  Over the past decades, hemodynamics simulators have steadily evolved and have
become tools of choice for studying cardiovascular systems in-silico. While
such tools are routinely used to simulate whole-body hemodynamics from
physiological parameters, solving the corresponding inverse problem of mapping
waveforms back to plausible physiological parameters remains both promising and
challenging. Motivated by advances in simulation-based inference (SBI), we cast
this inverse problem as statistical inference. In contrast to alternative
approaches, SBI provides \textit{posterior distributions} for the parameters of
interest, providing a \textit{multi-dimensional} representation of uncertainty
for \textit{individual} measurements. We showcase this ability by performing an
in-silico uncertainty analysis of five biomarkers of clinical interest
comparing several measurement modalities. Beyond the corroboration of known
facts, such as the feasibility of estimating heart rate, our study highlights
the potential of estimating new biomarkers from standard-of-care measurements.
SBI reveals practically relevant findings that cannot be captured by standard
sensitivity analyses, such as the existence of sub-populations for which
parameter estimation exhibits distinct uncertainty regimes. Finally, we study
the gap between in-vivo and in-silico with the MIMIC-III waveform database and
critically discuss how cardiovascular simulations can inform real-world data
analysis.
