Key result
Neural network Δ-FiberNet accurately maps RV fiber orientation and detects fibrosis from single activation maps.
Why the study?
Measuring right ventricular fiber orientations in clinical settings is challenging, resulting in a lack of patient-specific fiber information for personalized cardiac models.
A novel physics-informed neural network (Δ-FiberNet) demonstrates proof-of-concept feasibility for estimating patient-specific right ventricular fiber orientations and identifying fibrotic tissue using standard electroanatomical mapping data.
BACKGROUND AND OBJECTIVE: Accurate characterization of cardiac fiber orientation and detection of myocardial scars are crucial for developing personalized cardiac models that support precision diagnosis and treatment planning for heart diseases. However, measuring right ventricular (RV) fiber orientations remains challenging in clinical settings, resulting in a lack of patient-specific fiber information. In this study, we aim to assess the feasibility of reconstructing patient-specific RV fiber orientations and detecting fibrosis using available electroanatomical data by estimating tissue conductivity parameters. METHODS: We adopted an ensemble-based physics-informed neural network, Δ-FiberNet, to solve the inverse anisotropic Eikonal equation. The ensemble architecture enables the quantification of uncertainty in the estimated fiber field. We evaluated the model performance under both physiological and pathological conditions, including regions with slowed conduction or completely non-conductive tissue. The predicted conductivity parameters were then employed to identify fibrotic tissue. RESULTS: The results indicate that the model reliably learns from a single activation map, even in the presence of regions with abnormal conductivity. The robustness analysis results further confirmed the model's performance under various levels of measurement noise and sampling densities. CONCLUSIONS: This proof-of-concept study demonstrates Δ-FiberNet's ability to infer RV fiber orientations from a single activation map and provides initial evidence for its potential in identifying fibrotic tissue, which requires further validation.
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Chatraie et al. (2026) studied Right ventricular fibrosis and abnormal conductivity. Δ-FiberNet (physics-informed neural network) was evaluated on Reconstruction of patient-specific right ventricular fiber orientations and detection of fibrosis. The Δ-FiberNet physics-informed neural network reliably inferred right ventricular fiber orientations and identified fibrotic tissue from a single electroanatomical activation map.
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