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Review
. 2024 Dec 18;33(174):240159.
doi: 10.1183/16000617.0159-2024. Print 2024 Oct.

Digital twins for chronic lung diseases

Apolline Gonsard  1 , Martin Genet  2   3 , David Drummond  4   5
Affiliations
Review

Digital twins for chronic lung diseases

Apolline Gonsard et al. Eur Respir Rev. .

Abstract

Digital twins have recently emerged in healthcare. They combine advances in cyber-physical systems, modelling and computation techniques, and enable a bidirectional flow of information between the physical and virtual entities. In respiratory medicine, progress in connected devices and artificial intelligence make it technically possible to obtain digital twins that allow real-time visualisation of a patient's respiratory health. Advances in respiratory system modelling also enable the development of digital twins that could be used to predict the effectiveness of different therapeutic approaches for a patient. For researchers, digital twins could lead to a better understanding of the gene-environment-time interactions involved in the development of chronic respiratory diseases. For clinicians and patients, they could facilitate personalised and timely medicine, by enabling therapeutic adaptations specific to each patient and early detection of disease progression. The objective of this review is to allow the reader to explore the concept of digital twins, their feasibility in respiratory medicine, their potential benefits and the challenges to their implementation.

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Conflict of interest statement

Conflicts of interest: the authors have no relevant financial or non-financial interests to disclose.

Figures

FIGURE 1
FIGURE 1
Components of a patient digital twin system for respiratory diseases. Figure created with BioRender.com.
FIGURE 2
FIGURE 2
Comparison of monitoring and simulation digital twins for chronic lung diseases. Figure created with BioRender.com.
FIGURE 3
FIGURE 3
Multi-physics and multi-scale aspects of lung modelling for simulation digital twins. Figure created with BioRender.com.
FIGURE 4
FIGURE 4
Expected benefits and main challenges of patient digital twins in chronic lung diseases. Figure created with BioRender.com.
See this image and copyright information in PMC

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