Genetic-Driven Rapid and Precise Mimicry of Cardiovascular Fluctuations
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Background
Physics-based reduced-order models have gained significant attention for capturing cardiovascular fluctuations. However, achieving quick and precise mimicry of these fluctuations has been a persistent issue for decades.
Methods
Inspired by the principle of natural selection, we used a complex whole-body circulation model as an example and utilized genetic algorithms to automate the coordination of model parameters. Additionally, we introduced a “pseudo-distance” metric to evaluate the similarity between the simulated fluctuation curves and the target curves.
Results
Through rapid iterations (40 times), this strategy achieved a complete match with the target in both blood pressure and flow fluctuation amplitude and time domains, resulting in highly realistic fluctuation mimicry.
Conclusion
This study addresses the major challenge of reduced-order models in the mimicry of blood circulation, ending the history of manual parameter coordination that took months or even years.
Clinical Perspective
What Is New?
Physics-based reduced-order models are essential for analyzing whole-body hemodynamic status, but they have struggled with complex parameter coordination for decades. This study completely addressed this challenge by employing “genetic algorithms” and an updated “pseudo-distance” criterion, achieving precise mimicry of waveforms both spatially and temporally. Additionally, this work eliminates the dependency on large datasets, making personalized modeling more accessible and practical.
What Are the Clinical Implications?
This study lowers the barrier for researchers utilizing these models, significantly advancing the modeling of blood circulation and potentially benefitting physiological analysis, clinical diagnostics, and treatment planning in various cardiovascular events.
