| Literature DB >> 32128588 |
Jorge Corral-Acero1, Francesca Margara2, Maciej Marciniak3, Cristobal Rodero3, Filip Loncaric4, Yingjing Feng5,6, Andrew Gilbert7, Joao F Fernandes3, Hassaan A Bukhari6,8, Ali Wajdan9, Manuel Villegas Martinez9, Mariana Sousa Santos10, Mehrdad Shamohammdi11, Hongxing Luo11, Philip Westphal12, Paul Leeson13, Paolo DiAchille14, Viatcheslav Gurev14, Manuel Mayr15, Liesbet Geris16, Pras Pathmanathan17, Tina Morrison17, Richard Cornelussen12, Frits Prinzen11, Tammo Delhaas11, Ada Doltra4, Marta Sitges4,18, Edward J Vigmond5,6, Ernesto Zacur1, Vicente Grau1, Blanca Rodriguez2, Espen W Remme9, Steven Niederer3, Peter Mortier10, Kristin McLeod7, Mark Potse5,6,19, Esther Pueyo8,20, Alfonso Bueno-Orovio2, Pablo Lamata3.
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.Entities:
Keywords: Artificial intelligence; Computational modelling; Digital twin; Precision medicine
Mesh:
Year: 2020 PMID: 32128588 PMCID: PMC7774470 DOI: 10.1093/eurheartj/ehaa159
Source DB: PubMed Journal: Eur Heart J ISSN: 0195-668X Impact factor: 29.983