This study presents a comprehensive methodology for generating virtual human cardiovascular datasets for in-silico clinical trials, particularly for the evaluation of novel stents within the InSilc project. Our approach integrates a statistical modeling technique with a machine learning (ML)-based generative model to improve the accuracy and realism of virtual populations. The statistical model is based on a multivariate normal distribution and incorporates methods to address missing data and non-positive definite covariance matrices. The ML-based approach leverages Conditional Tabular Generative Adversarial Networks (CTGAN) to synthesize patient populations while maintaining the statistical integrity of real-world datasets. Initially, we simulated 20% of the test data for validation purposes. Following successful validation, we expanded the simulation to generate a virtual population of 10,000 patients. A comparative analysis revealed that the statistical model demonstrated higher accuracy in anatomical parameter prediction, whereas the ML approach excelled in capturing complex inter-variable relationships. The combination of these techniques enhances the ability to simulate diverse patient populations, thereby improving the robustness of in-silico clinical trials.Clinical Relevance — This methodology advances in-silico clinical trials by reducing reliance on traditional resource-intensive methods, improving trial efficiency, cost-effectiveness, and patient safety

Cardiovascular diseases remain a leading global health burden, with carotid artery stenosis progression being a critical determinant of adverse cerebrovascular outcomes. This study aims to enhance the predictive accuracy of carotid stenosis progression by leveraging advanced machine learning algorithms, thereby advancing precision medicine in vascular care. A comprehensive evaluation of six classification models was conducted, including Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Random Forest, Gradient Boosting, Logistic Regression, and Decision Tree. Each model was trained on a curated set of hemodynamic, imaging, and clinical features, and assessed using a 70%-30% stratified train-validation split. The study emphasizes the integration of high-fidelity simulation outputs and ultrasound-derived features in constructing predictive models. Among the classifiers tested, XGBoost demonstrated the highest performance, achieving an AUC of 0.741, accuracy of 70.5%, precision of 70%, and specificity of 88.9% on the validation dataset. Additionally, a 5-fold cross-validation strategy was performed to assess generalizability, with XGBoost achieving a mean AUC of 0.732 and improved overall robustness across folds. These metrics underline its superior capacity to distinguish between stable and progressive stenosis cases. SVM and Gradient Boosting also yielded competitive results, while simpler models lagged in performance. The findings underscore the value of machine learning—particularly ensemble-based approaches such as XGBoost—in predicting stenosis progression. By incorporating rich hemodynamic data and patient-specific imaging features, the model offers a viable tool for early intervention planning. Future work should focus on longitudinal datasets and further validation to support clinical translation and personalized therapeutic strategies.

Background/Objectives: The process of designing and fabricating bone tissue engineering scaffolds is a multi-faceted and intricate process. The scaffold is designed to attach cells to the required volume of regeneration to subsequently migrate, grow, differentiate, proliferate, and consequently develop tissue within the scaffold which, in time, will degrade, leaving just the regenerated tissue. The fabrication of tissue scaffolds requires adapting the properties of the scaffolds to mimic, to a large extent, the specific characteristics of each type of bone tissue. However, there are some significant limitations due to the constrained scaffolds’ architecture and structural features that inhibit the optimization of bone scaffolds. Methods: To overcome these shortcomings, new computational approaches for scaffold design have been adopted through currently adopted computational methods such as finite element analysis (FEA), computational fluid dynamics (CFD), and fluid–structure interaction (FSI). Results: This paper presents a narrative review of the state of the art in the field of parametric numerical modeling and computational fluid dynamics geometry-based models used in bone tissue engineering. Computational methods for scaffold design improve the process of constructing scaffolds and contribute to tissue engineering. Conclusions: This paper highlights the benefits of computational methods on employing scaffolds with different architectures and inherent characteristics that can potentially contribute to a favorable environment for hosting cells and predict their behavior and response. By recognizing these benefits, researchers can enhance and optimize scaffold properties for future advancements in tissue engineering research that will lead to more accurate and robust outcomes.

