Cardiac Digital Twin

• Cardiac digital twins are personalized computational models that integrate anatomical, electrophysiological, and biomechanical data to simulate individual heart function.
• They employ advanced techniques such as Bayesian calibration, finite element methods, and real-time data assimilation to predict arrhythmia risks and optimize therapies.
• Emerging frameworks stress modular software design, high-performance computing, and multi-modal data fusion to support clinical decision-making and regulatory standards.

A cardiac digital twin is a computational surrogate representing the multi-scale physiology of an individual heart. This construct integrates anatomical, electrophysiological, biomechanical, and clinical measurement data to simulate cardiac function, predict disease progression, and optimize interventions. Cardiac digital twins build on decades of mechanistic cardiac mathematical modeling, now augmented with patient-specific imaging, -omics, and device telemetry. While much contemporary digital twin research is oncology-focused, cardiac digital twins share core principles—model customization to individual subjects, assimilation of longitudinal multimodal data, Bayesian calibration for uncertainty quantification, and closed-loop support for clinical decision-making—that also underpin cardiovascular applications (Wang et al., 5 Mar 2024).

1. Mechanistic Foundations of Cardiac Digital Twins

Cardiac digital twins stem from biophysical models describing ionic currents, membrane potential propagation, and tissue biomechanics. Canonical frameworks include:

• Electrophysiology: Bidomain or monodomain PDEs for voltage $V(x,t)$ and extracellular potential, parameterized by cell-specific and tissue-specific conductances, as exemplified in cardiac electrophysiology twin models (Wang et al., 5 Mar 2024).
• Electromechanics: Coupled PDEs for myocardial strain $\epsilon(x,t)$ and stress $\sigma(x,t)$, using constitutive laws linked to calcium dynamics.
• Anatomical personalization: Segmentation of 3D cardiac structures (atria, ventricles, conduction system) from CT/MRI to generate subject-specific finite element meshes.

Patient-specific digital twins ingest individual imaging datasets (e.g., late gadolinium enhancement MRI for infarct zones), electrical mapping (ECG, intracardiac electrograms), and optionally genomic information (channelopathies, sarcomeric mutations).

2. Personalization and Model Calibration

Calibration of a cardiac digital twin requires assimilation of patient data at multiple scales:

• Dynamic measurements: Serial ECGs, implantable device logs, hemodynamic recordings.
• Imaging: Time-resolved MRI/CT volumes for cavity, wall, scar morphometry.
• Parameter inference: Bayesian updating or ensemble Kalman filtering is employed to estimate uncertain model parameters ($\theta$), such as tissue conductivity, local fibrosis, and ion channel kinetics, targeting agreement with observed trajectories.
• Prior distributions: Population-level studies inform truncated normal or log-normal priors for electrophysiological and anatomical parameters, with personal adjustment during calibration, directly analogous to oncology calibration workflows (Chaudhuri et al., 2023).

Posterior distribution of parameters provides explicit quantification of uncertainty, supporting robust predictive inference and risk-aware decisions (Pash et al., 13 May 2025).

3. Predictive Simulation and Clinical Decision Support

Cardiac digital twins enable simulation of physiological and pathological scenarios with direct application to clinical management:

• Arrhythmia risk stratification: Simulated pacing, repolarization, and conduction block under pharmacological or device interventions; identification of optimal ablation targets.
• Hemodynamic assessment: Predictions of cardiac output, pressure-volume relationships, and response to valve repair or replacement.
• Therapy optimization: Multi-objective risk-based optimization of device programming (ICD, CRT), drug dosing, or procedural planning, incorporating trade-offs between arrhythmia suppression, pump function, and adverse effect minimization.
• Uncertainty propagation: Computation of predictive intervals for primary endpoints, using superquantile (CVaR) or related risk metrics, aligns with frameworks in oncology digital twin literature (Chaudhuri et al., 2023).

4. Computational Infrastructure and Scalability

Clinical cardiac digital twins involve complex, high-dimensional PDEs requiring efficient solvers and UQ tools:

• FEM frameworks: Discretization of bidomain/electromechanical PDEs on patient-specific meshes, often leveraging established libraries (FEniCS, PETSc).
• HPC and GPU acceleration: Enables investigation of physiological scenarios in minutes-to-hours, supporting clinical workflows.
• Surrogate models: Reduced-order modeling and machine learning emulators can accelerate simulation and inference, as seen in real-time oncology twins (Kapteyn et al., 1 May 2025); physics-informed neural networks (PINNs) and transformer-based surrogates are emerging for cardiac waveform prediction.
• Modular software architecture: Separation of data ingestion (ECG, imaging), forward models, solvers, optimization, and calibration pipelines, facilitating extensibility (Kapteyn et al., 1 May 2025).

5. Current Clinical Integration and Research Landscape

Cardiac digital twins have demonstrated utility in several translational contexts:

• Device therapy: CRT/ICD programming optimization via digital twin-based simulation of pacing protocols and electrical activation sequences.
• Arrhythmia intervention: Patient-specific mapping to forecast reentrant circuit risk, guiding ablation strategies; in silico evaluation of drug or gene therapies.
• Heart failure management: Simulation-guided personalized fluid management, ventricular assist device titration, and risk-quantified predictions for adverse events.
• Regulatory engagement: Early dialogues are ongoing to establish validation standards, with cross-disciplinary transfer encouraged from successful workflows in oncology (Wang et al., 5 Mar 2024).

Notably, best practices emphasize:

• Tight data-model integration: Continuous assimilation of clinical and telemetry data.
• Rigorous UQ: Quantitative confidence intervals for all predictions, supporting risk-based decisions.
• Model explainability: Application of visual XAI tools and provenance chains, paralleling oncology digital twin advances (Wentzel et al., 18 Jul 2024).

6. Challenges, Limitations, and Future Directions

Key challenges for cardiac digital twin deployment include:

• Data scarcity: Limited high-resolution human activation maps and biophysical measurements restrict personalization.
• Model complexity: Full-scale cardiac bidomain/biomechanics simulations are costly; surrogates must respect physical constraints and multi-scale interactions.
• Practical identifiability: Overparameterized models risk ambiguity; iterative calibration and multi-cohort virtual patient strategies from oncology twin literature are recommended (Wang et al., 5 Mar 2024).
• Integration: Harmonization with EHRs, PACS, and device telemetrics for continuous operation.
• Regulatory compliance and ethical data governance: Ensuring transparent, verifiable VVUQ (Verification, Validation, and Uncertainty Quantification).

Future work will involve:

• Multi-modal data fusion (imaging, genomics, wearables, device telemetry) for state update and personalization.
• Adaptive optimization under uncertainty for therapy titration, leveraging multi-objective Pareto front visualization as in oncology (Chaudhuri et al., 2023, Pash et al., 13 May 2025).
• Systematic benchmarking of digital twin-driven decision support against standard-of-care, with real-world prospective clinical trials.
• Interdisciplinary transfer of methodologies (e.g., PINNs, physics-aware transformers, risk measures) from oncology and infectious disease to cardiac applications (Wang et al., 5 Mar 2024).

Cardiac digital twins represent the convergence of mechanistic simulation, real-time data assimilation, and rigorous personalized optimization, mirroring advances across biomedicine and driven by methodological developments shared with oncology digital twin research.