Machine Learning for Cardiovascular Biomechanics Modeling: Challenges and Beyond

Amirhossein Arzani, Jian Xun Wang, Michael S. Sacks, Shawn C. Shadden

Research output: Contribution to journalReview articlepeer-review

26 Scopus citations


Recent progress in machine learning (ML), together with advanced computational power, have provided new research opportunities in cardiovascular modeling. While classifying patient outcomes and medical image segmentation with ML have already shown significant promising results, ML for the prediction of biomechanics such as blood flow or tissue dynamics is in its infancy. This perspective article discusses some of the challenges in using ML for replacing well-established physics-based models in cardiovascular biomechanics. Specifically, we discuss the large landscape of input features in 3D patient-specific modeling as well as the high-dimensional output space of field variables that vary in space and time. We argue that the end purpose of such ML models needs to be clearly defined and the tradeoff between the loss in accuracy and the gained speedup carefully interpreted in the context of translational modeling. We also discuss several exciting venues where ML could be strategically used to augment traditional physics-based modeling in cardiovascular biomechanics. In these applications, ML is not replacing physics-based modeling, but providing opportunities to solve ill-defined problems, improve measurement data quality, enable a solution to computationally expensive problems, and interpret complex spatiotemporal data by extracting hidden patterns. In summary, we suggest a strategic integration of ML in cardiovascular biomechanics modeling where the ML model is not the end goal but rather a tool to facilitate enhanced modeling.

Original languageEnglish (US)
Pages (from-to)615-627
Number of pages13
JournalAnnals of Biomedical Engineering
Issue number6
StatePublished - Jun 2022
Externally publishedYes


  • Data-driven modeling
  • Deep learning
  • Hemodynamics
  • Physics-based modeling
  • Scientific machine learning

ASJC Scopus subject areas

  • Biomedical Engineering


Dive into the research topics of 'Machine Learning for Cardiovascular Biomechanics Modeling: Challenges and Beyond'. Together they form a unique fingerprint.

Cite this