The ‘Digital Twin’ to enable the vision of precision cardiology
By Jorge Corral-Acero, F. Margara, M. Marciniak, C. Rodero, F. Loncaric, Yingjing Feng, A. Gilbert, J. Fernandes, Hassaan A. Bukhari, A. Wajdan and 29 more. European Heart Journal, 2020
734 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Cardiology and Cardiovascular Medicine
View Paper: https://www.semanticscholar.org/paper/a9a7791fe867c4a6bdcbbcc07f20c501c68858c9
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Citations in the corpus
- Digital Twins: From Personalised Medicine to Precision Public Health (2021, background): “Another important area of research centres around establishing precision cardiology with the use of cardiac digital twins (CDT) [25].”
- Digital twin in healthcare: Recent updates and challenges (2023, methodology): “In realising clinical translation, model synergy can be used to increase clinical interpretability, verify the versatility of results, promote effective research results and testing methods and accelerate the integration of new technologies and clinical practice.(14)”
- The health digital twin to tackle cardiovascular disease—a review of an emerging interdisciplinary field (2022, methodology): “For example, within the digital twin construct and validation centred on a population-based databank, complementary deductive and inductive data modelling processes are at play 13 .”