# Rapid Thrombogenesis Prediction in Covid-19 Patients Using Machine Learning

Joong-Lyul Lee, Safaa Alwajidi, Mike Tree, Angelo Cristobal, Haitao Zhao

► To cite this version:

Joong-Lyul Lee, Safaa Alwajidi, Mike Tree, Angelo Cristobal, Haitao Zhao. Rapid Thrombogenesis Prediction in Covid-19 Patients Using Machine Learning. 19th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2023, León, Spain. pp.373-384, 10.1007/978-3-031-34107-6_30. hal-05498097

HAL Id: hal-05498097

https://inria.hal.science/hal-05498097v1

Submitted on 6 Feb 2026

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
<figure><img src="image_2.png" /></figure>

ifip

International Federation for Information Processing

This document is the original author manuscript of a paper submitted to an IFIP
conference proceedings or other IFIP publication by Springer Nature. As such, there
may be some differences in the official published version of the paper. Such
differences, if any, are usually due to reformatting during preparation for publication or
minor corrections made by the author(s) during final proofreading of the publication
manuscript.
# Rapid Thrombogenesis Prediction in Covid-19 Patients Using Machine Learning

Joong-Lyul Lee<fnref n="1" />(✉), Safaa Alwajidi<fnref n="2" />, Mike Tree<fnref n="3" />, Angelo Cristobal<fnref n="4" />,
and Haitao Zhao<fnref n="1" />

**Abstract.** Machine Learning (ML) algorithms are increasingly being utilized in the medical field to manage and diagnose diseases, leading to improved patient treatment and disease management. Several recent studies have found that Covid-19 patients have a higher incidence of blood clots, and understanding the pathological pathways that lead to blood clot formation (thrombogenesis) is critical. Current methods of reporting thrombogenesis-related fluid dynamic metrics for patient-specific anatomies are based on computational fluid dynamics (CFD) analysis, which can take weeks to months for a single patient. In this paper, we propose a ML-based method for rapid thrombogenesis prediction in the carotid artery of Covid-19 patients. Our proposed system aims to decrease the waiting time for clinicians to receive this information, leading to quicker treatment plans and improved patient outcomes. And we trained and tested several ML algorithms and found the optimal training data size while maintaining high accuracy. The dataset for this study was collected from previous studies and was pre-processed to ensure that the data was reliable and accurate. The proposed system's main contribution is the ability to quickly predict thrombogenesis in Covid-19 patients using ML models, which can help in preventive medicine by detecting serious diseases in advance.

**Keywords:** Thrombogenesis · Machine Learning · Covid-19 · Computational Fluid Dynamics

## 1 Introduction

Advances in medical technology and engineering have introduced the concept of patient-specific medical treatments, particularly in cardiovascular applications [1]. The importance of fluid mechanics within the cardiovascular system

© IFIP International Federation for Information Processing 2023

Published by Springer Nature Switzerland AG 2023

I. Maglogiannis et al. (Eds.): AIAI 2023, IFIP AICT 676, pp. 373–384, 2023.

https://doi.org/10.1007/978-3-031-34107-6_30

<footnote n="1">¹ The University of North Carolina at Pembroke, Pembroke, NC 28372, USA
{joonglyul.lee,haitao.zhao}@uncp.edu</footnote>

<footnote n="2">² The University of Baghdad, Baghdad, Iraq
safaa.alwajidi@sc.uobaghdad.edu.iq</footnote>

<footnote n="3">³ Corvid Technologies, Mooresville, NC 28117, USA
mike.tree@corvidtec.com</footnote>

