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Edited by: Sergey Karabasov, Queen Mary University of London, United Kingdom

Reviewed by: David Jean Winkel, University Hospital of Basel, Switzerland; Adelaide De Vecchi, King's College London, United Kingdom

This article was submitted to Cardiovascular Imaging, a section of the journal Frontiers in Cardiovascular Medicine

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

The computational modelling of blood flow is known to provide vital hemodynamic parameters for diagnosis and treatment-support for patients with valvular heart disease. However, most diagnosis/treatment-support solutions based on flow modelling proposed utilize time- and resource-intensive computational fluid dynamics (CFD) and are therefore difficult to implement into clinical practice. In contrast, deep learning (DL) algorithms provide results quickly with little need for computational power. Thus, modelling blood flow with DL instead of CFD may substantially enhances the usability of flow modelling-based diagnosis/treatment support in clinical routine. In this study, we propose a DL-based approach to compute pressure and wall-shear-stress (WSS) in the aorta and aortic valve of patients with aortic stenosis (AS).

A total of 103 individual surface models of the aorta and aortic valve were constructed from computed tomography data of AS patients. Based on these surface models, a total of 267 patient-specific, steady-state CFD simulations of aortic flow under various flow rates were performed. Using this simulation data, an artificial neural network (ANN) was trained to compute spatially resolved pressure and WSS using a centerline-based representation. An unseen test subset of 23 cases was used to compare both methods.

ANN and CFD-based computations agreed well with a median relative difference between both methods of 6.0% for pressure and 4.9% for wall-shear-stress. Demonstrating the ability of DL to compute clinically relevant hemodynamic parameters for AS patients, this work presents a possible solution to facilitate the introduction of modelling-based treatment support into clinical practice.

In the medical health sector, the impact of artificial intelligence (AI)-based technologies is steadily increasing. While automated medical image analysis is arguably the most successful domain of medical AI applications (

On the other hand, hemodynamic modelling, i.e., the computational modelling of blood flow using computational fluid dynamics (CFD) simulations, is also gaining more and more attention as the availability of computational power steadily increases. Having the potential to improve, facilitate and complement current diagnostic and therapy decision-making processes (

However, translation of these models into clinical routine remains cumbersome. Not only are CFD simulations very demanding with respect to time and computational costs (several hours on high-end workstations), they also often require expertise in both engineering and medicine. As a result, diagnosis and/or treatment support solutions based on cardiovascular modelling rarely find their way into clinical practice (

In this regard, deep learning (DL)-based algorithms are potentially suited to overcome the problem of computational demand for higher order cardiovascular modelling. Once trained, a DL algorithm can provide results quickly with little need for computational power or user experience. Considering that clinicians rarely have access to high-end workstations on the one hand and need to perform diagnosis and treatment planning quickly on the other, the above mentioned advantages of a DL-based method may substantially increase the clinical feasibility of hemodynamic modelling. Several studies already showcased the potential of DL-based methods to accurately predict hemodynamic parameters. Examples of application include coronary arteries (

AS represents the leading valvular heart disease with surgical (aortic valve reconstruction or replacement) and interventional (transcatheter aortic valve implantation, TAVI) treatment options that are increasingly applied also in elderly patients. It is thus a pathology with a considerable socio-economic burden. Accurate diagnostic assessment including either echocardiographic or catheter-based pressure measurements is mandatory to quantify AS severity degree and to define treatment indication (

In this study, we use CFD generated hemodynamic data to develop an artificial neural network (ANN) which computes clinically relevant hemodynamic parameters for AS patients. Using high resolution computed tomography (CT) image data, a large set of patient-specific hemodynamic CFD simulations is built based on a workflow developed earlier (

Temporally resolved CT image data sets of 103 patients with AS who were treated in two different centers between February 2019 and October 2020 were retrospectively analyzed.

Inclusion criteria were the presence of aortic valve stenosis with indication for aortic valve replacement according to the European Society for Cardiology (ESC) guidelines and the interdisciplinary decision of the Heart Team and the availability of temporally resolved, high-resolution CT images for each patient. No further exclusion criteria were defined. The study was registered at

Computed tomography image data sets of the entire heart were acquired prior to the TAVI-procedure. The detailed protocol was previously published (

To allow the exact identification of the systolic phase with the widest aortic valve opening area, a multiphase data set, i.e., multiple scans at different time points, was reconstructed for each patient. All images were reconstructed with a soft-tissue convolution kernel and with the use of a dedicated noise reduction software. The spatial resolution used for segmentation was (0.39–0.648 mm) x (0.39–0.648 mm) in-plane resolution and (0.5–1 mm) slice thickness. The temporal resolution ranged from 70 ms to 140 ms.

