A cardiovascular modeling framework for enabling personalized healthcare : A Digital Twin Approach

Cardiovascular illnesses continue to be the world’s top cause of death, yet there are few resources available for individualized real-time diagnosis and therapy optimization. The lack of precise patient-specific simulations impedes effective treatment techniques. This study offers a thorough framework for employing digital twin technologies to model and simulate the cardiovascular system in order to overcome this difficulty. A fresh viewpoint on cardiac dynamics is provided by the combination of time-varying compliance models and synthetic ECG validation. The framework leverages Fourier transforms for waveform reconstruction and sophisticated biomechanical modeling techniques to simulate heart activity. These approaches have undergone thorough validation using both known frameworks and real-world data. Variations in compliance over time and pressure-volume loops are important elements of the investigation. The findings show remarkable precision and fidelity in simulating heart dynamics, as confirmed by statistical metrics and peak pressure gradient (PPG) analysis for aortic stenosis. The method has low signal variability and captures genuine heart patterns. The results demonstrate how digital twin technology can accurately mimic cardiovascular physiology, providing a novel tool for precision medicine and individualized care. Real-time diagnosis and treatment planning could be transformed by more research into this technology.