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<p>Coronary artery disease in presence of lesions in the coronary arteries may restrict blood flow to the heart. Thus, a better understanding of the hemodynamics involved and development of rapid and accurate methodology will enable clinicians to assess patient-specific risk in order to administer timely interventions. In this study, it was proposed to develop and validate a new multifidelity machine learning framework to address limitations of the computational fluid dynamics approaches widely used to model hemodynamics in coronary and peripheral arteries. Proposed method significantly reduces the amount of experimental (including in-vivo) data required for training and will make the arterial pressure predictions less dependent on the availability of specific boundary conditions. Integration of various data sources allowed to introduce a set of physiology-related features (e.g., viscosity, blood flow, resistance, elasticity, etc.) and to achieve, in particular, an excellent agreement with mean values of in vitro pressure measurements: 1.2 ± 3.5 mmHg.</p>

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Deep operator learning for blood flow modelling in stenosed vessels

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月3日

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  • Alexis Velikorodny,
  • Lu Lu,
  • Vladimir Dudenkov,
  • Victor Glanz,
  • Boris Chernyavsky,
  • Antoinette Neylon,
  • Pieter C. Smits

摘要

Coronary artery disease in presence of lesions in the coronary arteries may restrict blood flow to the heart. Thus, a better understanding of the hemodynamics involved and development of rapid and accurate methodology will enable clinicians to assess patient-specific risk in order to administer timely interventions. In this study, it was proposed to develop and validate a new multifidelity machine learning framework to address limitations of the computational fluid dynamics approaches widely used to model hemodynamics in coronary and peripheral arteries. Proposed method significantly reduces the amount of experimental (including in-vivo) data required for training and will make the arterial pressure predictions less dependent on the availability of specific boundary conditions. Integration of various data sources allowed to introduce a set of physiology-related features (e.g., viscosity, blood flow, resistance, elasticity, etc.) and to achieve, in particular, an excellent agreement with mean values of in vitro pressure measurements: 1.2 ± 3.5 mmHg.