期刊
出版社
SAGE PUBLICATIONS LTD
DOI: 10.1177/0954411916683221
关键词
Computational modelling; respiratory system; clinical outcome prediction; mathematical modelling (medical); haemodynamics modelling
资金
- Royal Society of New Zealand Rutherford Discovery Fellowship [14-UOA-019]
- Aotearoa Foundation Postdoctoral Fellowship
- National Institute of Health (NIH) Bioengineering Research Partnership grant [R01HL119263]
- Human Lung Atlas-related projects [NIH R01-HL-094315, NIH R01 HL064368]
- Human Lung Atlas-related project (AirPROM consortium (EU)) [270194]
Respiratory disease is a significant problem worldwide, and it is a problem with increasing prevalence. Pathology in the upper airways and lung is very difficult to diagnose and treat, as response to disease is often heterogeneous across patients. Computational models have long been used to help understand respiratory function, and these models have evolved alongside increases in the resolution of medical imaging and increased capability of functional imaging, advances in biological knowledge, mathematical techniques and computational power. The benefits of increasingly complex and realistic geometric and biophysical models of the respiratory system are that they are able to capture heterogeneity in patient response to disease and predict emergent function across spatial scales from the delicate alveolar structures to the whole organ level. However, with increasing complexity, models become harder to solve and in some cases harder to validate, which can reduce their impact clinically. Here, we review the evolution of complexity in computational models of the respiratory system, including successes in translation of models into the clinical arena. We also highlight major challenges in modelling the respiratory system, while making use of the evolving functional data that are available for model parameterisation and testing.
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