<footnote n="4">⁴ Institute of Digital Engineering USA, Mooresville, NC 28117, USA
angelo.cristobal@ideusa.org</footnote>
has resulted in an entire industry surrounding its understanding and treatment. However, the time required to setup, execute, and analyze cardiovascular computational models precludes their general use [2]. High fidelity models of complex patient-specific anatomical systems require significant computational time and expertise which results in limiting the positive impact of these technological advances. Current methods for reporting thrombogenesis-related fluid dynamic metrics on patient-specific anatomies are based on computational fluid dynamics (CFD) analysis, and require weeks to months for a single patient. While the information is still valuable at this time interval, decreased wait time for the clinician to receive this information will naturally lead to quicker treatment plans and improved patient outcomes. Machine Learning (ML) may be able to reduce both the time and technical expertise required to model patient-specific cardiovascular flows enough to result in a bed-side clinical tool. To be clinically useful, a non-expert in fluid mechanics modeling must be able to acquire the additional information that patient-specific cardiovascular flow modeling can provide within a couple of hours. This can help clinicians predict disease progression, fill existing gaps in medical imaging, and aid clinical decision-making processes on a patient-by-patient basis [3]. In this paper, we investigate the use of ML approaches to predict blood clot formation (thrombogenesis). This problem was chosen due to many recent studies finding that Covid-19 patients experience a drastically increased incidence of blood clots [4–9]. Specifically, we investigate variations in the anatomy of the carotid artery aiming to derive patient-specific risk of developing potentially dangerous blood clots. The risk is measured through the prediction of carotid artery wall shear stress levels, which have been shown to be correlated with blood clot formation that may result in a stroke [10, 11].

**Contribution of this paper:** the main contributions can be summarized as follows:

1. We proposed a framework that utilizes machine learning models to enable rapid predictions of thrombogenesis. Our proposed framework represents a significant improvement over current methods for reporting thrombogenesis related computational fluid dynamics (CFD) analyses, which typically require weeks to months for a single patient. By using ML-based approach, we have dramatically reduced the time required to obtain results, enabling clinicians to receive predictions in a matter of minutes rather than weeks

2. Several ML algorithms were investigated to predict risk metrics for rapid thrombogenesis in the carotid artery and the optimal training data size was determined that would result in acceptable metric prediction in an effort to reduce future data synthesis requirements from expensive CFD simulation.

The rest of the paper is organized as follows: Sect. 2 discusses a literature review on the related work. Section 3 describes the ML models and the dataset used in these experiments. Section 4 discusses simulation results. Finally, we summarize our work and outline future work in Sect. 5.
## 2 Related Work

In the medical field, ML algorithms are used in various fields to manage and diagnose diseases, helping to improve patient treatment and disease management. A blood clot in the carotid artery can interrupt blood flow to the brain, leading to a stroke. In [12], the authors demonstrate the use of Support Vector Machine (SVM) and Decision Tree (DT) methods in identifying vulnerable plaque progression in mice. In [13], the authors researched the current state of the detection and management of atrial fibrillation using ML algorithms. Since a stroke is a medical emergency that can cause lasting brain damage, long-term disability, or even death, [14] investigated the mechanical retrieval of thrombotic material from acute ischemic stroke. In [15], the authors compared multiple ML algorithms and logistic regression models with multiple variable selection methods to predict radiological and clinical outcomes after Endovascular treatment (EVT) in stroke patients. This paper showed no difference in the performance results between the best-performing ML algorithms and the best-performing logistic regression models in predicting radiological or clinical outcome. Carotid artery stenting (CAS) can be used in a timely manner to help prevent such strokes, reducing the risk of stroke in [16,17]. It is essential to anticipate readmission within 30 days of the CAS procedure. By creating a model using ML algorithms to predict initial readmission, the authors in [18] can classify patients with a high readmission potential and focus on high-risk patients. Similar to this study, Feiger et al. used neural networks to predict pressure drop and time-averaged wall shear stress values in coarcted aorta geometries based of CFD simulations [1]. The authors also included an exercise based on design of experiments principles to evaluate the training data requirements for their neural network model to provide an arbitrary accuracy level.

## 3 Methods

### 3.1 Dataset and Data Preprocessing

The machine learning training data was composed of 384 transient CFD simulations consisting of 3 cardiac cycles worth of physical time. The 384 CFD simulations were created by varying fluid parameters for 3 different patient-specific geometries. Thus, the training data should be considered simulated, synthetic data based on real-world geometry. The varied parameters in the training dataset were related to specified flow through the geometry for each CFD simulation. The CFD simulation required specifying flow rate through the geometry as a function of time, static pressure at the geometry inlets and outlets as a function of time, and fluid viscosity. The flow rate was defined via a transient curve deconstructed using a fast Fourier transform (FFT) algorithm to include its most influential frequencies. Table 1 summarizes the parameters modified to create the 384 sets of CFD simulation training data.