Background/Objectives: Cerebrovascular events, such as strokes, are often preceded by the rupture of atherosclerotic plaques in the carotid arteries. This work introduces a novel approach to predict the occurrence of such events by integrating computational fluid dynamics (CFD), structural analysis, and machine learning (ML) techniques. The objective is to develop a predictive model that combines both imaging and non-imaging data to assess the risk of carotid atherosclerosis and subsequent cerebrovascular events, ultimately improving clinical decision-making. Methods: A multidisciplinary approach was employed, utilizing 3D reconstruction techniques and blood-flow simulations to extract key plaque characteristics. These were combined with patient-specific clinical data for risk evaluation. The study involved 134 asymptomatic individuals diagnosed with carotid artery disease. Data imbalance was addressed using two distinct approaches, with the optimal method chosen for training a Gradient Boosting Tree (GBT) classifier. The model’s performance was evaluated in terms of accuracy, sensitivity, specificity, and ROC AUC. Results: The best-performing GBT model achieved a balanced accuracy of 88%, with a ROC AUC of 0.92, a sensitivity of 0.88, and a specificity of 0.91. This demonstrates the model’s high predictive power in identifying patients at risk for cerebrovascular events. Conclusions: The proposed method effectively combines CFD, structural analysis, and ML to predict cerebrovascular event risk in patients with carotid artery disease. By providing clinicians with a tool for better risk assessment, this approach has the potential to significantly enhance clinical decision-making and patient outcomes.

Background: There has been a large discussion in literature regarding the proper management of asymptomatic patients with significant carotid artery stenosis. This study aims to identify potential risk factors associated with high-risk carotid plaques. Methods: This is a retrospective study based on a prospective database. Eligible patients had medium to severe symptomatic or asymptomatic carotid stenosis (>= 50%, North American Symptomatic Carotid Endarterectomy Trial criteria). This study will analyze patients recruited by our institution as part of the multicenter TAXINOMISIS project (NCT03495830). According to protocol, all patients underwent a colored Duplex ultrasound examination and a magnetic resonance angiography at baseline. Carotid plaques were classified according to Gray-Weale ultrasonographic criteria (types I-V). Main outcomes included the occurrence of symptoms, the high/low echogenicity of the plaque, the existence of intraplaque hemorrhage and the existence of lipidic/necrotic core. Secondary, risk factors associated with the aforementioned outcomes were evaluated. Results: A total of 62 patients (mean age: 68.7 +/- 9.3 years, 66.1% males, 24.2% symptomatic) were recruited by our department. Mean carotid stenosis was 70.81% +/- 13.53%. In multivariate regression analysis, C-reactive protein > 2 mg/l was strongly associated with symptomatic stenosis (odds ratio [OR] - 9.92 [1.12-88.178]; P - 0.039), and low high-density lipoprotein levels (<1200 mmol/l) were associated with lipidic/necrotic plaque core (OR - 16.88 [1.10-259.30]; P - 0.043). Low high-density lipoprotein levels (OR - 7.22 [1.00-51.95], P - 0.049) and HbA1c >7% (OR - 0.08 [0.01-0.93], P - 0.044) were associated with type III/IV plaques whereas HgAbc1 >7% (OR - 14.26 [1.21-168.34], P - 0.035) was associated with type V plaques. Conclusions: This preliminary study has revealed some potential risk factors associated with unstable carotid plaques. These data could help the future development of prognostic models for early detection patients that could benefit from further intervention.

This study aims to comprehensively evaluate the mechanical performance of bone scaffolds, focusing on a compression test displacement rate 0.5mm/s and 2mm/min. The research employs the Yeoh 3 rd order model for the hyperelastic PLA scaffolds to simulate the compression tests, which are validated using experimental results. More specifically, the stress- strain curves derived from the in-silico analysis exhibit strong agreement with the experimental results, highlighting the reliability of the computational approach. Two distinct scaffold geometries were employed and a comparative analysis of their responses to specific loading conditions had been performed. The investigation reveals that the PCL-50 scaffold demonstrates promising mechanical behavior in terms of better endurance in such loading conditions.

