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Leveraging Cardiovascular Simulations for In-Vivo Prediction of Cardiac Biomarkers

L Manduchi, A Wehenkel, J Behrmann… - arXiv preprint arXiv …, 2024 - arxiv.org
Astrophysics paper cs.LG Suggest

… Leveraging recent advances in simulation-based inference, we cast this problem as statistical inference by training an amortized neural posterior estimator on a newly built …

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@article{2412.17542v1,
Author        = {Laura Manduchi and Antoine Wehenkel and Jens Behrmann and Luca Pegolotti and Andy C. Miller and Ozan Sener and Marco Cuturi and Guillermo Sapiro and Jörn-Henrik Jacobsen},
Title         = {Leveraging Cardiovascular Simulations for In-Vivo Prediction of Cardiac
  Biomarkers},
Eprint        = {2412.17542v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {cs.LG},
Abstract      = {Whole-body hemodynamics simulators, which model blood flow and pressure
waveforms as functions of physiological parameters, are now essential tools for
studying cardiovascular systems. However, solving the corresponding inverse
problem of mapping observations (e.g., arterial pressure waveforms at specific
locations in the arterial network) back to plausible physiological parameters
remains challenging. Leveraging recent advances in simulation-based inference,
we cast this problem as statistical inference by training an amortized neural
posterior estimator on a newly built large dataset of cardiac simulations that
we publicly release. To better align simulated data with real-world
measurements, we incorporate stochastic elements modeling exogenous effects.
The proposed framework can further integrate in-vivo data sources to refine its
predictive capabilities on real-world data. In silico, we demonstrate that the
proposed framework enables finely quantifying uncertainty associated with
individual measurements, allowing trustworthy prediction of four biomarkers of
clinical interest--namely Heart Rate, Cardiac Output, Systemic Vascular
Resistance, and Left Ventricular Ejection Time--from arterial pressure
waveforms and photoplethysmograms. Furthermore, we validate the framework in
vivo, where our method accurately captures temporal trends in CO and SVR
monitoring on the VitalDB dataset. Finally, the predictive error made by the
model monotonically increases with the predicted uncertainty, thereby directly
supporting the automatic rejection of unusable measurements.},
Year          = {2024},
Month         = {Dec},
Url           = {http://arxiv.org/abs/2412.17542v1},
File          = {2412.17542v1.pdf}
}

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