Data-driven reduction of cardiac models
Mihai Itu L, Meister F, Sharma P, Passerini T (2020)
Publication Type: Book chapter / Article in edited volumes
Publication year: 2020
Edited Volumes: Artificial Intelligence for Computational Modeling of the Heart
Pages Range: 117-160
ISBN: 9780128175941
DOI: 10.1016/B978-0-12-817594-1.00015-2
Abstract
A common challenge in physiological modeling is the trade-off between the fidelity of the model and its complexity. Reliable models of cardiac physiology and pathology could have a tremendous value in supporting clinical decisions, by enabling advanced diagnosis, precise risk stratification and personalized therapy planning. However, for this technology to express its potential it is crucial that the data processing time is compatible with the fast-paced clinical workflow. In the ideal case, detailed and patient-specific physics phenomena should be reproduced and simulated with processing times enabling real-time interaction with the model solution. In many cases, real-time processing cannot be achieved if not at the expense of the descriptive power of the model. This chapter describes data-driven approaches for the reduction of physiological models, with the aim of retaining the ability of the model to describe complex physical phenomena, while at the same time drastically reducing the computational complexity.
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APA:
Mihai Itu, L., Meister, F., Sharma, P., & Passerini, T. (2020). Data-driven reduction of cardiac models. In Tommaso Mansi, Tiziano Passerini and Dorin Comaniciu (Eds.), Artificial Intelligence for Computational Modeling of the Heart. (pp. 117-160).
MLA:
Mihai Itu, Lucian, et al. "Data-driven reduction of cardiac models." Artificial Intelligence for Computational Modeling of the Heart. Ed. Tommaso Mansi, Tiziano Passerini and Dorin Comaniciu, 2020. 117-160.
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