The 'Digital Twin' to enable the vision of precision cardiology.
Artificial intelligence
Computational modelling
Digital twin
Precision medicine
Journal
European heart journal
ISSN: 1522-9645
Titre abrégé: Eur Heart J
Pays: England
ID NLM: 8006263
Informations de publication
Date de publication:
21 12 2020
21 12 2020
Historique:
received:
06
09
2019
revised:
29
11
2019
accepted:
24
02
2020
pubmed:
5
3
2020
medline:
15
5
2021
entrez:
5
3
2020
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 32128588
pii: 5775673
doi: 10.1093/eurheartj/ehaa159
pmc: PMC7774470
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
4556-4564Subventions
Organisme : Wellcome Trust
ID : 214290/Z/18/Z
Pays : United Kingdom
Organisme : National Centre for the Replacement, Refinement and Reduction of Animals in Research
ID : NC/P001076/1
Pays : United Kingdom
Organisme : British Heart Foundation
ID : FS/17/22/32644
Pays : United Kingdom
Organisme : British Heart Foundation
ID : CH/16/3/32406
Pays : United Kingdom
Organisme : British Heart Foundation
ID : RG/16/14/32397
Pays : United Kingdom
Organisme : Wellcome Trust
Pays : United Kingdom
Organisme : British Heart Foundation
ID : PG/16/75/32383
Pays : United Kingdom
Organisme : British Heart Foundation
ID : TG/17/3/33406
Pays : United Kingdom
Organisme : Wellcome Trust
ID : 209450/Z/17/Z
Pays : United Kingdom
Informations de copyright
© The Author(s) 2020. Published by Oxford University Press on behalf of the European Society of Cardiology.
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