In precision cardiology, virtual replicas (VRs) hold promise for predicting arrhythmias by leveraging patient-specific data and biophysics knowledge. A crucial first step is creating VRs of cardiac tissue based on retrospective patient data. However, VRs aim to replicate biopotential conduction directly, whereas only non-invasive methods are feasible for clinical use on real organs and tissues. This discrepancy challenges our understanding of VR applicability limits. This study aims to enhance the mathematical template of VR by developing an in vitro validation complement. We performed a frame-by-frame comparison of in vitro optical mapping of biopotential conduction with VR predictions. Patient-specific self-organized tissue samples from human induced pluripotent stem cell-derived cardiomyocytes (CMs) with diffuse fibrosis were utilized as VR prototypes. High-resolution optical mapping recordings (Δx = 117 ± 4 μm, Δt = 7.69 ms) and immunostaining were used to reproduce fibrotic samples of linear size 7.5 mm. We applied data-driven Bayesian optimization of the Cellular Potts model (CPM) to study wave propagation at the subcellular level. The modified CPM accurately reflected the “perinatal window” until the 20th day of differentiation, affecting CMs’ self-organization. The percolation threshold of virtual conductive pathways reached 0.26 (0.27 ± 0.03 of CMs in vitro), yielding a spatial correlation of amplitude maps with Pearson's coefficients of 0.83 ± 0.02. As a proof-of-concept, we demonstrated that CPM-enhanced VR could predict wavefront trajectories in optical mapping recordings, showing that approximating fibrosis distribution is crucial for improving VR prediction accuracy.

Atrial fibrillation (AF) management increasingly relies on patient-specific computational modeling to simulate arrhythmia mechanisms and optimize ablation strategies. However, predictive reliability is strongly influenced by accurate reconstruction of myocardial fiber architecture, which governs electrical anisotropy. Because patient-specific atrial fiber orientations cannot currently be acquired reliably in vivo using diffusion tensor magnetic resonance imaging (DT-MRI), alternative computational mapping strategies are required. This protocol presents a comprehensive, open-source pipeline for constructing patient-specific 3D models of the human left atrium with explicit transmural fiber reconstruction from high-resolution clinical computed tomography (CT) data. The methodology extends the Universal Atrial Coordinate (UAC) framework into the volumetric domain by solving the Laplace-Dirichlet problem to derive a continuous transmural coordinate. This enables transfer of atlas-based fiber orientations and subsequent interpolation across the atrial wall. Linear and threshold-based transmural interpolation strategies are quantitatively compared, and their effects on fiber orientation and macroscopic wavefront propagation are evaluated. Across all six models, the mean angular deviation between interpolation methods was 8.06 ± 1.13°. Electrophysiological simulations demonstrate that the choice of interpolation delayed activation in Region 1 by 2.0 ± 0.6 ms, corresponding to 6.1 ± 1.5 ms (p = 0.031 for both comparisons). The open-source pipeline and patient-specific CT-based atrial models provide a reproducible framework for evaluating the functional effects of transmural fiber reconstruction strategies.

Despite significant advances in diagnosis and treatment, atrial fibrillation remains a common cardiac arrhythmia affecting up to 2% of our world’s population. Atrial fibrillation results from complex dynamic interactions between risk factors and comorbidities that trigger a variety of atrial remodeling processes. Atrial remodeling increases the persistence of atrial fibrillation while contributing to disease progression. The variability of manifestations and the wide range of mechanisms involved in the initiation, maintenance, and progression of atrial fibrillation, as well as the associated adverse outcomes, make early identification of causative factors a major challenge for modern cardiology. Computer modeling over the past 60 years has opened new avenues for understanding mechanisms, risk prediction, and personalized therapy in the treatment of atrial fibrillation. As part of our modeling of patient-specific atrial structure from gadolinium-enhanced magnetic resonance imaging data, we investigated fibrosis as a substrate for the occurrence of re-entry from a purely electrophysiological perspective. We demonstrated the effects occurring on the patient-specific atrial structure in the case of normal electrophysiology and the electrophysiology obtained in long-term atrial fibrillation. In this work, we also compared the effects of the antiarrhythmic agent Verapamil on conduction by atrial models with different electrophysiology and fibrosis distribution.