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The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.

The ‘Digital Twin’ to enable the vision of precision cardiology

Open access
Corral-Acero, Jorge, Margara, Francesca, Marciniak, Maciej, Rodero, Cristobal, Loncaric, Filip, Feng, Yingjing, Gilbert, Andrew, Fernandes, Joao F, Bukhari, Hassaan A, Wajdan, Ali, Martinez, Manuel Villegas, Santos, Mariana Sousa, Shamohammdi, Mehrdad, Luo, Hongxing, Westphal, Philip, Leeson, Paul, DiAchille, Paolo, Gurev, Viatcheslav, Mayr, Manuel, Geris, Liesbet, Pathmanathan, Pras, Morrison, Tina, Cornelussen, Richard, Prinzen, Frits, Delhaas, Tammo, Doltra, Ada, Sitges, Marta, Vigmond, Edward J, Zacur, Ernesto, Grau, Vicente, Rodriguez, Blanca, Remme, Espen W, Niederer, Steven, Mortier, Peter, McLeod, Kristin, Potse, Mark, Pueyo, Esther, Bueno-Orovio, Alfonso, Lamata, Pablo·2020·European Heart Journal
DOI 10.1093/eurheartj/ehaa159
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Abstract

Providing therapies tailored to each patient is the vision of precision medicine, enabled by the increasing ability to capture extensive data about individual patients. In this position paper, we argue that the second enabling pillar towards this vision is the increasing power of computers and algorithms to learn, reason, and build the ‘digital twin’ of a patient. Computational models are boosting the capacity to draw diagnosis and prognosis, and future treatments will be tailored not only to current health status and data, but also to an accurate projection of the pathways to restore health by model predictions. The early steps of the digital twin in the area of cardiovascular medicine are reviewed in this article, together with a discussion of the challenges and opportunities ahead. We emphasize the synergies between mechanistic and statistical models in accelerating cardiovascular research and enabling the vision of precision medicine.

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Metadata

Authors
Corral-Acero, J., Margara, F., Marciniak, M., Rodero, C., Loncaric, F., Feng, Y., Gilbert, A., Fernandes, J. F., Bukhari, H. A., Wajdan, A., Martinez, M. V., Santos, M. S., Shamohammdi, M., Luo, H., Westphal, P., Leeson, P., DiAchille, P., Gurev, V., Mayr, M., Geris, L., Pathmanathan, P., Morrison, T., Cornelussen, R., Prinzen, F., Delhaas, T., Doltra, A., Sitges, M., Vigmond, E. J., Zacur, E., Grau, V., Rodriguez, B., Remme, E. W., Niederer, S., Mortier, P., McLeod, K., Potse, M., Pueyo, E., Bueno-Orovio, A., & Lamata, P.
Venue
European Heart Journal
Publisher
Oxford University Press (OUP)
Published
2020-03-04
Volume
41
Issue
48
Pages
4556-4564
ISSN
  • 0195-668X (ISSN-L)
  • 1522-9645
DOI
10.1093/eurheartj/ehaa159
OpenAlex
W3010152544
Cited by
932
References
11
FWCI
21.09
Type
journal article
Language
en
Open access
hybrid

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CoChat. (2026). Citation check: The ‘Digital Twin’ to enable the vision of precision cardiology. https://app.cochat.ai/papers/10.1093/eurheartj/ehaa159
The paper
Corral-Acero, J., Margara, F., Marciniak, M., Rodero, C., Loncaric, F., Feng, Y., Gilbert, A., Fernandes, J. F., Bukhari, H. A., Wajdan, A., Martinez, M. V., Santos, M. S., Shamohammdi, M., Luo, H., Westphal, P., Leeson, P., DiAchille, P., Gurev, V., Mayr, M., . . . Lamata, P. (2020). The ‘Digital Twin’ to enable the vision of precision cardiology. European Heart Journal. https://doi.org/10.1093/eurheartj/ehaa159

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.