Thrombosis in rotary blood pumps arises from complex flow conditions that remain difficult to translate into reliable and interpretable risk predictions using existing computational models. This limitation reflects an incomplete understanding of how specific flow features contribute to thrombus initiation and growth. This study introduces an interpretable machine learning framework for spatial thrombosis assessment based directly on computational fluid dynamics-derived flow features. A logistic regression (LR) model combined with a structured feature-selection pipeline is used to derive a compact and physically interpretable feature set, including nonlinear feature combinations. The framework is trained using spatial risk patterns from a validated, macro-scale thrombosis model for two representative scenarios. The model reproduces the labeled risk distributions and identifies distinct sets of flow features associated with increased thrombosis risk. When applied to a centrifugal pump, despite training on a single axial pump operating point, the model predicts plausible thrombosis-prone regions. These results show that interpretable machine learning can link local flow features to th
Deep Venous Thrombosis (DVT) is a common vascular disease with blood clots inside deep veins, which may block blood flow or even cause a life-threatening pulmonary embolism. A typical exam for DVT using ultrasound (US) imaging is by pressing the target vein until its lumen is fully compressed. However, the compression exam is highly operator-dependent. To alleviate intra- and inter-variations, we present a robotic US system with a novel hybrid force motion control scheme ensuring position and force tracking accuracy, and soft landing of the probe onto the target surface. In addition, a path-based virtual fixture is proposed to realize easy human-robot interaction for repeat compression operation at the lesion location. To ensure the biometric measurements obtained in different examinations are comparable, the 6D scanning path is determined in a coarse-to-fine manner using both an external RGBD camera and US images. The RGBD camera is first used to extract a rough scanning path on the object. Then, the segmented vascular lumen from US images are used to optimize the scanning path to ensure the visibility of the target object. To generate a continuous scan path for developing virtual
Deep Vein Thrombosis (DVT) is a common yet potentially fatal condition, often leading to critical complications like pulmonary embolism. DVT is commonly diagnosed using Ultrasound (US) imaging, which can be inconsistent due to its high dependence on the operator's skill. Robotic US Systems (RUSs) aim to improve diagnostic test consistency but face challenges with the complex scanning pattern needed for DVT assessment, where precise control over US probe pressure is crucial for indirectly detecting occlusions. This work introduces an imitation learning method, based on Kernelized Movement Primitives (KMP), to standardize DVT US exams by training an autonomous robotic controller using sonographer demonstrations. A new recording device design enhances demonstration ergonomics, integrating with US probes and enabling seamless force and position data recording. KMPs are used to capture scanning skills, linking scan trajectory and force, enabling generalization beyond the demonstrations. Our approach, evaluated on synthetic models and volunteers, shows that the KMP-based RUS can replicate an expert's force control and image quality in DVT US examination. It outperforms previous methods u
Subclinical leaflet thrombosis (SLT) is a potentially serious complication of aortic valve replacement with a bioprosthetic valve in which blood clots form on the replacement valve. SLT is associated with increased risk of transient ischemic attacks and strokes and can progress to clinical leaflet thrombosis. SLT following aortic valve replacement also may be related to subsequent structural valve deterioration, which can impair the durability of the valve replacement. Because of the difficulty in clinical imaging of SLT, models are needed to determine the mechanisms of SLT and could eventually predict which patients will develop SLT. To this end, we develop methods to simulate leaflet thrombosis that combine fluid-structure interaction and a simplified thrombosis model that allows for deposition along the moving leaflets. Additionally, this model can be adapted to model deposition or absorption along other moving boundaries. We present convergence results and quantify the model's ability to realize changes in valve opening and pressures. These new approaches are an important advancement in our tools for modeling thrombosis in which they incorporate both adhesion to the surface of
