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

Jorge Corral-Acero 1 Francesca Margara 2 Maciej Marciniak 3 Cristobal Rodero 3 Filip Loncaric 4 Yingjing Feng 5 Andrew Gilbert 6 Joao Fernandes 3 Hassaan Bukhari 7, 8 Ali Wajdan 6 Manuel Villegas Martinez 6 Mariana Santos 9 Mehrdad Shamohammdi 10 Hongxing Luo 10 Philip Westphal 11 Paul Leeson 12 Paolo Diachille 13 Viatcheslav Gurev 13 Manuel Mayr 3 Liesbet Geris 14 Pras Pathmanathan 15 Tina Morrison 15 Richard Cornelussen 11 Frits Prinzen 16 Tammo Delhaas 16 Ada Doltra 17 Marta Sitges 17 Edward Vigmond 5, 8 Ernesto Zacur 18 Vicente Grau 1 Blanca Rodriguez 2 Espen Remme 19 Steven Niederer 3 Peter Mortier 9 Kristin Mcleod 20 Mark Potse 21, 22, 8 Esther Pueyo 7, 23 Alfonso Bueno-Orovio 2 Pablo Lamata 3
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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Submitted on : Monday, February 8, 2021 - 10:52:33 AM
Last modification on : Saturday, December 4, 2021 - 3:44:01 AM


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Jorge Corral-Acero, Francesca Margara, Maciej Marciniak, Cristobal Rodero, Filip Loncaric, et al.. The 'Digital Twin' to enable the vision of precision cardiology. European Heart Journal, Oxford University Press (OUP): Policy B, 2020, 41 (48), pp.4556-4564. ⟨10.1093/eurheartj/ehaa159⟩. ⟨hal-02947289v2⟩



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