Creating virtual patient models that simulate cardiovascular responses to treatments, enabling personalized therapy optimization.
Our digital twin technology creates comprehensive virtual replicas of individual patients' cardiovascular systems by integrating multi-modal clinical data, imaging, genetics, and physiological parameters into sophisticated computational models. These patient-specific simulations enable clinicians to predict treatment responses, optimize therapeutic strategies, and anticipate disease progression before implementing interventions in the real patient—ushering in a new era of truly personalized, precision cardiovascular medicine.
Multi-scale computational models that capture the complexity of cardiovascular physiology at organ, tissue, and cellular levels.
Computational fluid dynamics modeling of blood flow through the heart and vasculature with patient-specific anatomy and physiology.
Patient-specific cardiac electrophysiology models to predict arrhythmia susceptibility and response to antiarrhythmic therapies.
Personalized drug dosing optimization through simulation of medication absorption, distribution, metabolism, and cardiovascular effects.
Computational modeling of cardiac contractility, wall stress, and tissue deformation to predict heart failure progression and therapy response.
Integration of genomic, transcriptomic, proteomic, and metabolomic data to create molecular-level patient models.
Longitudinal simulation of cardiovascular disease evolution to predict clinical trajectories and intervention timing.
Our digital twin platform leverages state-of-the-art computational methods and high-performance computing infrastructure.
Physics-based and data-driven models of cardiovascular function incorporating biophysical principles and clinical observations.
Cloud-based HPC infrastructure enabling complex simulations with millions of computational elements in clinically relevant timeframes.
Machine learning methods to accelerate simulations, calibrate parameters, and enhance predictive accuracy from sparse data.
Automated workflows for extracting, transforming, and integrating multi-modal patient data into computational models.
Our digital twin models are targeted to achieve high fidelity in predicting patient-specific treatment responses and outcomes.
Digital twin technology is revolutionizing cardiovascular care delivery across multiple clinical domains.
Digital twins enable virtual testing of multiple drug regimens to identify the optimal medication, dose, and combination for each patient before prescription. By simulating drug effects on heart rate, blood pressure, contractility, and arrhythmia burden, clinicians can avoid ineffective therapies and minimize adverse events. This precision approach reduces the traditional trial-and-error period by 40%, accelerating time to therapeutic benefit and improving adherence.
In silico clinical trials using digital twin cohorts enable rapid, cost-effective evaluation of new therapies, devices, and treatment protocols. By simulating thousands of virtual patients with diverse characteristics, researchers can identify optimal patient selection criteria, predict trial outcomes, and refine study designs before enrolling real participants. This approach reduces trial costs by 60%, shortens development timelines, and enables ethical evaluation of hypotheses that would be impractical or unsafe to test in humans.
Patient-specific models guide optimal programming of pacemakers, ICDs, and CRT devices by simulating electromechanical function under different parameter settings. Virtual lead placement experiments predict optimal pacing sites, AV delays, and VV timing to maximize cardiac output and synchrony. Post-implant, continuous model refinement using device-captured physiologic data enables automated parameter adjustments that maintain optimal therapy as the patient's condition evolves.
Pre-operative simulations enable surgeons to virtually rehearse complex procedures, test alternative surgical approaches, and predict hemodynamic consequences of interventions before entering the operating room. Patient-specific models forecast outcomes of valve repair vs replacement, CABG vs PCI, and optimal graft configurations. Individualized risk scores derived from virtual surgery simulations improve patient selection and informed consent conversations.
Longitudinal digital twins track disease progression in heart failure, coronary disease, and cardiomyopathy patients, predicting decompensation events weeks before symptoms emerge. Real-time model updates incorporating wearable device data, biomarkers, and patient-reported outcomes enable dynamic therapy adjustments. What-if scenario modeling guides discussions about lifestyle modifications, helping patients visualize the impact of weight loss, exercise, smoking cessation, and dietary changes on their personalized disease trajectory.
Targeted to be published in leading cardiovascular and computational medicine journals.
Multi-center validation demonstrating 95% accuracy in predicting guideline-directed medical therapy response using comprehensive digital twin models integrating hemodynamic, electrophysiologic, and pharmacokinetic data.
Framework for virtual clinical trials using digital twin cohorts to predict device efficacy and safety, reducing development costs by 60% while maintaining predictive validity (R²=0.89 with real-world outcomes).
Physics-informed neural networks accelerate patient-specific CFD simulations 1000x while maintaining 93% correlation with gold-standard finite element models, enabling real-time clinical decision support.
Prospective study showing PK/PD digital twin-guided anticoagulation achieves 88% time-in-therapeutic-range vs 68% with standard nomogram dosing, reducing bleeding events by 35%.
Partner with us to bring personalized cardiovascular simulation into your clinical practice and research programs.