The CFD simulations conducted consumed between 5,000 and 38,500 cpu-hours of resources and were post-processed to produce two key metrics related
<table><caption>**Table 1.** CFD Simulation Features</caption><thead><tr><th>Feature</th><th>Description</th></tr></thead><tbody><tr><td>nu.0</td><td>Maximum fluid viscosity (Pa·s)</td></tr><tr><td>Q_mean.cca</td><td>Mean volumetric inflow rate (into the common carotid artery, m³/sec)</td></tr><tr><td>T.cc</td><td>Cardiac cycle period length (sec)</td></tr><tr><td>area.cca</td><td>Cross sectional area (m²)</td></tr><tr><td>normal.cca</td><td>Unit vector normal to the inflow plane</td></tr><tr><td>fft_array_size</td><td>xft array size; used to reconstruct the inflow waveform</td></tr><tr><td>fft_data</td><td>List of 10 tuples describing the xft solution of the inflow waveform (the top 10 frequency contributions to the curve)</td></tr><tr><td>Rp.ica</td><td>Internal carotid artery proximal resistance</td></tr><tr><td>C.ica</td><td>Internal carotid artery capacitance</td></tr><tr><td>Rd.ica</td><td>Internal carotid artery distal resistance</td></tr><tr><td>Pi.ica</td><td>Internal carotid artery initial pressure (Pa)</td></tr><tr><td>Rp.eca</td><td>External carotid artery proximal resistance</td></tr><tr><td>C.eca</td><td>External carotid artery capacitance</td></tr><tr><td>Rd.eca</td><td>External carotid artery distal resistance</td></tr><tr><td>Pi.eca</td><td>External carotid artery initial pressure (Pa)</td></tr></tbody></table>

*Rp, C, and Rd values are used to compute pressure at each outlet (internal and external carotid arteries) given the inflow waveform using a 1D

<table><caption>**Table 2.** CFD Target Risk Metrics</caption><thead><tr><th>Feature</th><th>Description</th></tr></thead><tbody><tr><td>washout</td><td>Percent of Lagrangian particles that washout in one cardiac cycle when the velocity field is seeded with particles at a consistent location</td></tr><tr><td>mean.tawss</td><td>Spatial mean of the time-averaged wall shear stress</td></tr></tbody></table>

<figure><img src="image_3.png" /><figcaption>**Fig. 1.** CFD Particle Traces</figcaption></figure>

to blood clots: mean time-averaged wall shear stress and fluid washout. The mean time-averaged wall shear stress is a measure of the friction force existing between the fluid and the artery wall. The fluid washout metric was represented as a percentage of neutrally-buoyant Lagrangian particles present in the fluid domain after a set amount of time. Table 2 lists the blood clot risk metrics to be predicted using the machine learning algorithm.
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Time-Averaged Wall Shear Stress (TAWSS) contours on the patient-specific anatomy</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Correlation analysis in features</figcaption></figure>

Figure 1 shows CFD particle trace image and Fig. 2 shows Time-Averaged Wall Shear Stress (TAWSS) contours on the patient-specific anatomy. We show the correlation analysis to understand which features influence the data in the Fig. 3. From this result, we can see that there is a positive relationship between all variables except Rp_ica, which has a negative relationship with all other variables. Additionally, there is a strong correlation higher than 0.7 between almost all variables. Rp_eca and Rd_eca have a weak relationship of 0.3 or 0.4.
We re-processed the data values described in Table 1 in re-scaling the range of features to scale the range in [0, 1] by using the Min-Max Normalization method.

## 3.2 Machine Learning Algorithms

The ML algorithms used in our study were several regression models and Support Vector Machine (SVM), which has been demonstrated to achieve good generalization performance on clinical data. We implemented the following ML models with cross-validation using Sklearn libraries and Keras libraries with Tensorflow in Python.