Baseline echocardiographic evaluation results are additionally presented in ^{2}), moderately (up to 0.7 cm^{2}) and highly stenosed (<0.5 cm^{2}) cases.

Patient characteristics.

Age [years] | 82 ± 5 | 61–94 |

Height [cm] | 168 ± 10 | 145–195 |

Weight [kg] | 77 ± 19 | 35–135 |

TPG [mmHg] | 61.9 ± 22.0 | 20–118 |

AVA [cm^{2}] |
0.74 ± 0.17 | 0.4–1.1 |

Stroke volume [ml] | 52.2 ± 16.7 | 17–97 |

Ejection fraction [%] | 57.3 ± 8.9 | 25–73 |

Based on the acquired CT data, the patient-specific surfaces of the aorta and aortic valve were segmented semi-automatically using a shape-constrained deformable model that was described earlier (

Surface model of the aorta and aortic valve of a typical case with severe aortic stenosis. Additionally, labels for the various parts of the aorta are provided as well as a schematic depiction of the left ventricle attached to the left-ventricular outflow tract (LVOT).

Numerical flow simulations were performed using Siemens StarCCM+ v.15.04 (Siemens PLM, Plano, Texas). StarCCM+ uses the finite volume method to numerically solve the incompressible Navier–Stokes governing equations for the pressure and velocity fields. Given the high Reynolds number expected, a k-omega SST was used to account for turbulence. Blood was considered incompressible with a shear rate dependent viscosity based on the works of Abraham et al. (^{3}, zero/infinite-shear viscosity = 0.16/0.0035 Pa·s).

Discretization of the flow domain was performed using StarCCM+‘s built in polyhedral meshing algorithm with five boundary layers to improve near wall flow resolution. Mesh size and structure is based on experience from similar studies made previously (

An example of the computational mesh structure in the aortic valve and ascending aorta used for the computational fluid dynamics simulations. The boundary layer structure used to resolve near wall flow can be seen around the valve and vessel walls.

Owed the complex nature of three-dimensional viscous fluid flow on the one hand and the limited amount of clinical data on the other, some simplifications regarding the numerical modelling process were necessary however. Simulations were therefore performed with steady flow boundary conditions at peak systolic flow rates, i.e., the maximum flow rate achieved during the ejection phase of the cardiac cycle. Furthermore, vessel wall and valve leaflets were considered rigid. Thus, the simulations model peak-systolic, instantaneous hemodynamics only, where the AS induced transvalvular pressure gradient (TPG) is expected to have the highest relevance. A transient solver (second order backward, 0.001 s time step) was used however to capture small-scaled unsteady flow effects such as flow separation and the valve jet mixture layer. The time step size of 1 ms ensures that the CFL-number remains at the order of one for a sufficient numerical stability and temporal resolution of the aforementioned unsteady effects.

Peak systolic flow rates were derived from left-ventricular volumetry (LVV) (

Although the ability of the quasi-steady approach utilized here to accurately compute peak-systolic hemodynamics was investigated in earlier work (

Comparison between a simulation with unsteady flow boundary conditions (BC) and a steady flow BC at peak systolic flow rate for a patient with a severe aortic stenosis. Top diagram compares pressure, bottom diagram wall-shear-stress (WSS). The flow curve for the ejection phase as well as the peak-systolic flow rate were derived from left-ventricular volumetry data.

In addition to the baseline peak-systolic flow rates, simulations with varied flow rates were also performed to increase the amount of training data for the ANN. Flow rates were varied by ±25% of the respective base peak-systolic flow. This flow variation produces a desired substantial change in hemodynamics and corresponds with literature data on exercise-induced peak systolic flow change (

Although it is technically possible to use the three-dimensional data fields to train the ANN, a more compact representation of the simulation results becomes necessary considering the limited amount of training data available. This compact representation is constructed by locally averaging pressure and WSS along the aortic centerline using cross-section planes. This centerline-based representation substantially reduces input data dimensionality while retaining key spatial information. Moreover, this representation has shown to be more feasible for clinical use in our experience than three-dimensional data fields. Constructing the centerline-based representation of static pressure is performed in three steps:

Generation of a discrete (2 mm point spacing) aortic centerline from the case-specific surface model.

