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Updated: May 26, 2026
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Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations
Published on: January 8, 2013
Probabilistic Cardiac Digital Twins for Robust Patient-Specific Modeling
Dimitris G Giovanis1,2, Kelly Zhang3, Justin Tso3
1Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.
Biorxiv : the Preprint Server for Biology
|May 25, 2026
Summary
This study presents a new framework for uncertainty quantification in cardiac digital twins, improving personalized medicine. The method efficiently learns complex data relationships, enabling more reliable predictions for cardiac conditions.
Area of Science:
- Computational biology
- Biomedical engineering
- Statistical modeling
Background:
- Uncertainty quantification (UQ) is crucial for reliable cardiac digital twins (DTs) in personalized medicine.
- Traditional UQ methods struggle with high-dimensional, nonlinear cardiac models.
Purpose of the Study:
- To introduce a novel framework for learning joint probability densities in cardiac DTs.
- To enable efficient uncertainty propagation and quantification in patient-specific cardiac models.
Main Methods:
- A geometry-aware generative learning framework is employed.
- Identifies low-dimensional latent representations of cardiac data.
- Uses stochastic differential equations in latent space for efficient sampling and mapping to high-dimensional spaces.
Main Results:
- The framework learns joint probability densities over cardiac observables and parameters.
- Enables construction of predictive distributions through sampling and conditioning.
- Demonstrated effectiveness on a ventricular arrhythmia prediction benchmark with fewer model evaluations than conventional methods.
Conclusions:
- The proposed framework offers a powerful approach for UQ in cardiac DTs.
- Facilitates characterization of statistical dependencies and exploration of interdependencies among observables.
- Advances the development of reliable and personalized cardiac digital twins for clinical applications.
