Prediction of 3D Cardiovascular hemodynamics before and after coronary artery bypass surgery via deep learning

Gaoyang Li, Haoran Wang, Mingzi Zhang, Simon Andre Tupin, Aike Qiao, Youjun Liu, Makoto Ohta, Hitomi Anzai

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)


The clinical treatment planning of coronary heart disease requires hemodynamic parameters to provide proper guidance. Computational fluid dynamics (CFD) is gradually used in the simulation of cardiovascular hemodynamics. However, for the patient-specific model, the complex operation and high computational cost of CFD hinder its clinical application. To deal with these problems, we develop cardiovascular hemodynamic point datasets and a dual sampling channel deep learning network, which can analyze and reproduce the relationship between the cardiovascular geometry and internal hemodynamics. The statistical analysis shows that the hemodynamic prediction results of deep learning are in agreement with the conventional CFD method, but the calculation time is reduced 600-fold. In terms of over 2 million nodes, prediction accuracy of around 90%, computational efficiency to predict cardiovascular hemodynamics within 1 second, and universality for evaluating complex arterial system, our deep learning method can meet the needs of most situations.

Original languageEnglish
Article number99
JournalCommunications Biology
Issue number1
Publication statusPublished - 2021 Dec
Externally publishedYes

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)


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