Creation of centerline-orthogonal vessel cross-sections at each centerline point.

Calculating the average of static pressure on each cross-section and assigning the resulting value to the respective centerline point.

In the valve region, only the area bound by the valve leaflets is used to calculate cross-section averaged pressure.

For WSS, the process is identical except that WSS is not averaged on the cross-sections planes but on vessel segments between two adjacent cross sections. For WSS in the valve region, only the inner leaflet surfaces are used to compute local WSS.

Deriving the compact representation (bottom plot) from the three-dimensional pressure and velocity field (top). Cross-section planes are defined along the vessel centerline. The average values of pressure on these planes are used to plot locally averaged pressure along the vessel centerline. Similarly, average values of WSS are computed from vessel sections between two adjacent cross-sections.

A recurrent neural network (RNN) is used to compute the hemodynamic parameters of pressure and WSS in the compact representation described above from a sequence of input features. These two hemodynamic parameters are chosen based on their clinical relevance. Static pressure derived TPG is the primary parameter for AS diagnostic and treatment (

A bi-directional long short-term memory (LSTM) RNN is used. This choice of architecture is well suited to work with the sequence-like data provided by the centerline-based representation and has proven successful in a similar study published earlier (

The LSTM uses a sequence of input features derived from the case-specific surface model and flow rate and outputs a sequence of state vectors. From these state vectors, a fully connected layer computes the values of pressure and WSS along the vessel centerline.

Centerline point coordinates (n × 3).

Cross-section area (n × 1).

Cross-section area normalized flow rate (n × 1).

m-dimensional encoded cross-section shape (n × m).

Schematic depiction of the artificial neural network (ANN) based workflow for computing pressure and wall-shear-stress (WSS) along the vessel centerline. Clinical data pre-processing includes the extraction of the vessel surface model as well as peak systolic flow rate from multiphasic computed tomography (CT) image data. Vessel centerline and cross-section shapes are then derived from the surface model and passed to the ANN directly (centerline-coordinates) and through an auto encoder, respectively. A long-short-term-memory (LSTM) recurrent neural network computes the flow states at each centerline point based on centerline coordinates, encoded cross-sections and flow rate. Lastly, a fully connected layer derives pressure and wall-shear-stress along vessel centerline from the flow states provided by the LSTM. Hidden state and input/output sizes are also provided in the schematic.

Where n denotes the number of centerline points for a given case (usually 110–130, depending on the length of the aorta).

The last feature is provided by an auto encoder (AE) which is trained separately. Using the AE, information on cross-section shape can be passed to the LSTM using only a few parameters of size m instead of complex polygons or triangulations. This information is deemed necessary since valve geometry varies substantially between individual cases and using cross-section area alone might not suffice to account for this geometrical variation.

The AE used here is part of Matlab’s Deep Learning toolbox and consists of a single-layer neural network (the encoder) that maps an input of size n to a hidden state of size m. From this hidden state, a second neural network (the decoder) reconstruct the original input data. The error between original and reconstructed input allows to define a cost function which is used to extract information critical to ANN accuracy from the original input (in this case cross-section shape), thereby allowing to substantially reduce input dimensionality without compromising accuracy. The size of the encoded state m (i.e., the amount of ‘essential’ information contained in the raw cross-section shapes) is determined during hyperparameter optimization. Further reference on the AE used in this study can be found in the respective Matlab documentation (

A total of 309 simulation results were available for training and validation. This dataset was reduced to 267 cases based on a maximum pressure drop (MPD), defined as the difference between inlet pressure and the lowest pressure found within the vessel, of 120 mmHg. This removed cases with an unphysiologically high pressure drop caused by either an excessive flow rate (+25% or modelled flow), an underestimation of valve area (i.e., segmentation errors) or a combination of both. The threshold of 120 mmHg is based on clinical guidelines, where TPG of 60 mmHg is already considered severe (

The remaining 267 cases were split into 11 datasets, one for testing and the other 10 for training and validation/optimization. Stratification was achieved by ensuring that each subset covers the whole range of allowable MPD (approx. 0–120 mmHg). Moreover, individual geometries were not distributed across subsets, meaning that a particular case will have all of its flow variations (baseline ±25%) within one subset. Thus, it is ensured that validation/test data remains fully unknown during training/testing. The test subset contained 23 cases, leaving 244 cases for training and hyperparameter optimization.