- Linear Regression (LR) Model

- Random Sample Consensus (RANSAC) Model

- Ridge Regression Model

- Least Absolute Shrinkage and Selection Operator (LASSO) Regression Model

- Elastic Net Regression Model

- k-Nearest Neighbor (k-NN) Model

- Decision Tree (DT) Model

- Polynomial Regression Model

- eXtreme Gradient Boosting (XGBoost) Model

- Support Vector Machine (SVM) Model.

# 4 Results and Discussion

## 4.1 Simulation Results

In this simulation, we obtained the results for several ML models as shown in Table 3, and 4.

<table><caption>**Table 3.** Simulation results using different ML models</caption><thead><tr><td>Model</td><td>Avg MSE</td><td>Avg MAE</td><td>Avg R<sup>2</sup></td></tr></thead><tbody><tr><td>LR</td><td>0.00343</td><td>0.047</td><td>0.955</td></tr><tr><td>RANSAC</td><td>0.00343</td><td>0.047</td><td>0.955</td></tr><tr><td>Ridge</td><td>0.966</td><td>0.243</td><td>-0.285</td></tr><tr><td>Lasso</td><td>0.829</td><td>0.217</td><td>-0.103</td></tr><tr><td>ElasticNet</td><td>0.0766</td><td>0.211</td><td>-0.0187</td></tr><tr><td>k-NN</td><td>0.0814</td><td>0.223</td><td>-0.0841</td></tr><tr><td>DT</td><td>0.00385</td><td>0.0489</td><td>0.949</td></tr><tr><td>SVM</td><td>0.00364</td><td>0.0483</td><td>0.952</td></tr><tr><td>XGBoost</td><td>0.0957</td><td>0.0143</td><td>0.9852</td></tr></tbody></table>
<table><caption>**Table 4.** Polynomial Regression model results</caption><thead><tr><td>Model</td><td>Avg MSE</td><td>Avg MAE</td><td>Avg R<sup>2</sup></td></tr></thead><tbody><tr><td>Degree-1</td><td>0.00345</td><td>0.0471</td><td>0.955</td></tr><tr><td>Degree-2</td><td>0.00757</td><td>0.0728</td><td>0.899</td></tr><tr><td>Degree-3</td><td>4.04e + 24</td><td>1.33e + 12</td><td>-5.42e + 25</td></tr><tr><td>Degree-4</td><td>6.82e + 23</td><td>5.61e + 11</td><td>-9.07e + 24</td></tr><tr><td>Degree-5</td><td>0.0188</td><td>0.112</td><td>0.75</td></tr></tbody></table>

<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Simulation results of average accuracy ($R^2$), average MAE, and average MSE using different ML models</figcaption></figure>

The LR, RANSAC, DT, SVM, XGBoost, and Polynomial Regression models showed high accuracy levels of 0.955, 0.955, 0.949, 0.952, 0.9852, and 0.955, respectively, with XGBoost exhibiting the highest accuracy where accuracy is measured using the $R^2$ score. On the other hand, Ridge, Lasso, ElasticNet and k-NN models produced negative accuracy results. The $R^2$ score is a measure of how well a regression model fits the data. A high $R^2$ score indicates that the model explains a large proportion of the variance in the dependent variables, while a low $R^2$ score indicates that the model does not explain much of the variance in the dependent variables. The $R^2$ score ranges from 0 to 1, with 1 indicating a perfect fit between the predicted values and the actual values, and 0 indicating no relationship between the independent and dependent variables. Regarding the Polynomial Regression model, we observed the highest accuracy of 0.955 in the first degree, slightly lower in the second degree and fifth degree, and negative values in the third and fourth degree, as shown in Table 4.

Figure 4 shows the average mean absolute error (MAE), average mean squared error (MSE), and $R^2$ score in average accuracy. The graph indicates that high accuracy is associated with relatively low error rates, while low accuracy is associated with relatively high error rates. The average MSE results,
which are presented in Fig. 4, are consistent with the average MAE results in showing low error values relative to the high accuracy levels.

## 4.2 Dataset Quantity Study

Typically, more data is better. However, when the cost of generating the dataset is significant, determining the quantity of data needed for acceptable accuracy is desired. In order to obtain the optimal number of training dataset and testing dataset, we conducted experiments by dividing several data sets as shown in Table 5 to train XGBoost, DT, and LR models that had previously shown high accuracy results in simulations.

