Towards enabling a cardiovascular digital twin for human systemic circulation using inverse analysis

Chakshu, Neeraj Kavan; Sazonov, Igor; Nithiarasu, Perumal

doi:10.1007/s10237-020-01393-6

Cited by 74 publications

(63 citation statements)

References 48 publications

(71 reference statements)

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“…Moreover, our results support the use of 1-D modelling to create datasets of thousands of 'virtual' (computed) subjects with different sizes of large-artery stenoses and AAA for assessing the performance of such indices and algorithms, following our existing approach [73], which so far has been used to create healthy virtual subjects only. For instance, deeplearning algorithms for estimating the size of an AAA from a peripheral pressure wave can be developed using such datasets of virtual subjects [74].…”

Section: Significancementioning

confidence: 99%

Arterial pulse wave propagation across stenoses and aneurysms: assessment of one-dimensional simulations against three-dimensional simulations andin vitromeasurements

Jin

Alastruey

2021

J. R. Soc. Interface.

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One-dimensional (1-D) arterial blood flow modelling was tested in a series of idealized vascular geometries representing the abdominal aorta, common carotid and iliac arteries with different sizes of stenoses and/or aneurysms. Three-dimensional (3-D) modelling and in vitro measurements were used as ground truth to assess the accuracy of 1-D model pressure and flow waves. The 1-D and 3-D formulations shared identical boundary conditions and had equivalent vascular geometries and material properties. The parameters of an experimental set-up of the abdominal aorta for different aneurysm sizes were matched in corresponding 1-D models. Results show the ability of 1-D modelling to capture the main features of pressure and flow waves, pressure drop across the stenoses and energy dissipation across aneurysms observed in the 3-D and experimental models. Under physiological Reynolds numbers ( Re ), root mean square errors were smaller than 5.4% for pressure and 7.3% for the flow, for stenosis and aneurysm sizes of up to 85% and 400%, respectively. Relative errors increased with the increasing stenosis and aneurysm size, aneurysm length and Re , and decreasing stenosis length. All data generated in this study are freely available and provide a valuable resource for future research.

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Section: Significancementioning

confidence: 99%

Arterial pulse wave propagation across stenoses and aneurysms: assessment of one-dimensional simulations against three-dimensional simulations andin vitromeasurements

Jin

Alastruey

2021

J. R. Soc. Interface.

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“…Although little effort has been carried out to assess the CVD risk via machine learning methods, researchers have recently become engaged in the subject. For instance, a recent study has proposed a potential algorithm to estimate the size of an abdominal aortic aneurysm from pressure waves measured at carotid, brachial and femoral arteries using deep learning models [17]. In vascular ageing research, Tavallali et al used an artificial neural network to estimate cfPWV with an RMSE of 1.1244 m/s.…”

Section: Introductionmentioning

confidence: 99%

Estimating pulse wave velocity from the radial pressure wave using machine learning algorithms

2021

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One of the European gold standard measurement of vascular ageing, a risk factor for cardiovascular disease, is the carotid-femoral pulse wave velocity (cfPWV), which requires an experienced operator to measure pulse waves at two sites. In this work, two machine learning pipelines were proposed to estimate cfPWV from the peripheral pulse wave measured at a single site, the radial pressure wave measured by applanation tonometry. The study populations were the Twins UK cohort containing 3,082 subjects aged from 18 to 110 years, and a database containing 4,374 virtual subjects aged from 25 to 75 years. The first pipeline uses Gaussian process regression to estimate cfPWV from features extracted from the radial pressure wave using pulse wave analysis. The mean difference and upper and lower limits of agreement (LOA) of the estimation on the 924 hold-out test subjects from the Twins UK cohort were 0.2 m/s, and 3.75 m/s & -3.34 m/s, respectively. The second pipeline uses a recurrent neural network (RNN) to estimate cfPWV from the entire radial pressure wave. The mean difference and upper and lower LOA of the estimation on the 924 hold-out test subjects from the Twins UK cohort were 0.05 m/s, and 3.21 m/s & -3.11m/s, respectively. The percentage error of the RNN estimates on the virtual subjects increased by less than 2% when adding 20% of random noise to the pressure waveform. These results show the possibility of assessing the vascular ageing using a single peripheral pulse wave (e.g. the radial pressure wave), instead of cfPWV. The proposed code for the machine learning pipelines is available from the following online depository (https://github.com/WeiweiJin/Estimate-Cardiovascular-Risk-from-Pulse-Wave-Signal).

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“…In clinical practice, the concept of digital twins has also been applied to the fields of cardiology and endocrinology[ 11 - 13 ]. In cardiology, a few digital twin models have recently been developed to allow clinicians to provide precise care tailored to the patient by considering inter-individual variability and integrating the wide spectrum of biologic, environmental, and lifestyle data that influence cardiovascular outcomes.…”

Section: What Is a Digital Twin?mentioning

confidence: 99%

Predictive modeling in neurocritical care using causal artificial intelligence

Dang¹,

Lal²,

Flurin³

et al. 2021

WJCCM

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Artificial intelligence (AI) and digital twin models of various systems have long been used in industry to test products quickly and efficiently. Use of digital twins in clinical medicine caught attention with the development of Archimedes, an AI model of diabetes, in 2003. More recently, AI models have been applied to the fields of cardiology, endocrinology, and undergraduate medical education. The use of digital twins and AI thus far has focused mainly on chronic disease management, their application in the field of critical care medicine remains much less explored. In neurocritical care, current AI technology focuses on interpreting electroencephalography, monitoring intracranial pressure, and prognosticating outcomes. AI models have been developed to interpret electroencephalograms by helping to annotate the tracings, detecting seizures, and identifying brain activation in unresponsive patients. In this mini-review we describe the challenges and opportunities in building an actionable AI model pertinent to neurocritical care that can be used to educate the newer generation of clinicians and augment clinical decision making.

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Towards enabling a cardiovascular digital twin for human systemic circulation using inverse analysis

Cited by 74 publications

References 48 publications

Arterial pulse wave propagation across stenoses and aneurysms: assessment of one-dimensional simulations against three-dimensional simulations andin vitromeasurements

Arterial pulse wave propagation across stenoses and aneurysms: assessment of one-dimensional simulations against three-dimensional simulations andin vitromeasurements

Estimating pulse wave velocity from the radial pressure wave using machine learning algorithms

Predictive modeling in neurocritical care using causal artificial intelligence

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