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Uncertainty quantification in virtual surgery hemodynamics predictions for single ventricle palliation
D E Schiavazzi1, G Arbia2, C Baker3
1Mechanical and Aerospace Engineering Department, University of California at San Diego, San Diego, CA, U.S.A.
Summary
This study introduces a new method to quantify uncertainty in surgical simulations by propagating input data variability. This allows for confidence intervals on virtual surgery predictions, improving reliability for clinical decision-making.
Area of Science:
- Computational fluid dynamics
- Medical simulation
- Surgical planning
Background:
- Surgical simulation tools face challenges due to input data uncertainty.
- Deterministic simulations lack methods to estimate prediction confidence.
Purpose of the Study:
- To develop a methodology for full uncertainty propagation in surgical simulations.
- To quantify confidence in model predictions for virtual surgery.
Main Methods:
- Utilized probability density functions (PDFs) for clinical data.
- Employed Bayesian parameter estimation and Kriging for inverse problems.
- Applied sparse grid stochastic collocation for uncertainty propagation.
Main Results:
- Quantified statistical variability in virtual surgery predictions.
- Established confidence intervals for post-operative hemodynamic outcomes.
- Demonstrated methodology in a virtual single ventricle palliation surgery.
Conclusions:
- The proposed methodology enables confidence estimation in surgical simulations.
- Full uncertainty propagation enhances the reliability of virtual surgical outcomes.
- This approach supports more informed clinical decision-making in complex surgeries.
