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Biomechanics and Modeling in Mechanobiology

, Volume 18, Issue 6, pp 1987–2001 | Cite as

Using machine learning to characterize heart failure across the scales

  • M. Peirlinck
  • F. Sahli Costabal
  • K. L. Sack
  • J. S. Choy
  • G. S. Kassab
  • J. M. Guccione
  • M. De Beule
  • P. Segers
  • E. KuhlEmail author
Original Paper

Abstract

Heart failure is a progressive chronic condition in which the heart undergoes detrimental changes in structure and function across multiple scales in time and space. Multiscale models of cardiac growth can provide a patient-specific window into the progression of heart failure and guide personalized treatment planning. Yet, the predictive potential of cardiac growth models remains poorly understood. Here, we quantify predictive power of a stretch-driven growth model using a chronic porcine heart failure model, subject-specific multiscale simulation, and machine learning techniques. We combine hierarchical modeling, Bayesian inference, and Gaussian process regression to quantify the uncertainty of our experimental measurements during an 8-week long study of volume overload in six pigs. We then propagate the experimental uncertainties from the organ scale through our computational growth model and quantify the agreement between experimentally measured and computationally predicted alterations on the cellular scale. Our study suggests that stretch is the major stimulus for myocyte lengthening and demonstrates that a stretch-driven growth model alone can explain \(52.7\%\) of the observed changes in myocyte morphology. We anticipate that our approach will allow us to design, calibrate, and validate a new generation of multiscale cardiac growth models to explore the interplay of various subcellular-, cellular-, and organ-level contributors to heart failure. Using machine learning in heart failure research has the potential to combine information from different sources, subjects, and scales to provide a more holistic picture of the failing heart and point toward new treatment strategies.

Keywords

Machine learning Gaussian process regression Bayesian inference Uncertainty quantification Heart failure Growth and remodeling Multiscale 

Notes

Acknowledgements

This work was supported by the Flanders Innovation and Entrepreneurship Agency (VLAIO) strategic basic research Grant 141014 and a travel Grant by the Flemish Fund for Scientific Research (FWO) to Mathias Peirlinck, by the Becas Chile-Fulbright Fellowship to Francisco Sahli Costabal, and by the National Institutes of Health Grant U01 HL119578 to Julius M. Guccione, Ghassan S. Kassab, and Ellen Kuhl.

Compliance with ethical standards

Conflicts of interest

The authors declare that they have no conflict of interest

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Biofluid, Tissue and Solid Mechanics for Medical Applications (IBiTech, bioMMeda)Ghent UniversityGhentBelgium
  2. 2.Department of Mechanical EngineeringStanford UniversityStanfordUSA
  3. 3.Department of Human BiologyUniversity of Cape TownCape TownSouth Africa
  4. 4.California Medical Innovations Institute, Inc.San DiegoUSA
  5. 5.Department of SurgeryUniversity of California at San FranciscoSan FranciscoUSA
  6. 6.Departments of Mechanical Engineering and BioengineeringStanford UniversityStanfordUSA

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