Thrombosis under high-shear conditions is mediated by the mechanosensitive blood glycoprotein von Willebrand Factor (vWF). vWF unfolds in response to strong flow gradients and facilitates rapid recruitment of platelets in flowing blood. While the thrombogenic effect of vWF is well recognized, its conformational response in complex flows has largely been omitted from numerical models of thrombosis. We recently presented a continuum model for the unfolding of vWF, where we represented vWF transport and its flow-induced conformational change using convection-diffusion-reaction equations. Here, we incorporate the vWF component into our multi-constituent model of thrombosis, where the local concentration of stretched vWF amplifies the deposition rate of free-flowing platelets and reduces the shear cleaning of deposited platelets. We validate the model using three benchmarks: in vitro model of atherothrombosis, a stagnation point flow, and the PFA-100, a clinical blood test commonly used for screening for von Willebrand Disease (vWD). The simulations reproduced the key aspects of vWF-mediated thrombosis observed in these experiments, such as the thrombus location, thrombus growth dynamic
Hemostasis and thrombosis are often thought as two sides of the same clotting mechanism whereas hemostasis is a natural protective mechanism to prevent bleeding and thrombosis is a blood clot abnormally formulated inside a blood vessel, blocking the normal blood flow. The evidence to date suggests that at least arterial thrombosis results from the same critical pathways of hemostasis. Analysis of these complex processes and pathways using quantitative systems pharmacological model-based approach can facilitate the delineation of the causal pathways that lead to the emergence of thrombosis. In this paper, we provide an overview of the main molecular and physiological mechanisms associated with hemostasis and thrombosis, and review the models and quantitative system pharmacological modeling approaches that are relevant in characterizing the interplay among the multiple factors and pathways of thrombosis. An emphasis is given to computational models for drug development. Future trends are discussed.
Percutaneous catheter pumps are intraventricular temporary mechanical circulatory support (MCS) devices that are positioned across the aortic valve into the left ventricle (LV) and provide continuous antegrade blood flow from the LV into the ascending aorta (AA). MCS devices are most often computationally evaluated as isolated devices subject to idealized steady-state blood flow conditions. In clinical practice, MCS devices operate connected to or within diseased pulsatile native hearts and are often complicated by hemocompatibility related adverse events such as stroke, bleeding, and thrombosis. Whereas aspects of the human circulation are increasingly being simulated via computational methods, the precise interplay of pulsatile LV hemodynamics with MCS pump hemocompatibility remains mostly unknown and not well characterized. Technologies are rapidly converging such that next-generation MCS devices will soon be evaluated in virtual physiological environments that increasingly mimic clinical settings. The purpose of this brief communication is to report results and lessons learned from an exploratory CFD simulation of hemodynamics and thrombosis for a catheter pump situated within
Mathematical models of thrombosis are currently used to study clinical scenarios of pathological thrombus formation. Most of these models involve inherent uncertainties that must be assessed to increase the confidence in model predictions and identify avenues of improvement for both thrombosis modeling and anti-platelet therapies. In this work, an uncertainty quantification analysis of a multi-constituent thrombosis model is performed considering a common assay for platelet function (PFA-100). The analysis is performed using a polynomial chaos expansion as a parametric surrogate for the thrombosis model. The polynomial approximation is validated and used to perform a global sensitivity analysis via computation of Sobol' coefficients. Six out of fifteen parameters were found to be influential in the simulation variability considering only individual effects. Nonetheless, parameter interactions are highlighted when considering the total Sobol' indices. In addition to the sensitivity analysis, the surrogate model was used to compute the PFA-100 closure times of 300,000 virtual cases that align well with clinical data. The current methodology could be used including common anti-platele
The assessment of left atrial appendage (LAA) thrombogenesis has experienced major advances with the adoption of patient-specific computational fluid dynamics (CFD) simulations. Nonetheless, due to the vast computational resources and long execution times required by fluid dynamics solvers, there is an ever-growing body of work aiming to develop surrogate models of fluid flow simulations based on neural networks. The present study builds on this foundation by developing a deep learning (DL) framework capable of predicting the endothelial cell activation potential (ECAP), linked to the risk of thrombosis, solely from the patient-specific LAA geometry. To this end, we leveraged recent advancements in Geometric DL, which seamlessly extend the unparalleled potential of convolutional neural networks (CNN), to non-Euclidean data such as meshes. The model was trained with a dataset combining 202 synthetic and 54 real LAA, predicting the ECAP distributions instantaneously, with an average mean absolute error of 0.563. Moreover, the resulting framework manages to predict the anatomical features related to higher ECAP values even when trained exclusively on synthetic cases.
