A data-driven model to study utero-ovarian blood flow physiology during pregnancy

Carson, Jason; Lewis, Michael; Rassi, D.; Loon, Raoul van

doi:10.1007/s10237-019-01135-3

Cited by 22 publications

(32 citation statements)

References 93 publications

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“…This type of adaptation on the above 1-D model has been validated against in vivo data in previous publications (Carson et al. 2019a , c ; Carson and Van Loon 2017 ).…”

Section: Methodssupporting

confidence: 55%

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

Chakshu

Sazonov

Nithiarasu

2020

Biomech Model Mechanobiol

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An exponential rise in patient data provides an excellent opportunity to improve the existing health care infrastructure. In the present work, a method to enable cardiovascular digital twin is proposed using inverse analysis. Conventionally, accurate analytical solutions for inverse analysis in linear problems have been proposed and used. However, these methods fail or are not efficient for nonlinear systems, such as blood flow in the cardiovascular system (systemic circulation) that involves high degree of nonlinearity. To address this, a methodology for inverse analysis using recurrent neural network for the cardiovascular system is proposed in this work, using a virtual patient database. Blood pressure waveforms in various vessels of the body are inversely calculated with the help of long short-term memory (LSTM) cells by inputting pressure waveforms from three non-invasively accessible blood vessels (carotid, femoral and brachial arteries). The inverse analysis system built this way is applied to the detection of abdominal aortic aneurysm (AAA) and its severity using neural networks.

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“…This type of adaptation on the above 1-D model has been validated against in vivo data in previous publications (Carson et al. 2019a , c ; Carson and Van Loon 2017 ).…”

Section: Methodssupporting

confidence: 55%

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

Chakshu

Sazonov

Nithiarasu

2020

Biomech Model Mechanobiol

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“…An initial simulation of a closed‐loop system is implemented with an adaptive parameter estimation technique to achieve realistic cardiac output during hyperaemia of 7.6 L/min for all patients. The resistance in the coronary network was initially distributed by assuming the total coronary blood flow to be approximately 4.5 % of the cardiac output.…”

Section: Methodsmentioning

confidence: 99%

Non‐invasive coronary CT angiography‐derived fractional flow reserve: A benchmark study comparing the diagnostic performance of four different computational methodologies

Carson

Pant

Roobottom

et al. 2019

Numer Methods Biomed Eng

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Non‐invasive coronary computed tomography (CT) angiography‐derived fractional flow reserve (cFFR) is an emergent approach to determine the functional relevance of obstructive coronary lesions. Its feasibility and diagnostic performance has been reported in several studies. It is unclear if differences in sensitivity and specificity between these studies are due to study design, population, or "computational methodology." We evaluate the diagnostic performance of four different computational workflows for the prediction of cFFR using a limited data set of 10 patients, three based on reduced‐order modelling and one based on a 3D rigid‐wall model. The results for three of these methodologies yield similar accuracy of 6.5% to 10.5% mean absolute difference between computed and measured FFR. The main aspects of modelling which affected cFFR estimation were choice of inlet and outlet boundary conditions and estimation of flow distribution in the coronary network. One of the reduced‐order models showed the lowest overall deviation from the clinical FFR measurements, indicating that reduced‐order models are capable of a similar level of accuracy to a 3D model. In addition, this reduced‐order model did not include a lumped pressure‐drop model for a stenosis, which implies that the additional effort of isolating a stenosis and inserting a pressure‐drop element in the spatial mesh may not be required for FFR estimation. The present benchmark study is the first of this kind, in which we attempt to homogenize the data required to compute FFR using mathematical models. The clinical data utilised in the cFFR workflows are made publicly available online.

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“…The 1D model of blood flow utilised in the present article is described in Carson and colleagues 15 , 28 and contains two tiers: (1) the closed-loop model that is described in Carson and colleagues 28 , 29 is used to generate the flow rate waveforms for the inlet of the left coronary and right coronary arteries; (2) an open-loop coronary network (the coronary network in Mynard and colleagues 24 , 30 ) is then used, where the vessel length and areas are adapted at random to produce similar vessel area variations to what is seen in real patient data, in addition a stenosis is added to one of the vessels in the network using a random blockage percentage, stenosis length, and stenosis location.…”

Section: Methodsmentioning

confidence: 99%

Artificial intelligence approaches to predict coronary stenosis severity using non-invasive fractional flow reserve

Carson

Chakshu

Sazonov

et al. 2020

Proc Inst Mech Eng H

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Fractional flow reserve is the current reference standard in the assessment of the functional impact of a stenosis in coronary heart disease. In this study, three models of artificial intelligence of varying degrees of complexity were compared to fractional flow reserve measurements. The three models are the multivariate polynomial regression, which is a statistical method used primarily for correlation; the feed-forward neural network; and the long short-term memory, which is a type of recurrent neural network that is suited to modelling sequences. The models were initially trained using a virtual patient database that was generated from a validated one-dimensional physics-based model. The feed-forward neural network performed the best for all test cases considered, which were a single vessel case from a virtual patient database, a multi-vessel network from a virtual patient database, and 25 clinically invasive fractional flow reserve measurements from real patients. The feed-forward neural network model achieved around 99% diagnostic accuracy in both tests involving virtual patients, and a respectable 72% diagnostic accuracy when compared to the invasive fractional flow reserve measurements. The multivariate polynomial regression model performed well in the single vessel case, but struggled on network cases as the variation of input features was much larger. The long short-term memory performed well for the single vessel cases, but tended to have a bias towards a positive fractional flow reserve prediction for the virtual multi-vessel case, and for the patient cases. Overall, the feed-forward neural network shows promise in successfully predicting fractional flow reserve in real patients, and could be a viable option if trained using a large enough data set of real patients.

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A data-driven model to study utero-ovarian blood flow physiology during pregnancy

Cited by 22 publications

References 93 publications

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

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

Non‐invasive coronary CT angiography‐derived fractional flow reserve: A benchmark study comparing the diagnostic performance of four different computational methodologies

Artificial intelligence approaches to predict coronary stenosis severity using non-invasive fractional flow reserve

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