Forwarded by the technological urge of this era, several computational methods are implemented to give further insights into possible outcomes of diseases. In this context, atherosclerosis, which is one of the most fatal diseases nowadays, is treated alike, where several computational models are proposed annually allowing for the evaluation of several outcomes for patient specific cases. Among them, one of the most significant models is able to predict the atherosclerotic evolution over time. In this proof-of-concept study, we aim to investigate the effect of plaque morphology on plaque rupture in a two-case scenario - a longitudinal and a bulk plaque evolution in 3D-reconstructed patient-specific carotids arteries. Our approach is based on a three-step process: i) the implementation of a state-of-the-art plaque growth model that predicts evolving and new plaques in real patient specific carotid arteries, ii) the selection of 2 patient cases, one with longitudinal plaque evolution and one with bulk plaque evolution and, finally, iii) the evaluation the maximum principal stress over the plaques and the endothelium layers to assess the plaque rupture risk. The results indicate that the evolving plaques towards the lumen, not only cause stenoses but also are more prone to rupture. Clinical relevance- This proof-of-concept work establishes that the plaques that grow towards the luminal border present with a higher risk of potential rupture compared to plaques that grow longitudinally, thus giving valuable insights to clinicians for important decision making regarding potential endarterectomy procedures.

IntroductionThe relationship between carotid artery stenosis (CAS) and ipsilateral silent brain ischemia (SBI) remains unclear, with uncertain therapeutic implications. The present study, part of the TAXINOMISIS project (nr. 755,320), aimed to investigate SBIs in patients with asymptomatic CAS, correlating them with clinical, carotid ultrasonographic data, and CFD analyses.MethodsThe TAXINOMISIS clinical trial study (nr. NCT03495830) involved six vascular surgery centers across Europe, enrolling patients with asymptomatic and symptomatic CAS ranging from 50 to 99%. Patients underwent carotid ultrasound and magnetic resonance imaging (MRI), including brain diffusion-weighted, T2-weighted/FLAIR, and T1-weighted sequences. Brain MRI scans were analyzed for the presence of SBI according to established definitions. Ultrasound assessments included Doppler and CFD analysis. Only asymptomatic patients were included in this substudy.ResultsAmong 195 asymptomatic patients, the mean stenosis (NASCET) was 64.1%. Of these, a total of 33 patients (16.9%) had at least one SBI detected on a brain MRI scan. Specifically, 19 out of 33 patients (57.6%) had cortical infarcts, 4 out of 33 patients (12.1%) had ipsilateral lacunar infarcts, 6 out of 33 patients had (18.2%) subcortical infarcts, 1 out of 33 patients (3.0%) had both cortical and lacunar infarcts, and 3 out of 33 patients (9.1%) both cortical and subcortical infarcts. Patients with SBIs exhibited significantly higher risk factors, including a higher body mass index (28.52 ± 9.38 vs. 26.39 ± 3.35, p = 0.02), diastolic blood pressure (80.87 ± 15.73 mmHg vs. 80.06 ± 8.49 mmHg, p = 0.02), creatinine levels (93.66 ± 34.61 μmol/L vs. 84.69 ± 23.67 μmol/L, p = 0.02), and blood triglycerides (1.8 ± 1.06 mmol/L vs. 1.48 ± 0.78 mmol/L, p = 0.03). They also had a higher prevalence of cardiovascular interventions (29.6% vs. 13.8%, p = 0.04), greater usage of third/fourth-line antihypertensive treatment (50%vs16%, p = 0.03), and anticoagulant medications (60% vs. 16%, p = 0.01). Additionally, the number of contralateral cerebral infarcts was higher in patients with SBIs (35.5% vs. 13.4%, p < 0.01). Moreover, carotid ultrasound revealed higher Saint Mary’s ratios (15.33 ± 12.45 vs. 12.96 ± 7.99, p = 0.02), and CFD analysis demonstrated larger areas of low wall shear stress (WSS) (0.0004 ± 0.0004 m2 vs. 0.0002 ± 0.0002 m2, p < 0.01).ConclusionThe TAXINOMISIS clinical trial provides valuable insights into the prevalence and risk factors associated with SBIs in patients with moderate asymptomatic carotid stenosis. The findings suggest that specific hemodynamic and arterial wall characteristics may contribute to the development of silent brain infarcts.

