Machine learning-enabled estimation of cardiac output from peripheral waveforms is independent of blood pressure measurement location in an in silico population

Machine learning-enabled estimation of cardiac output from peripheral waveforms is independent of blood pressure measurement location in an in silico population

Abstract Monitoring of cardiac output (CO) is a mainstay of hemodynamic management in the acutely or critically ill patient. Invasive determination of CO using thermodilution, albeit regarded as the gold standard, is cumbersome and bears risks inherent to catheterization. In the pursuit of noninvasi...

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Bibliographic Details
Main Authors: Lydia Aslanidou, Georgios Rovas, Ramin Mohammadi, Sokratis Anagnostopoulos, Cemre Çelikbudak Orhon, Nikolaos Stergiopulos
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Scientific Reports
Subjects:
Non-invasive monitoring
Neural networks
Cardiac output
Applanation tonometry
Machine learning
Pressure waveforms
Online Access:https://doi.org/10.1038/s41598-025-10492-2
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https://doi.org/10.1038/s41598-025-10492-2

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