Cardiac Digital Twins: Personalized Heart Modeling

• Cardiac digital twins are computational models that simulate individual heart anatomy, electrophysiology, and mechanics using clinical imaging and biophysical data.
• They integrate multi-modal data for forward simulation and inverse parameter estimation, aiding in personalized diagnosis and therapeutic decision-making.
• Advanced pipelines employ deep learning, robust segmentation, and uncertainty quantification to validate high-fidelity models for clinical translation.

Cardiac digital twins (CDTs) are computational representations that emulate the anatomical, electrophysiological, and mechanical properties of an individual's heart, with the goal of providing patient-specific simulation, diagnostic insight, and predictive therapeutic planning. CDTs integrate multi-modal data—clinical imaging, biophysical models, and physiological measurements—into a personalized platform that supports both forward simulation and inverse parameter estimation in silico. The construction, validation, and clinical translation of CDTs depend on advances in geometry extraction, tissue property estimation, electrophysiology models, robust fiber architecture prescription, and parameter inference methodologies. This article reviews the principal mathematical frameworks, data-driven modeling approaches, uncertainty quantification, and clinical applications central to cardiac digital twin research, with emphasis on rigorous pipeline components and their impact on real-world precision cardiology.

1. Anatomical Model Generation and Fiber Architecture Assignment

The anatomical component of a cardiac digital twin requires accurate reconstruction of heart geometry and regional fiber orientation, as conduction and mechanical behavior are highly anisotropic.

Volumetric Meshes and Universal Coordinates

Automated pipelines use clinical cardiac magnetic resonance (CMR) images (short-axis stacks and long-axis views) and deep-learning segmentation methods (e.g., nnU-Net) to segment cardiac chambers and assign anatomical landmarks (Ugurlu et al., 27 May 2025). Surface meshes are generated using atlas-based diffeomorphic registration, while volumetric finite-element meshes are created with tetrahedralization tools followed by rule-based or diffusion-based assignment of myocardial fiber directions (e.g., Bayer et al. 2012). Universal Ventricular Coordinates (UVCs) facilitate shape correspondence, regional mesh sampling, and fiber field parametrization (Santvliet et al., 8 Jan 2025). Algorithmic outputs include tens of thousands of subject-specific, QC-passed meshes with embedded fibers and coordinate fields.

High-Fidelity Fiber Models for Atrial Twins

Atrial digital twins (ADTs) require careful prescription of biatrial myocardial fibers due to complex atrial morphologies and anisotropic conduction. The Laplace-Dirichlet-Rule-Based Method (LDRBM) decomposes the biatrial volume into ≈27 anatomical bundles using scalar Laplace “distance” fields—each solved as

$$-\Delta \chi = 0 \quad \text{in} \quad \Omega_{\text{bia}}$$

with appropriate Dirichlet and Neumann boundary labeling (pulmonary veins, annuli, endocardium, epicardium). At each mesh node, bundle-selection rules and bundle-specific transmural rotations determine local coordinate axes and fiber orientation. Regional rotation angles are fit to histology or sub-millimeter DTMRI datasets, achieving local angular fidelity with <15° error in nearly half the myocardium (Piersanti et al., 15 Oct 2024). The open-source lifex-fiber module automates the process in under 10 minutes per case. Compared to atlas-based and legacy rule-based models, LDRBM delivers higher agreement with DTMRI ground truth and substantially reduced activation timing errors.

2. Functional Model Parameterization and Simulation

Accurate functional modeling depends on well-calibrated tissue property assignment, personalized electrophysiological parameter sets, and robust model inversion pipelines.

Elasticity and Strain Energy Function Discovery

Tissue mechanics within CDTs rely on hyperelastic strain energy functions (SEFs) that describe myocardial stress-strain relationships. Traditional SEFs are expert-designed with many parameters (Holzapfel–Ogden: 8 parameters), resulting in high parameter variance and limited identifiability. The Cardiac Hyperelastic Evolutionary Symbolic Regression Algorithm (CHESRA) applies evolutionary symbolic regression to derive highly compact, easily identifiable SEFs:

$$\psi_{\rm CH1} = (p_1 + \bar I_{1})\, (p_2 + p_3\, (\bar I_{8fs}+\bar I_{5f})) - p_1 p_2$$

$$\psi_{\rm CH2} = p_{1}(p_{2} + \bar I_{5f})(p_{3} + \bar I_{1})(p_{4} + \bar I_{5s}) - p_{1}p_{2}p_{3}p_{4}$$

where the $\bar I_k$ are normalized strain invariants. CH1/CH2 require only 3 or 4 parameters, fit cross-species datasets with sub-percent error, and retain sharp minimum in parameter landscapes. Parameter estimation via Levenberg–Marquardt achieves unique solution convergence and lower variance than legacy models, facilitating real-time or iterative digital twin calibration (Ohnemus et al., 13 Aug 2025).

Electrophysiology Models and Calibration

CDT electrophysiology models range from monodomain and bidomain PDEs for transmembrane potential propagation to anisotropic reaction–eikonal solvers for activation time and wavefront geometry. Specific pipelines embed physiologically informed Purkinje networks as root nodes, assign conduction velocities ($V_f$, $V_s$, $V_n$), and infer repolarization heterogeneities via APD90 maps. Sequential Monte Carlo approximate Bayesian computation (SMC-ABC) methods match simulated ECG waveforms to measured data, sampling plausible parameter ensembles instead of single optima, thus quantifying model uncertainty and enabling forward evaluation of drug effects and device interventions (Camps et al., 18 Jan 2024).