The fractional flow reserve (FFR) is well recognized as a gold standard measure for the estimation of functional coronary stenosis. Technological progressions in image processing have empowered the reconstruction of three-dimensional models of the coronary arteries via both non-invasive and invasive imaging modalities. The application of computational fluid dynamics (CFD) techniques to coronary 3D anatomical models allows the virtual evaluation of the hemodynamic significance of a coronary lesion with high diagnostic accuracy. Methods: Search of the bibliographic database for articles published from 2011 to 2023 using the following search terms: invasive FFR and non-invasive FFR. Pooled analysis of the sensitivity and specificity, with the corresponding confidence intervals from 32% to 94%. In addition, the summary processing times were determined. Results: In total, 24 studies published between 2011 and 2023 were included, with a total of 13,591 patients and 3345 vessels. The diagnostic accuracy of the invasive and non-invasive techniques at the per-patient level was 89% (95% CI, 85–92%) and 76% (95% CI, 61–80%), respectively, while on the per-vessel basis, it was 92% (95% CI, 82–88%) and 81% (95% CI, 75–87%), respectively. Conclusion: These opportunities providing hemodynamic information based on anatomy have given rise to a new era of functional angiography and coronary imaging. However, further validations are needed to overcome several scientific and computational challenges before these methods are applied in everyday clinical practice.

Atherosclerotic carotid plaque development results in a steady narrowing of the artery lumen, which may eventually trigger catastrophic plaque rupture leading to thromboembolism and stroke. The primary cause of ischemic stroke in the EU is carotid artery disease, which increases the demand for tools for risk stratification and patient management in carotid artery disease. Additionally, advancements in cardiovascular modeling over the past few years have made it possible to build accurate three-dimensional models of patient-specific primary carotid arteries. Computational models then incorporate the aforementioned 3D models to estimate either the development of atherosclerotic plaque or a number of flow-related parameters that are linked to risk assessment. This work presents an attempt to provide a carotid artery stenosis prognostic model, utilizing non-imaging and imaging data, as well as simulated hemodynamic data. The overall methodology was trained and tested on a dataset of 41 cases with 23 carotid arteries with stable stenosis and 18 carotids with increasing stenosis degree. The highest accuracy of 71% was achieved using a neural network classifier. The novel aspect of our work is the definition of the problem that is solved, as well as the amount of simulated data that are used as input for the prognostic model.Clinical Relevance—A prognostic model for the prediction of the trajectory of carotid artery atherosclerosis is proposed, which can support physicians in critical treatment decisions.

The severity of coronary artery disease can be assessed invasively using the Fractional Flow Reserve (FFR) index which is a useful diagnostic tool for the clinicians to select the treatment approach. The present work capitalizes a Gaussian process (GP) framework over graphs for the prediction of FFR index using only non-invasive imaging and clinical features. More specifically, taking the per-node one-hop connectivity vector as input, we employed a regression-based task by applying an ensemble of graph-adapted Gaussian process experts, with a data-adaptive fashion via online training. The main novelty of the work lies in the fact that for the first time in a medical field the inference model considers only the similarity condition of the patients, instead of their features. Our results demonstrate the impressive merits of the proposed medical EGP (MedEGP) method, in comparison to the single GP, and Linear Regression (LR) models to predict the FFR index, with well-calibrated uncertainty.Clinical Relevance— This paper establishes an accurate non-invasive approach to predict the FFR for the diagnosis of coronary artery disease.

A reform in the diagnosis and treatment process is urgently required as carotid artery disease remains a leading cause of death in the world. To this purpose, all computational techniques are now being applied to enhancing the most cutting-edge diagnosis techniques. Computational modeling of plaque generation and evolution is being refined over the past years to forecast the atherosclerotic progression and the corresponding risk in patient-specific carotid arteries. A prerequisite to their implementation is the reconstruction of the precise three-dimensional models of patient-specific main carotid arteries. Even with the most sophisticated algorithms, accurate reconstruction of the arterial vessel is frequently difficult. Furthermore, there are several works of plaque growth modeling that ignore the reconstruction of the artery's outer layer in favor of a virtual one. In this paper, we investigate the importance of an accurate adventitia layer in plaque growth modeling. This is done as a comparative study by implementing a novel plaque growth model in two reconstructed carotid arterial segments using either their realistic or virtual adventitia layer as input. The results indicate that accurate adventitia reconstruction is of minor importance regarding species distributions and plaque growth in carotid segments, which initially did not contain any plaque regions.Clinical Relevance— The findings of this comparative study emphasize the importance of precise adventitia geometry in plaque growth modeling. As a result, this work sets a higher standard for publishing new plaque growth models.

