Key result
The unscented Kalman filter framework incorporated optimization uncertainty in model predictions and was faster than the MCMC approach, although more sensitive to noise in flow measurements.
Why the study?
Patient outcome in TAVI therapy partly relies on haemodynamic properties that cannot be determined from current diagnostic methods alone.
A novel computational framework using UKF and polynomial chaos expansion offers a faster method than MCMC for predicting patient-specific haemodynamic outcomes with uncertainty after TAVI.
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May accelerate TAVI haemodynamic modeling; leaves open prospective clinical validation before practice adoption.
Meiburg et al. (2020) studied Aortic valve stenosis (n=3). Unscented Kalman filter (UKF) approach vs. Monte Carlo Markov Chain (MCMC) approach was evaluated on Optimization uncertainty in model predictions. The unscented Kalman filter framework incorporated optimization uncertainty in model predictions and was faster than the MCMC approach, although more sensitive to noise in flow measurements.
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