The geometry of blood vessels strongly affects hemostasis and thrombosis through red blood cell (RBC) dynamics and platelet margination. Growing platelet aggregates, in turn, reshape the local vessel wall topography, leading to a strongly coupled system. However, it is not well understood how surface heterogeneities alter local hemodynamics and platelet margination, thereby driving further aggregate growth. This study investigates how hematocrit (Ht) and shear rate affect RBC dynamics, cell-free layer (CFL) thickness, and platelet margination near a sinusoidal wall. The sinusoidal wall, with crests and valleys aligned with the flow direction, serves as a model of the flow-aligned platelet aggregates observed in microfluidic experiments [Pero et al., CRPS, 2024]. We perform three-dimensional immersed-boundary-lattice-Boltzmann simulations of particulate blood flow with deformable RBCs and nearly rigid spherical platelets. Our results show that platelet margination is primarily governed by Ht and is more pronounced in regions where the CFL thickness is similar to the platelet size. At low Ht, platelets preferentially accumulate at crests, promoting high-amplitude aggregate growth. In
Coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2, represents a major global pandemic of the 21st century, with long-term effects termed long COVID. This systematic review and network meta-analysis (NMA) evaluated pharmacological interventions for adults with long COVID, incorporating randomized controlled trials and adjusted observational studies. Primary outcomes included all-cause mortality, hospitalization, ICU admission, and mechanical ventilation; secondary outcomes covered symptom recovery across five categories, with safety assessed via adverse events. Results from random-effects models showed that saline nasal irrigation (SMD=21.10, 95% CI [16.91, 25.30]), nitrilotriacetic acid trisodium (SMD=7.40 [5.79, 9.01]), tetra sodium pyrophosphate (SMD=3.69 [2.61, 4.77]), and sodium gluconate (SMD=3.01 [1.92, 4.09]) significantly improved anosmia versus control. For thrombosis, rivaroxaban reduced arterial (OR=0.33 [0.01, 8.19]) and venous thrombotic events (OR=0.12 [0.01, 0.97]), while therapeutic-dose anticoagulants lowered thrombotic risks but increased major bleeding events (OR=1.86 [1.19, 2.89]) compared to prophylactic dosing. This NMA provides comparative evidence to
Endovascular treatment of cerebral aneurysms aims to achieve functional occlusion and isolation of the aneurysm sac from bloodflow. In clinical practice, treatment success is assessed primarily through digital subtraction angiography (DSA), which visualizes contrast-agent inflow and washout but does not directly resolve thrombus formation driving early occlusion. We present a computational framework that couples acute fibrin thrombus formation with virtual angiography, enabling early thrombus growth to be interpreted through clinically familiar DSA-like imaging. Three common treatment strategies: endovascular coiling, flow diversion, and stent-assisted coiling, are modeled under pulsatile hemodynamics and linked to simulated contrast transport. Across three representative aneurysm morphologies, the simulations demonstrate that while devices reduce inflow, residual contrast access and trapping may persist, with early thrombus formation contributing substantially to perfusion suppression and altered washout patterns. These effects are clearly reflected in the virtual angiographic imaging. The importance of vortical structures in device-induced thrombosis is highligthed in one of the
Thrombotic vascular diseases contribute to significant global mortality, yet current therapeutic strategies face persistent challenges including bleeding risks, suboptimal efficiency, and procedural complexity. Here, we report a micro-explosive thermochemical thrombolysis (METCT) therapy via injectable liquid alkali metal (LAM) encapsulated in dimethyl silicone (LAM@oil), which enables prompt, efficient and safe vascular recanalization within an ultrafast timeframe (< 90 seconds). This LAM@oil system effectively disrupts thrombus tissue through a synergistic triple-action mechanism: Mechanical micro-explosions forces, alkaline ablation due to highly localized exothermic chemical reactions, and thermal thrombolysis mediated by elevated temperature. Upon thrombolysis completion, the non-toxic reaction byproducts (sodium and potassium ions) exhibit physiologically biocompatible and metabolizable effects. Critically, the LAM@oil demonstrates significantly higher thrombolytic efficacy compared to clinically available thrombolytic drugs (residual thrombus area percent 10.87%+-7.16% for LAM@oil vs. 80.86%+-13.32% for urokinase), with no associated bleeding risks. This strategy opens a