Network training was performed using adaptive moment estimation, a stochastic gradient descend algorithm that aims at minimizing the loss function (i.e., the difference between desired and actual network output). Hyperparameter optimization was performed using grid search. For each configuration, a 10-fold cross validation was performed. The configuration with the highest average accuracy was chosen as the final model. The following hyperparameters were considered (optimum values in brackets):

AE input size (68 × 68).

AE hidden size (4).

LSTM hidden size (900).

Fully connected layer size (200).

ANN robustness and explainability was analyzed using a local feature perturbation approach used earlier (

ANN accuracy was assessed by computing the root-mean-square-error (RMSE) between CFD- and ANN-based pressure/WSS on the test dataset. The pressure RMSE for a specific case is defined as:

Where

RMSEs for pressure and WSS were furthermore normalized to provide a relative measure of accuracy. Pressure RMSE was normalized with CFD-based MPD whereas WSS was normalized with the maximum WSS observed in the respective CFD result.

Additionally, a test for statistical equivalence is performed using a two one-sided tests (TOST) procedure. TOST based equivalence tests decide whether two different measuring methods applied on a set of subjects can be considered statistically equivalent within some bounds

Let

is hypothesized to be outside of a reference value

Hypotheses (5) and (6) are tested using a signed rank test, since the TPG values are not normally distributed in our data. If both (5) and (6) can be rejected at some significance level

For this study, an equivalence margin

Top left: Distribution of maximum pressure drop (MPD) within the entire simulation data. Top right: Distribution of MPD within the set of test cases. Bottom: An exemplary depiction of CFD-based pressure and wall-shear-stress (WSS) in the compact representation (i.e., average along centerline). Additionally, the measurement points for transvalvular pressure gradient (TPG, red) and MPD (green) are shown with their respective values.

A total of 309 simulations were performed using 103 patient-specific geometries (baseline flow ±25%). 42 simulations were excluded based on an MPD of 120 mmHg as mentioned previously, leaving 267 simulations results.

The characteristic rapid pressure drop and increase of WSS in the valve region can be seen as well as a slight pressure recovery in the ascending aorta along with a reduction of WSS. Moreover, a slight fluctuation of WSS with local maxima in the ascending aorta are observed, which is a result of secondary flow structures in that region generated by the valve jet.

The explainability and robustness analysis revealed that the network is most sensitive to perturbations in the valve region while changes to the input downstream of the aortic valve have little effect on output (RMSE <1 mmHg). Moreover, the input parameters of flow and encoded cross-section shape appear to have the most influence on the hemodynamic results provided by the ANN. Reducing cross-section area/shape and/or increasing flow-rate results in an increase of TPG and

Median of absolute pressure RMSE for all 23 test cases was 2.0 mmHg with an IQR of 3.2 mmHg. Absolute WSS RMSE was normally distributed (Lilliefors test at

Scatter plots of absolute and normalized root-mean-square-errors (RMSE), calculated between computational fluid dynamics (CFD) simulations and artificial neural network (ANN)-based results for the set of 23 test cases. Top shows absolute pressure and wall-shear-stress (WSS) RMSE over CFD computed maximum pressure drop and maximum WSS, respectively. Bottom shows pressure and WSS RMSE normalized with TPG and maximum WSS, respectively.

A more detailed presentation of the ANN’s performance is provided in

Comparison of ANN and CFD-based pressure and wall-shear-stress (WSS) along vessel centerline. Cases (

WSS appears to match similarly well between CFD and ANN, however, notable differences occur in the ascending aorta. In that region, the ANN either overestimates WSS (case C) or fails to capture local fluctuations (case B) or a combination of both (case A). WSS in the valve region on the other hand matches well between ANN and CFD.

