abnormalities, with a sensitivity of 92% for identifying occlusions. Numerical analysis confirmed that flow disturbances correlated with the degree and location of blockages. This computational approach reduced reliance on physical prototyping, streamlining the development process.
Conclusion: This study demonstrates the feasibility of computationally modeled blood flow sensors for DES in cardiovascular monitoring. The findings suggest potential applications in early-stage blockage management and personalized medicine. Future work will focus on enhancing sensor sensitivity, integrating machine learning for predictive analytics, and transitioning to laboratory validation, paving the way for innovative, low-cost cardiovascular diagnostics.