Blood coagulation is governed by tightly regulated reaction networks that unfold within a flowing, heterogeneous microvascular environment. Reduced kinetic models of the intrinsic and extrinsic pathways have seen limited in vitro validation, and their behavior within spatially resolved flow fields remains largely unexplored. Here, we embed two established reduced networks into a recently proposed mesoscale particle-based framework that resolves fluid momentum transport alongside multispecies advection-diffusion-reaction dynamics. We investigate the initiation phase of coagulation by simulating thrombin formation in microvascular geometries and in vitro assays, and we assess the framework's ability to reproduce thrombin generation curves (TGCs) under physiologically relevant conditions. We further examine how variations in fibrinogen levels - an important determinant of clot structure and a biomarker for inflammation and thrombosis - affect thrombin and fibrin formation. Overall, this study provides a unified computational approach for analysing how biochemical kinetics interact with transport processes, offering insights relevant to thrombosis modeling and blood diagnostics.
Vascular diseases such as atherosclerosis, thrombosis, and aneurysms can lead to life-threatening medical events. Conventional catheter- or guidewire-based interventional devices often struggle to navigate through highly tortuous vasculature. The recently developed multifunctional magnetic milli-spinner offers a promising wireless solution by integrating a central through-hole and side slits into a cylindrical body with helical fins, enabling rapid and stable navigation for clot debulking, targeted drug delivery, and aneurysm treatment. Here, we combine computational fluid dynamics simulations with experimental validation to optimize the milli-spinner's structural design for high-velocity propulsion and high-efficiency clot debulking in tubular flow environments. By systematically investigating the effects of through-hole radius, fin number, fin helical angle, and slit dimension on propulsion performance, the optimized milli-spinner achieves swimming velocities of 55 cm/s (175 body lengths per second) in saline water and 44 cm/s (140 body lengths per second) in a fluid with viscosity (3.5 mPa.s) comparable to that of arterial blood at high shear rates, far exceeding existing unteth
Platelet adhesion and aggregation are essential for primary hemostasis, forming a clot that quickly stops initial bleeding. Despite this critical role, the dynamic interactions of platelet receptors with exposed collagen and von Willebrand factor (vWF) at the injury site and how these interactions influence thrombus formation under varying blood flow conditions are not fully understood. This study aimed to investigate the mechanisms of platelet adhesion and aggregation on collagen- or vWF-coated surfaces numerically. We combined the stochastic Bell's law with a deterministic elastic force featuring a time-dependent coefficient within the context of a dissipative particle dynamics (DPD) model to simulate thrombosis formation numerically. Our simulation results revealed that the numerically predicted platelet adhesion patterns closely matched experimental observations reported in the literature, demonstrating accurate replication of platelet behavior on collagen- and vWF-coated surfaces. Consequently, our deterministic/stochastic force model in DPD provides valuable insights into platelet adhesion dynamics under different flow conditions. These results contribute to a deeper understa