One of the main causes of death worldwide is carotid artery disease, which causes increasing arterial stenosis and may induce a stroke. To address this problem, the scientific community aims to improve our understanding of the underlying atherosclerotic mechanisms, as well as to make it possible to forecast the progression of atherosclerosis. Additionally, over the past several years, developments in the field of cardiovascular modeling have made it possible to create precise three-dimensional models of patient-specific main carotid arteries. The aforementioned 3D models are then implemented by computational models to forecast either the progression of atherosclerotic plaque or several flow-related metrics which are correlated to risk evaluation. A precise representation of both the blood flow and the fundamental atherosclerotic process within the arterial wall is made possible by computational models, therefore, allowing for the prediction of future lumen stenoses, plaque areas and risk prediction. This work presents an attempt to integrate the outcomes of a novel plaque growth model with advanced blood flow dynamics where the deformed luminal shape derived from the plaque growth model is compared to the actual patient-specific luminal model in terms of several hemodynamic metrics, to identify the prediction accuracy of the aforementioned model. Pressure drop ratios had a mean difference of <3%, whereas OSI-derived metrics were identical in 2/3 cases.Clinical Relevance—This establishes the accuracy of our plaque growth model in predicting the arterial geometry after the desired timeline.

During the past years, tissue engineering has been qualified as a solid surrogate of autografts in the stimulation of bone tissue regeneration, through the development of three dimensional (3D) porous matrices, commonly known as scaffolds. Polycaprolactone-based scaffolds have attracted worldwide attention as promising biodegradable implants in bone tissue engineering. Finite Element Analysis is used in tissue engineering to evaluate the mechanical behaviour and to simulate the processes inside the scaffold. In this work, we analysed two regular polycaprolactone scaffold structures (with sharp edges and with rounded edges) by performing computational fluid dynamics simulations, to compare velocity and pressure distributions for the two scaffolds. A sensitivity analysis was performed for the two different scaffold geometries and the element size that was used was 0.07mm. A laminar flow 1 mm/s was used as an inlet boundary condition at the top of the scaffolds and the fluid was treated as Newtonian [1, 2]. Our results indicate that the scaffold with sharp edges depicted a better flow velocity distribution within the pores of the scaffold for all layers and a lower speed, compared to the scaffold with rounded edges. The pressure gradually decreases from the inlet to the outlet, with no substantial differences between the two geometries, with a uniform pressure distribution along the height of the scaffolds. Based on the Computational Fluid Dynamics derived results, the rounded edges geometry produced more appealing velocity distribution results which makes it superior compared to the sharp edges scaffold.

Carotid Artery Disease is a complex multi-disciplinary medical condition causing strokes and several other disfunctions to individuals. Within this work, a cloud - based platform is proposed for clinicians and medical doctors that provides a comprehensive risk assessment tool for carotid artery disease. It includes three modeling levels: baseline data-driven risk assessment, blood flow simulations and plaque progression modeling. The proposed models, which have been validated through a wide set of studies within the TAXINOMISIS project, are delivered to the end users through an easy-to-use cloud platform. The architecture and the deployment of this platform includes interfaces for handling the electronic patient record, the 3D arterial reconstruction, blood flow simulations and risk assessment reporting. TAXINOMISIS, compared with both similar software approaches and with the current clinical workflow, assists clinicians to treat patients more effectively and more accurately by providing innovative and validated tools.Clinical Relevance - Asymptomatic carotid artery disease is a prevalent condition that affects a significant portion of the population, leading to an increased risk of stroke and other cardiovascular events. Early detection and appropriate treatment of this condition can significantly reduce the risk of adverse outcomes and improve patient outcomes. The development of a software tool to assist clinicians in the assessment and management of asymptomatic patients with carotid artery disease is therefore of great clinical relevance. By providing a comprehensive and reliable assessment of the disease and its risk factors, this tool will enable clinicians to make informed decisions regarding patient management and treatment. The impact of this tool on patient outcomes and the reduction of healthcare costs will be of great importance to both patients and the healthcare system.