Finally, the TOST for TPG measurement equivalency between CFD (median 32 mmHg, IQR 37 mmHg) and ANN (median 34 mmHg, IQR 33 mmHg) resulted in both methods being equivalent within the equivalency bounds of ±5 mmHg (

In this pilot study, we presented a deep ANN computing spatially resolved, compact hemodynamics in the aorta and aortic valve of AS patients as an alternative to traditional numerical modelling. The network was trained using 244 patient-specific CFD simulations of the aorta and aortic valve while 23 simulation were used for the evaluation of the ANN’s performance. Modelling hemodynamics using AI has been undertaken by several studies earlier (

The comparison between ANN- and CFD-based pressure computations provided in the results section showed good agreement between both methods with differences being at the order of a few mmHg. Considering that clinically relevant TPG values are at the order of 20 mmHg and upwards (

Assessing the accuracy of ANN-based WSS computation is more difficult, since no current clinical guidelines for WSS measurement or evaluation exist. Thus, the mean error of approx. 5 Pa is difficult to put into a meaningful clinical perspective. However, judging by the normalized accuracy, WSS and pressure computations are similarly accurate, although some test cases showed substantial errors in the ascending aorta. Another interesting finding is that ANN accuracy does not suffer from altering the flow rate given any particular geometry. This may provide an important capability for clinical use, as outlined further below.

All in all, an ANN-based modelling of aortic hemodynamics of AS patients appears to be a viable alternative to CFD. Considering the enormously lower computational time required (hours for CFD vs. seconds for ANN), ANN-based methods may facilitate the translation of hemodynamic modelling into clinical practice, something that CFD-based methods struggle with, despite their potential (

In the context of the use case of AS discussed here, such an ANN could be embedded into a CT or magnetic resonance imaging (MRI) scanner, expanding the diagnostic capability of the device to provide treatment-critical hemodynamic information (e.g., pressure, TPG) along with image data in real-time. This could in turn reduce the requirement for invasive diagnostics of TPG and thus reduce costs and patient risk. Furthermore, the change in TPG under increased cardiac output (i.e., flow) could be simulated for borderline-symptomatic patients, thus reducing or even replacing the current practice of measuring pressure under drug or exercise induced stress for such patients (

Although the results are promising, the work presented here does not fully provide the potential capabilities outlined above yet and several factors need to be considered for a successful clinical translation. First of all, the ANN and CFD-based results show notable differences for cases with high MPD/TPG. Especially WSS appears to diverge substantially between the two methods in the ascending aorta, as seen in

For both pressure and WSS, an increase in ANN pressure and WSS RMSE was observed with an increase of MPD and max WSS, respectively. This was more pronounced for WSS, where maximum WSS and WSS RMSE where notably correlated. This suggests that the overall error can be viewed as a combination of a systematic error which depends on some parameter/feature and a random error. The systematic error may be explained by the fact that with higher MPD/maximum WSS, the ANN struggles to capture the resulting high spatial variation of these parameters along the centerline since the distance between the centerline points is fixed to 2 mm. Moreover, the loss of spatial information from the cross-section averaging process has a greater impact on cases with a severe stenosis and thus high MPD/maximum WSS. For such cases, the pressure/WSS on a single cross section plane may vary by up to 20 mmHg/50 Pa, which appears mostly in the region where the valve jet impinges the vessel wall. The cases displayed in

In this context, the correlation between RMSE and MPD/max WSS may be seen as an indicator which of the two error sources, constant or random, dominates the overall error. Hence, the random error appears more influential for pressure RMSE than it is for WSS RMSE, where it supposedly accounts for half of the variance in the WSS RMSE (R^{2} of 0.5). This appears reasonable given the fact that the local variation of WSS across the vessel wall is higher than the local variation of pressure on the cross sections (with respect to the overall level of pressure/WSS). The random error, on the other hand, is likely the result of uncertainties in the input data, as will be discussed further below. All in all, the differences observed between CFD and ANN based solutions can be attributed to the following factors:

Uncertainty in the input data.

Random numerical errors in the training data (i.e., CFD).

Comparably low amount of training data/biased training data.

Input data pre-processing.

The first two factors mostly contribute to the random error described earlier. The uncertainty in the input data for instance, which results from inconsistencies in the definitions of the cross-sections/vessel segments in the valve region, may omit pressure/WSS data in some cases, leading to an erroneous input and thus result. The random numerical errors are the result of the discretization error inherent to the CFD method on the one hand and the fact that the pressure and velocity fields fluctuate slightly during runtime on the other. These are at the order of 1–2 mmHg based on the mesh independency study as well as the convergence behavior observed in our simulations.

