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Cited by 384

The DOI matches its Crossref record; 59 of the 59 references it cites were found in a registry or an index.

The health digital twin to tackle cardiovascular disease—a review of an emerging interdisciplinary field

Open access
Coorey, Genevieve, Figtree, Gemma A., Fletcher, David F., Snelson, Victoria J., Vernon, Stephen Thomas, Winlaw, David, Grieve, Stuart M., McEwan, Alistair, Yang, Jean Yee Hwa, Qian, Pierre, O’Brien, Kieran, Orchard, Jessica, Kim, Jinman, Patel, Sanjay, Redfern, Julie·2022·npj Digital Medicine
DOI 10.1038/s41746-022-00640-7
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Abstract

Potential benefits of precision medicine in cardiovascular disease (CVD) include more accurate phenotyping of individual patients with the same condition or presentation, using multiple clinical, imaging, molecular and other variables to guide diagnosis and treatment. An approach to realising this potential is the digital twin concept, whereby a virtual representation of a patient is constructed and receives real-time updates of a range of data variables in order to predict disease and optimise treatment selection for the real-life patient. We explored the term digital twin, its defining concepts, the challenges as an emerging field, and potentially important applications in CVD. A mapping review was undertaken using a systematic search of peer-reviewed literature. Industry-based participants and patent applications were identified through web-based sources. Searches of Compendex, EMBASE, Medline, ProQuest and Scopus databases yielded 88 papers related to cardiovascular conditions (28%, n = 25), non-cardiovascular conditions (41%, n = 36), and general aspects of the health digital twin (31%, n = 27). Fifteen companies with a commercial interest in health digital twin or simulation modelling had products focused on CVD. The patent search identified 18 applications from 11 applicants, of which 73% were companies and 27% were universities. Three applicants had cardiac-related inventions. For CVD, digital twin research within industry and academia is recent, interdisciplinary, and established globally. Overall, the applications were numerical simulation models, although precursor models exist for the real-time cyber-physical system characteristic of a true digital twin. Implementation challenges include ethical constraints and clinical barriers to the adoption of decision tools derived from artificial intelligence systems.

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Metadata

Authors
Coorey, G., Figtree, G. A., Fletcher, D. F., Snelson, V. J., Vernon, S. T., Winlaw, D., Grieve, S. M., McEwan, A., Yang, J. Y. H., Qian, P., O’Brien, K., Orchard, J., Kim, J., Patel, S., & Redfern, J.
Venue
npj Digital Medicine
Publisher
Springer Science and Business Media LLC
Published
2022-08-26
Volume
5
Issue
1
Pages
126-126
ISSN
2398-6352 (ISSN-L)
DOI
10.1038/s41746-022-00640-7
OpenAlex
W4293109418
Cited by
384
References
59
FWCI
14.71
Type
journal article
Language
en
Open access
gold

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CoChat. (2026). Citation check: The health digital twin to tackle cardiovascular disease—a review of an emerging interdisciplinary field. https://app.cochat.ai/papers/10.1038/s41746-022-00640-7
The paper
Coorey, G., Figtree, G. A., Fletcher, D. F., Snelson, V. J., Vernon, S. T., Winlaw, D., Grieve, S. M., McEwan, A., Yang, J. Y. H., Qian, P., O’Brien, K., Orchard, J., Kim, J., Patel, S., & Redfern, J. (2022). The health digital twin to tackle cardiovascular disease—a review of an emerging interdisciplinary field. npj Digital Medicine. https://doi.org/10.1038/s41746-022-00640-7

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.