Through the recent years, tissue engineering has been proven as a solid substitute of autografts in the stimulation of bone tissue regeneration, through the development of three dimensional (3D) porous matrices, commonly known as scaffolds. In this work, we analysed two scaffold structures with 500μm pore size, by performing computational fluid dynamics simulations, to compare permeability, Wall Shear Stress (WSS), velocity and pressure distributions. Taking into account those parameters the geometry named as "PCL-50" was the best to anticipate showing a superior performance in supporting cell growth due to the improved flow characteristics in the scaffold.Clinical Relevance— Bone defects that require invasive surgical treatment with high risks in terms of success and effectiveness. Bone tissue engineering (BTE) in combination with the use of computational fluid dynamics (CFD) analysis tools aim to assist in designing optimal scaffolds that better promote bone growth and repair. The fluid dynamic characteristics of a porous scaffold plays a vital role in cell viability and cell growth, affecting the osteogenic performance of the scaffold.

Since atherosclerosis has been declared as the leading cause of mortality worldwide, the imminent need for the design and development of straightforward computational modeling workflows to improve the existing cardiovascular disease risk stratification models is more important than ever. Agent-based modelling (ABM) is a promising computational approach which can be utilized for decision making in various domains from the healthcare sector to industrial applications. In the present study, we propose a straightforward approach for atheromatic plaque progression in the coronary and peripheral arteries using specialized mathematical models and computational simulations which will enable the accurate prediction of the cardiovascular disease evolution. The model incorporates the realistic 3D geometry of the artery and is the first ABM implemented in C#. According to our results, the 3D ABM was able to simulate the Trans Endothelial Migration of Lymphocytes, Monocytes and Neutrophils, the artery wall cells, endothelium cells and plaque cells reducing the time step for each cycle from 40 seconds to 0.04 seconds per cycle.

The carotid artery disease is one of the leading causes of mortality worldwide, as it leads to the progressive arterial stenosis that may result to stroke. To address this issue, the scientific community is attempting not only to enrich our knowledge on the underlying atherosclerotic mechanisms, but also to enable the prediction of the atherosclerotic progression. This study investigates the role of T-cells in the atherosclerotic plaque growth process through the implementation of a computational model in realistic geometries of carotid arteries. T-cells mediate in the inflammatory process by secreting interferon-y that enhances the activation of macrophages. In this analysis, we used 5 realistic human carotid arterial segments as input to the model. In particular, magnetic resonance imaging data, as well as, clinical data were collected from the patients at two time points. Using the baseline data, plaque growth was predicted and correlated to the follow-up arterial geometries. The results exhibited a very good agreement between them, presenting a high coefficient of determination R2=0.64.

The progression of atherosclerotic carotid plaque causes a gradual stenosis in the arterial lumen which might result to catastrophic plaque rupture ending to thromboembolism and stroke. Carotid artery disease is the main cause for ischemic stroke in the EU, thus intensifying the need of the development of tools for risk stratification and patient management in carotid artery disease. In this work, we present a comparative study between ultrasound-based and MRI-based 3D carotid artery models to investigate if US-based models can be used to assess the hemodynamic status of the carotid vasculature compared with the respective MRI-based models which are considered as the most realistic representation of the carotid vasculature. In-house developed algorithms were used to reconstruct the carotid vasculature in 3D. Our work revealed a promising similarity between the two methods of reconstruction in terms of geometrical parameters such as cross-sectional areas and centerline lengths, as well as simulated hemodynamic parameters such as peak Time-Averaged WSS values and areas of low WSS values which are crucial for the hemodynamic status of the cerebral vasculature. The aforementioned findings, therefore, constitute carotid US a possible MRI surrogate for the initial carotid artery disease assessment in terms of plaque evolution and possible plaque destabilization.