The 'Digital Twin' to enable the vision of precision cardiology

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

doi:10.1093/eurheartj/ehaa159

The 'Digital Twin' to enable the vision of precision cardiology

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

Research output: Contribution to journal › Article › peer-review

164 Citations (Scopus)

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.

Original language	English
Pages (from-to)	4556-4564
Number of pages	9
Journal	European Heart Journal
Volume	41
Issue number	48
DOIs	https://doi.org/10.1093/eurheartj/ehaa159
Publication status	Published - 21 Dec 2020

Keywords

Artificial intelligence
Computational modelling
Digital twin
Precision medicine

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10.1093/eurheartj/ehaa159

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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, 41(48), 4556-4564. https://doi.org/10.1093/eurheartj/ehaa159

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title = "The 'Digital Twin' to enable the vision of precision cardiology",

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.",

keywords = "Artificial intelligence, Computational modelling, Digital twin, Precision medicine",

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

year = "2020",

month = dec,

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doi = "10.1093/eurheartj/ehaa159",

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Corral-Acero, J, Margara, F, Marciniak, M , Rodero, C, Loncaric, F, Feng, Y, Gilbert, A, Fernandes, JF, Bukhari, HA, Wajdan, A, Martinez, MV, Santos, MS, 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, EJ, Zacur, E, Grau, V, Rodriguez, B, Remme, EW, Niederer, S, Mortier, P, McLeod, K, Potse, M, Pueyo, E, Bueno-Orovio, A & Lamata, P 2020, 'The 'Digital Twin' to enable the vision of precision cardiology', European Heart Journal, vol. 41, no. 48, pp. 4556-4564. https://doi.org/10.1093/eurheartj/ehaa159

TY - JOUR

T1 - The 'Digital Twin' to enable the vision of precision cardiology

AU - Corral-Acero, Jorge

AU - Margara, Francesca

AU - Marciniak, Maciej

AU - Rodero, Cristobal

AU - Loncaric, Filip

AU - Feng, Yingjing

AU - Gilbert, Andrew

AU - Fernandes, Joao F.

AU - Bukhari, Hassaan A.

AU - Wajdan, Ali

AU - Martinez, Manuel Villegas

AU - Santos, Mariana Sousa

AU - Shamohammdi, Mehrdad

AU - Luo, Hongxing

AU - Westphal, Philip

AU - Leeson, Paul

AU - DiAchille, Paolo

AU - Gurev, Viatcheslav

AU - Mayr, Manuel

AU - Geris, Liesbet

AU - Pathmanathan, Pras

AU - Morrison, Tina

AU - Cornelussen, Richard

AU - Prinzen, Frits

AU - Delhaas, Tammo

AU - Doltra, Ada

AU - Sitges, Marta

AU - Vigmond, Edward J.

AU - Zacur, Ernesto

AU - Grau, Vicente

AU - Rodriguez, Blanca

AU - Remme, Espen W.

AU - Niederer, Steven

AU - Mortier, Peter

AU - McLeod, Kristin

AU - Potse, Mark

AU - Pueyo, Esther

AU - Bueno-Orovio, Alfonso

AU - Lamata, Pablo

PY - 2020/12/21

Y1 - 2020/12/21

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AB - 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.

KW - Artificial intelligence

KW - Computational modelling

KW - Digital twin

KW - Precision medicine

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DO - 10.1093/eurheartj/ehaa159

M3 - Article

C2 - 32128588

AN - SCOPUS:85099325483

SN - 0195-668X

VL - 41

SP - 4556

EP - 4564

JO - European Heart Journal

JF - European Heart Journal

IS - 48

ER -

The 'Digital Twin' to enable the vision of precision cardiology

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Keywords

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