The last two points are likely to influence both the systematic and the random error. More training data would provide a denser sampling of the parameter space (both geometric and hemodynamic parameters) and allow for more sophisticated inputs to be used, thus potentially reducing the random error. Moreover, additional cases in the high MPD range would remove the bias in the training data which could be another factor contributing to the systematic error besides the ones discussed earlier.

The pre-processing of input data plays a key role in how information is passed to the network. Currently, the raw simulation as well as the shape data are substantially reduced using the centerline-based representation. This was deemed necessary to efficiently utilize the clinical data available for this study. However, it creates the aforementioned problems of spatial discretization as well as the mismatch between average and actual pressure/WSS distributions. Furthermore, the auto encoder-based reduction of the cross section shape dimensionality is also likely to introduce errors into the input data and hence the results. Increasing input data dimensionality with a denser centerline sampling, a larger AE hidden-state size and more distribution parameters for pressure and WSS are likely to reduce both the systematic error as well as the random error through input data uncertainty. However, the possibility of providing additional input data is closely tied to clinical data availability.

Finally, the choice of ANN architecture itself is worth evaluating, although the question of optimal ANN architecture cannot be definitely answered within the scope of this study. While the choice of architecture used here appears reasonable given the underlying physical modelling problem, it is not necessarily the best one. Other ANN types such as convolutional neural networks, which are highly successful in working with image data, may produce even better results depending on available input data and required output. Evaluating different types of architectures should therefore be another key aspect of future studies.

An important concern regarding the translatability of the ANN into clinical practice is the underlying CFD method, on which the ANN is trained. Since the ANN can only be as good as the training data it uses (i.e., the CFD data), it must be ensured that the CFD-based hemodynamic modelling is accurate and validated against clinical data. Although CFD simulations of aortic flow in AS patients using the setup employed here showed good agreement with clinical data in earlier studies (

The steady-state, peak systolic computational model employed in this study is another limitation worth mentioning. This simplification was deemed necessary to find a good balance between modelling complexity and clinical relevance for this initial work. And while this approach may already provide clinically relevant data (

Finally, an important aspect necessary to realize an application of an ANN-based hemodynamic computation as outlined earlier in the section is the pre-processing of clinical input data. Although the ANN presented here partially uses image data for the input, this image data is not raw CT or MRI data and all input sequences are derived from a segmented surface of the aorta and valve. This surface is currently the result of a lengthy semi-manual segmentation of CT data, which negates the ANN’s primary benefits of low computational times and user interaction. Therefore, a fast segmentation algorithm capable of automatically extracting aortic centerline, aortic- and valve-surfaces from cardiac CT or MRI is necessary. The development of such algorithms is ongoing and several promising methods exist, however, automatic segmentation of cardiac image data remains a challenging task (

The ANN-based computation of pressure and WSS in patients with AS is a viable alternative to time- and resource-intensive CFD-based modelling. Requiring very little computational power or user interaction, ANN-based hemodynamic modelling can become a key part in the integration of hemodynamic modelling into clinical practice, thereby expanding diagnostic capabilities, reducing costs and improving outcome. However, in its current state, the ANN presented here needs further improvement to be suitable for a clinical application. Apart from improving the ANN’s accuracy as outlined in section 4.2, further work is required to validate ANN accuracy against clinical data as well as provide an automatic image segmentation tool in order to derive a hemodynamic outcome from raw image data in a clinical setting. Nevertheless, the suitability of ML-based methods to perform hemodynamic modelling has been demonstrated.

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found at:

The study was registered at

PY and BF: data curation. PY and LG: formal analysis. LG, TK, and MS: funding acquisition. PY, LG, and MS: investigation and writing-original draft. PY: methodology and visualization. MS and LG: supervision. All: writing-review and editing, conceptualization, and contributed to the article and approved the submitted version.

The work presented here is largely built on the “ArtiCardio” project, which was funded by the German Federal Ministry of Education and Research (BMBF), grant number 13GW0208B. MS is a participant in the BIH Charité Digital Clinician Scientist Program funded by the Charité – Universitätsmedizin Berlin, and Berlin Institute of Health at Charité (BIH). Additional funding was provided through professor Goubergrits by the Einstein Center Digital Future (ECDF). Finally, we would like to acknowledge financial support from the Open Access Publication Fund of Charité – Universitaetsmedizin Berlin and the German Research Foundation (DFG).

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.