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Comparison between Single-Phase Flow Simulation and Multiphase Flow Simulation of Patient-Specific Total Cavopulmonary Connection Structures Assisted by a Rotationally Symmetric Blood Pump

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An abdominal aortic aneurysm (AAA) is usually asymptomatic until rupture, which is associated with extremely high mortality. Consequently, the early detection of AAAs is of paramount importance in reducing mortality; however, most AAAs are detected by medical imaging only incidentally. The aim of this study was to investigate the feasibility of machine learning-based pulse wave (PW) analysis for the early detection of AAAs using a database of in silico PWs. PWs in the large systemic arteries were simulated using one-dimensional blood flow modelling. A database of in silico PWs representative of subjects (aged 55, 65 and 75 years) with different AAA sizes was created by varying the AAA-related parameters with major impacts on PWs—identified by parameter sensitivity analysis—in an existing database of in silico PWs representative of subjects without AAAs. Then, a machine learning architecture for AAA detection was trained and tested using the new in silico PW database. The parameter sensitivity analysis revealed that the AAA maximum diameter and stiffness of the large systemic arteries were the dominant AAA-related biophysical properties considerably influencing the PWs. However, AAA detection by PW indexes was compromised by other non-AAA related cardiovascular parameters. The proposed machine learning model produced a sensitivity of 86.8 % and a specificity of 86.3 % in early detection of AAA from the photoplethysmogram PW signal measured in the digital artery with added random noise. The number of false positive and negative results increased with increasing age and decreasing AAA size, respectively. These findings suggest that machine learning-based PW analysis is a promising approach for AAA screening using PW signals acquired by wearable devices.

An abdominal aortic aneurysm (AAA) is usually defined as the irreversible localized dilatation of the infrarenal abdominal aorta, which is usually asymptomatic until rupture [1]. The morbidity of AAA is significantly higher in men than in women (1.3% to 8.9% vs. 1.0% to 2.2%) and increases as a result of various factors such as tobacco smoking, ageing, and a family history of AAAs [1, 2, 3]. Given that the rupture of an AAA is often lethal with mortality reaching about 90% [4, 5], timely diagnosis and appropriate treatment are crucial for patients with an AAA. Large AAAs in underweight subjects can often be detected by physical examination, but accuracy depends on the examiner’s skills and is considerably reduced for obese body habitus and small AAA size [6]. In current clinical practice, AAAs are most often detected as incidental findings of ultrasonography, abdominal computed tomography, or magnetic resonance imaging performed for other purposes [6]. However, these medical imaging examinations require professional equipment that cannot be used in daily life.

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Considering that the presence of an AAA has a systemic impact on the biophysical properties of the cardiovascular system, thus influencing the arterial pulse wave (PW) [7, 8], analysis of PWs acquired by wearable devices may provide an alternative approach for the early detection of AAAs. These devices are more convenient and less expensive for large-scale screening than medical imaging exams. In particular, the photoplethysmogram (PPG) PW is easily acquired using pulse oximeters, which are frequently used in healthcare settings to measure arterial blood oxygen saturation and pulse rate. The PPG signal can also be acquired by devices available to the wider population, such as smartphones, smartwatches and fitness bands [9]. Therefore, if it was possible to detect an AAA from the PPG then it may have great clinical utility.

In recent years, machine learning-based PW analyses have been performed to investigate a wide range of clinical problems, showing promising results [10, 11, 12, 13]. Training machine learning models usually requires databases of PWs measured in a large number of subjects. Acquiring these data, however, presents several challenges: (i) the measurement accuracy is subject to the type of equipment used and may be operator-dependent; (ii) it is complex to measure PWs at all sites of interest; (iii) it can be difficult to measure reference variables precisely; (iv) it is challenging to study the influence of individual cardiovascular properties on the PW in vivo since other properties may change over time; and (v) data acquisition is expensive and time-consuming. Databases of simulated PWs representative of real subject samples provide an alternative approach that addresses all these challenges. A database of in silico PWs can be produced by using computational blood flow modelling [14, 15, 16, 17]. This is a cost-effective approach to generate a large number of virtual subjects, each with a distinctive set of PW signals, across a wide range of pathophysiological conditions for the training process of a machine learning model for PW analysis. Consequently, machine learning-based PW analysis using the database of in silico PWs could be employed for the early detection of AAAs.

Previous studies on AAAs have mainly studied AAA rupture by statistical analysis of AAA morphology [18, 19], biomechanical analysis using semi-empirical equations [20], and three-dimensional finite element analysis of AAA wall stress [21, 22]. Some computational studies investigated the initiation and growth of an AAA [23, 24, 25], and others focused on the prediction and planning of interventional procedures for AAAs such as endovascular deployment of stent-grafts [26, 27, 28]. Moreover, some studies have studied PW propagation in the presence of AAAs [29, 30, 31] and investigated the effects of AAAs on PW morphology by using computational blood flow modelling [32, 33, 34, 35, 36].

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The aim of this study was to create a new database of in silico PWs representative of subjects aged 55, 65 and 75 years old, with and without AAAs, and investigate the feasibility of machine learning-based PW analysis for the early detection of AAA using this database. Firstly, PWs in baseline subjects with and without AAAs were modelled using one-dimensional blood flow modelling, and a parameter sensitivity analysis was performed to evaluate the influence of AAA-related biophysical properties on the simulated PWs. Subsequently, the new database of in silico PWs was created by introducing the AAA-related parameters found to have a large impact on PWs (by the sensitivity analysis) into an existing in silico PW database representative of subjects without AAAs [14]. Finally, a machine learning architecture was proposed based on the recurrent neural network (RNN). This was trained and tested using the peripheral PPG PW derived from the in-silico PW database to evaluate the performance of machine learning-based PW analysis for the early detection of AAAs.

One-dimensional blood flow modelling in the lager systemic arteries (see Figure 1) was used to simulate several PW signals: blood pressure, blood flow velocity, blood flow rate and PPG. The baseline subject without an AAA was adapted from the baseline 65-year-old subject derived from the database of in silico PWs developed by Charlton [14], given that it is usually elderly people who suffer from AAA [2, 22]. Herein, two biophysical properties of the original model were adapted to make the subjects with and without AAA comparable: (i) the tapered infrarenal abdominal aorta was replaced by a straight tube with a diameter equal to the average value of the original proximal and distal diameters, and (ii) wall viscosity in this segment was removed to improve numerical stability. The baseline subject with an AAA was then obtained by modifying the baseline subject without AAA (Figure 1). First, the shape of the infrarenal abdominal aorta was transformed from a straight line (i.e., constant diameter) into a cosine curve with the same length and the maximum diameter being set at 30 mm. Second, the stiffness of this segment, which was quantified by the product of elastic modulus and wall thickness (Eh), was decoupled from the diameter and thereby maintained constant.

Based on the reference model of a subject aged 65 years old with an AAA introduced in Section 2.1, several AAA-related biophysical properties—the type of AAA shape, AAA maximum diameter, AAA length, AAA local stiffness and global stiffness of the larger systemic arteries—were varied individually to investigate their