Key result
Optimization-based uncertainty quantification accurately reproduces inputs and outputs in patient-specific cardiovascular models.
Why the study?
Estimating patient-specific computational model parameters relies on data that is often unreliable and ill-suited for deterministic approaches.
A novel optimization-based uncertainty quantification framework successfully determines parameter distributions for patient-specific cardiovascular models using uncertain data.
May aid uncertainty handling in cardiovascular models; hypothesis-generating and requires prospective clinical validation.
Estimating a patient-specific computational model's parameters relies on data that is often unreliable and ill-suited for a deterministic approach. We develop an optimization-based uncertainty quantification framework for probabilistic model tuning that discovers model inputs distributions that generate target output distributions. Probabilistic sampling is performed using a surrogate model for computational efficiency, and a general distribution parameterization is used to describe each input. The approach is tested on seven patient-specific modeling examples using CircAdapt, a cardiovascular circulatory model. Six examples are synthetic, aiming to match the output distributions generated using known reference input data distributions, while the seventh example uses real-world patient data for the output distributions. Our results demonstrate the accurate reproduction of the target output distributions, with a correct recreation of the reference inputs for the six synthetic examples. Our proposed approach is suitable for determining the parameter distributions of patient-specific models with uncertain data and can be used to gain insights into the sensitivity of the model parameters to the measured data.
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Mineroff et al. (2022) studied Patient-specific computational modeling. Optimization-based uncertainty quantification framework was evaluated on Reproduction of target output distributions and recreation of reference inputs. An optimization-based uncertainty quantification framework accurately reproduced target output distributions and correctly recreated reference inputs in patient-specific cardiovascular modeling.
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