Summary of Leveraging Cardiovascular Simulations For In-vivo Prediction Of Cardiac Biomarkers, by Laura Manduchi et al.
Leveraging Cardiovascular Simulations for In-Vivo Prediction of Cardiac Biomarkers
by Laura Manduchi, Antoine Wehenkel, Jens Behrmann, Luca Pegolotti, Andy C. Miller, Ozan Sener, Marco Cuturi, Guillermo Sapiro, Jörn-Henrik Jacobsen
First submitted to arxiv on: 23 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Engineering, Finance, and Science (cs.CE); Biological Physics (physics.bio-ph)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel approach to solving the inverse problem of mapping observational data from cardiovascular systems back to plausible physiological parameters. The authors leverage recent advances in simulation-based inference and train an amortized neural posterior estimator on a large dataset of cardiac simulations, which is publicly released. To improve predictive capabilities, the framework incorporates stochastic elements modeling exogenous effects and can integrate in-vivo data sources. The proposed method enables finely quantifying uncertainty associated with individual measurements, allowing trustworthy prediction of four biomarkers (Heart Rate, Cardiac Output, Systemic Vascular Resistance, and Left Ventricular Ejection Time) from arterial pressure waveforms and photoplethysmograms. In silico experiments demonstrate the framework’s capabilities, while in vivo validation on the VitalDB dataset shows accurate capture of temporal trends in CO and SVR monitoring. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating a new tool that helps us understand how our hearts work by looking at blood pressure waves. Right now, it’s hard to figure out what these waves mean without knowing things like heart rate or blood flow. The authors created a special kind of computer program that can take these waveforms and use them to predict important details about our hearts, like how fast they’re beating or how well they’re working. This is useful because doctors need this information to make good decisions about patient care. The researchers tested their program on some real data and it worked pretty well! They also made the program’s mistakes get worse as the uncertainty got higher, which helps doctors know when the data isn’t reliable. |
Keywords
» Artificial intelligence » Inference