<table><caption>**Table 5.** Simulation results for optimal data quantity using ML models that showed high accuracy</caption><thead><tr><th>Num of Data</th><th>Model</th><th>Avg. R<sup>2</sup></th><th>Model</th><th>Avg. R<sup>2</sup></th><th>Model</th><th>Avg. R<sup>2</sup></th></tr></thead><tbody><tr><td>10</td><td>XGBoost</td><td>0.72</td><td>DT</td><td>0.54</td><td>LR</td><td>0.728</td></tr><tr><td>20</td><td>XGBoost</td><td>0.87</td><td>DT</td><td>0.873</td><td>LR</td><td>0.908</td></tr><tr><td>30</td><td>XGBoost</td><td>0.92</td><td>DT</td><td>0.829</td><td>LR</td><td>0.912</td></tr><tr><td>40</td><td>XGBoost</td><td>0.93</td><td>DT</td><td>0.881</td><td>LR</td><td>0.919</td></tr><tr><td>51</td><td>XGBoost</td><td>0.95</td><td>DT</td><td>0.875</td><td>LR</td><td>0.912</td></tr><tr><td>61</td><td>XGBoost</td><td>0.95</td><td>DT</td><td>0.872</td><td>LR</td><td>0.905</td></tr><tr><td>71</td><td>XGBoost</td><td>0.96</td><td>DT</td><td>0.873</td><td>LR</td><td>0.957</td></tr><tr><td>81</td><td>XGBoost</td><td>0.97</td><td>DT</td><td>0.864</td><td>LR</td><td>0.957</td></tr><tr><td>92</td><td>XGBoost</td><td>0.97</td><td>DT</td><td>0.847</td><td>LR</td><td>0.952</td></tr><tr><td>101</td><td>XGBoost</td><td>0.99</td><td>DT</td><td>0.954</td><td>LR</td><td>0.96</td></tr></tbody></table>

Figure 5 displays the average accuracy ($R^2$) of the XGBoost, LR, and DT models as the number of training data increase, as shown in Table 5. The graph shows that the $R^2$ score increases with an increase in the number of data.

We confirm that the XGBoost model performed best for this type of data in this simulation. Therefore, we select this type of model for additional study. In this additional experiment, we checked the $R^2$ score, MAE, and MSE values while increasing the data size. As shown in Fig. 6, 7 and 8, random repeated sampling from the data were taken for each quantity (total 126 data items) and statistics were derived to observe trends. Additionally, hyperparameters for all XGBoost models were optimized.

Based on this study, acceptable simulation results required at least 30 data items in training dataset. Beyond 30 data items, the accuracy shows improvement with less variance across the sampling permutations. However, the inflection point is at approximately 30 data items. This is the requisite quantity of data for modeling the three anatomies currently in our dataset. It is expected that
<figure><img src="image_7.png" /><figcaption>**Fig. 5.** Simulation results of average accuracy ($R^2$) to find an optimal number of training data size using ML models</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>**Fig. 6.** Simulation results for the XGBoost model, showing the MAE as a function of the training data quantity, in order to determine the optimal amount of training data</figcaption></figure>

more data would be required as additional geometries are inspected. In practice, optimized XGBoost models should be generated as training data results become available, where simulation features are determined using design of experiments methods such as the Latin Hypercube method or Adaptive Sampling. Once the model behavior shows diminishing returns, generation of new data can be stopped.
<figure><img src="image_9.png" /><figcaption>Fig. 7. Simulation results for the XGBoost model, showing the MSE as a function of the training data quantity, in order to determine the optimal amount of training data</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>Fig. 8. Simulation results of $R^2$ score for determining optimal training data quantity using XGBoost model</figcaption></figure>
# 5 Conclusions

Blood clotting is a common complication among Covid-19 patients, which requires accurate and timely diagnosis. While CFD simulation data has shown promise in predicting blood clots, it can take weeks to complete. To address this issue, we present a framework for predicting blood clots using 10 different ML models on patient MRI data. Our study reveals that XGBoost, LR, and DT models show a high accuracy rate of over 95% in predicting blood clots. Moreover, we conducted simulations to determine the minimum training dataset size necessary to maintain high accuracy, and the results show that the XGBoost model is the most accurate. Importantly, our proposed framework significantly reduces the time required to obtain results from weeks to minutes.