3. Inverse Problem Solving and Parameter Inference

Solving inverse problems (e.g., ECGI) is pivotal for personalizing electrophysiology and local property assignment in CDTs.

Deterministic and Bayesian Inverse Solvers

Linear forward problems (lead-field: $b = Ax + \varepsilon$) are solved via regularized least squares, penalizing non-smooth or non-physical solutions:

$$x^* = \arg\min_x \|A x - b\|_2^2 + \lambda \|L x\|_2^2$$

where $L$ may encode spatial, temporal, or physiologic regularity (Li et al., 17 Jun 2024). Deep learning solvers employ U-Nets, graph convnets, or transformer architectures to map multi-lead ECG signals to activation maps; hybrid models unroll proximal iterations or incorporate physics residuals via PINN approaches.

Identifiability and Uncertainty Quantification

Recent results demonstrate that multiple distinct activation patterns (LAT maps) can reproduce identical surface ECGs—non-uniqueness provable even under subendocardial constraint of Purkinje-muscle junctions. Ensemble optimization (e.g., Geodesic-BP) samples dozens of feasible initiation site configurations $X_0 = \{(x_i, t_i)\}$ with mean $\sigma_\tau$ of LAT ≤12 ms and ECG fit RMSE < 0.03 mV. Probabilistic frameworks produce $\bar{\tau}(x)$, $\sigma_\tau(x)$ maps that encode uncertainty and support robust, risk-adjusted therapy planning (Grandits et al., 31 Oct 2024).

4. Variability, Calibration Robustness, and Model Validation

Architectural and parameter variability in CDTs is analyzed via multi-source uncertainty modeling and rigorous calibration benchmarks.

Quantitative Variability Analysis

Monodomain/reaction–eikonal frameworks, combined with lead-field ECG mapping, facilitate controlled assessment of anatomical shifts (translation/rotation/scaling), tissue conductivity variance, and electrode misplacement. Simulated perturbations (±3 cm translation, ±10° rotation, ±10 % scaling, ±20–30 % conductivity change) induce relative ECG feature changes that remain within observed beat-to-beat physiologic ranges of healthy and arrhythmic patients (QRS/T duration CV < 1–5 %, amplitude shifts typically < 0.3 mV) (Zappon et al., 24 Jul 2024). Blood pool conductivity exerts the largest effect, while precordial lead placement is most sensitive to morphological perturbation, but key diagnostic indices (QRS width, T-wave polarity) remain robust.

Validation Strategies

CDT mesh generation and segmentation methods (nnU-Net, diffeomorphic registration) report Dice coefficients of 0.97/0.93 for LV cavity, 0.93/0.88 for RV cavity segmentation. Fiber prescription accuracy via LDRBM achieves 43–48 % of atrial nodes with <15° angular error versus DTMRI ground truth. Pressure–volume curve matching and multi-scenario inverse benchmarks confirm model fidelity in mechanics and electrophysiology (Ugurlu et al., 27 May 2025, Piersanti et al., 15 Oct 2024, Ohnemus et al., 13 Aug 2025).

5. Clinical Applications and Translational Impact

Cardiac digital twins drive advancements across personalized diagnosis, risk stratification, therapy evaluation, and interventional planning.

Personalized Electrophysiology and Arrhythmia Risk

High-fidelity bundle-architecture, uncertainty-quantified LAT simulations, and robust parameter inference enable mechanistic prediction of arrhythmia triggers, reentrant circuits, and ablation outcomes. Digital twins inform precision mapping of AF drivers, VT isthmus, CRT lead positioning, and virtual therapy response evaluation (Piersanti et al., 15 Oct 2024, Li et al., 17 Jun 2024).

Virtual Therapy and Device Planning

Full pipelines integrating CMR geometry, fiber orientation, and personalized conduction/repolarization parameters yield hundreds of plausible model instances per patient. Forward monodomain simulations reproduce clinical ECG features (Pearson r≈0.93), enable drug response prediction (e.g., Dofetilide QTc prolongation), and support virtual clinical trial design (Camps et al., 18 Jan 2024).

Population-Based Cohorts and Large-Scale Modeling

Open-source mass-scale pipelines process > 50,000 CMR datasets, generating QC’d biventricular meshes and representative atlas resources for cardiac research stratified by sex, age, and BMI (Ugurlu et al., 27 May 2025). Synthetic geometry cohorts and weakly supervised 4D mesh reconstructions permit rapid functional twinning and serve downstream simulation and biomarker extraction (Liu et al., 21 Jul 2025).

6. Limitations, Challenges, and Future Directions

Key limitations of current CDT frameworks include computational expense of full 3D electromechanical models, segmentation difficulties in poor-quality images, lack of prospective clinical validation, and limited disease-state generalization.

Proposed extensions focus on:

• Multi-scale and multi-organ modeling (cellular to circulatory)
• Physics-constrained surrogate emulators for real-time operation
• Integrating wearable or implant data streams for continuous twin updating
• Regulatory pathways for explainable AI integration and device software safety
• Standardized open data formats and model exchange for multicenter studies

The convergence of robust biophysical modeling, validated high-fidelity pipelines, and uncertainty-quantified parameter inference positions cardiac digital twins as a central tool for predictive, mechanism-driven personalized cardiology. Expansion into comprehensive population cohorts and automated large-scale processing supports both clinical and translational research at unprecedented resolution and throughput.