In future studies, we will implement a neural network and deep learning-based model to process more data and achieve better results. Overall, our framework has the potential to assist clinicians in making quick and accurate diagnoses of blood clotting in Covid-19 patients.

**Acknowledgements.** This project is supported by the North Carolina Collaboratory at the University of North Carolina at Chapel Hill with funding appropriated by the North Carolina General Assembly via the American Rescue Plan Act of 2021 (H.R. 1319) (federal award identification number SLFRP0129).

# References

1. Feiger, B., et al.: Accelerating massively parallel hemodynamic models of coarctation of the aorta using neural networks. Sci. Rep. **10**(1), 1–13 (2020)

2. Qin, S., Wu, B., Liu, J., Shiu, W.-S., Yan, Z., Chen, R., Cai, X.-C.: Efficient parallel simulation of hemodynamics in patient-specific abdominal aorta with aneurysm. Comput. Biol. Med. **136**, 104652 (2021)

3. Marsden, A.L., Esmaily-Moghadam, M.: Multiscale modeling of cardiovascular flows for clinical decision support. Appl. Mech. Rev. **67**(3), 030804 (2015)

4. Klok, F., et al.: Incidence of thrombotic complications in critically ill ICU patients with covid-19. Thrombosis Res. **191**, 145–147 (2020)

5. Poissy, J., et al.: Pulmonary embolism in patients with covid-19: awareness of an increased prevalence. Circulation **142**(2), 184–186 (2020)

6. Ramlall, V., et al.: Immune complement and coagulation dysfunction in adverse outcomes of sars-cov-2 infection. Nat. Med. **26**(10), 1609–1615 (2020)

7. Huertas, A., et al.: Endothelial cell dysfunction: a major player in sars-cov-2 infection (covid-19) (2020)

8. Varga, Z., et al.: Endothelial cell infection and endotheliitis in covid-19. Lancet **395**(10234), 1417–1418 (2020)

9. Magro, C., et al.: Complement associated microvascular injury and thrombosis in the pathogenesis of severe covid-19 infection: a report of five cases. Transl. Res. **220**, 1–13 (2020)

10. Wootton, D.M., Ku, D.N.: Fluid mechanics of vascular systems, diseases, and thrombosis. Ann. Rev. Biomed. Eng. **1**(1), 299–329 (1999)
11. Zarins, C.K., Giddens, D.P., Bharadvaj, B., Sottiurai, V.S., Mabon, R.F., Glagov, S.: Carotid bifurcation atherosclerosis. quantitative correlation of plaque localization with flow velocity profiles and wall shear stress. Circ. Res. **53**(4), 502–514 (1983)

12. Li, B., et al.: Contralateral artery enlargement predicts carotid plaque progression based on machine learning algorithm models in apoe-/– mice. Biomed. Eng. Online **15**(2), 233–246 (2016)

13. Wegner, F.K., et al.: Machine learning in the detection and management of atrial fibrillation. Clin. Res. Cardiol. **1–8** (2022). https://doi.org/10.1007/s00392-022-02012-3

14. Dargazanli, C., et al.: Machine learning analysis of the cerebrovascular thrombi pro- teome in human ischemic stroke: an exploratory study. Front. Neurol. **11**, 575376 (2020)

15. Van Os, H.J., et al.: Predicting outcome of endovascular treatment for acute ischemic stroke: potential value of machine learning algorithms. Front. Neurol. **9**, 784 (2018)

16. Flaherty, M.L., et al.: Carotid artery stenosis as a cause of stroke. Neuroepidemi- ology **40**(1), 36–41 (2013)

17. Bonati, L.H., Lyrer, P., Ederle, J., Featherstone, R., Brown, M.M.: Percutaneous transluminal balloon angioplasty and stenting for carotid artery stenosis. Cochrane Database Syst. Rev. **9** (2012)

18. Amritphale, A., et al.: Predictors of 30-day unplanned readmission after carotid artery stenting using artificial intelligence. Adv. Therapy **38**(6), 2954–2972 (2021)