Purpose: Aortic dissections are life-threatening cardiovascular conditions requiring accurate segmentation of true lumen (TL), false lumen (FL), and false lumen thrombosis (FLT) from CTA images for effective management. Manual segmentation is time-consuming and variable, necessitating automated solutions. Materials and Methods: We developed four deep learning-based pipelines for Type B aortic dissection segmentation: a single-step model, a sequential model, a sequential multi-task model, and an ensemble model, utilizing 3D U-Net and Swin-UnetR architectures. A dataset of 100 retrospective CTA images was split into training (n=80), validation (n=10), and testing (n=10). Performance was assessed using the Dice Coefficient and Hausdorff Distance. Results: Our approach achieved superior segmentation accuracy, with Dice Coefficients of 0.91 $\pm$ 0.07 for TL, 0.88 $\pm$ 0.18 for FL, and 0.47 $\pm$ 0.25 for FLT, outperforming Yao et al. (1), who reported 0.78 $\pm$ 0.20, 0.68 $\pm$ 0.18, and 0.25 $\pm$ 0.31, respectively. Conclusion: The proposed pipelines provide accurate segmentation of TBAD features, enabling derivation of morphological parameters for surveillance and treatment planni
Albeit the hemodynamics of artificial heart valves has been investigated for several decades, the local shear-induced activation potential and subsequent transport phenomena of activated platelets in different valve designs, which mediate thrombosis, remains poorly understood. Here, platelet activation due to local shear stresses and the associated transport phenomena are investigated in two designs of mechanical heart valves (MHVs), namely a trileaflet MHV (TMHV) and a bileaflet MHV (BMHV) and compared against a surgical bioprosthetic heart valve (BHV) as a control. It is observed that the local activation and transport of platelets in any aortic region reach a cyclic state, with MHVs showing higher levels of both activation and transport than BHV. When integrated over the volume of the aortic sinuses and central lumen, the local activation is, respectively, 5.90 and 2.26 times higher in BMHV whereas 2.97 and 1.39 times higher in TMHV than in BHV. The washout of activated platelets from the sinuses and central lumen is, respectively, 10.40 and 2.39 times higher in BMHV while 4.90 and 1.40 times higher in TMHV compared to BHV. The low washout of sinuses in BHV is also demonstrated
Motivated by the use of Taylor-Couette flow in extracorporeal circulation devices [K$\ddot{\rm o}$rfer et al., 2003, 26(4): 331-338], where it leads to an accumulation of platelets and plasma proteins in the vortex center and therefore to a decreased probability of contact between platelets and material surfaces and its protein adsorption per square unit is significantly lower than laminar flow. Increased platelet adhesion or protein adsorption on the device surface can induce platelet aggregation or thrombosis, which is analogous to the ``blow-up phenomenon" in mathematical modeling. Here we mathematically analyze this stability mechanism and demonstrate that sufficiently strong flow can prevent blow-up from occurring. In details, we investigate the two-dimensional Patlak-Keller-Segel-Navier-Stokes system in an annular domain around a Taylor-Couette flow $U(r,θ)=A\big(r+\frac{1}{r} \big)(-\sinθ, \cosθ)^{T}$ with $(r,θ)\in[1,R]\times\mathbb{S}^{1}$, and prove that the solutions are globally bounded without any smallness restriction on the initial cell mass or velocity when $A$ is large.
Cerebral aneurysms affect three to five percent of the population, and rupture remains a major cause of stroke-related death and disability. Current therapies, surgical clipping, endovascular coiling, and flow diversion, have improved outcomes but each carries limitations. Clipping is invasive and often unsuitable for deep or posterior lesions. Coiling is prone to recurrence from compaction or incomplete occlusion, particularly in wide-neck or fusiform aneurysms. Flow diverters offer improved durability but rely on rigid metallic scaffolds that may malappose in tortuous vessels, compromise branch arteries, delay endothelialization, and necessitate long-term dual antiplatelet therapy. These shortcomings highlight a gap in current management: devices primarily provide mechanical occlusion but fail to conform to complex geometries or reliably promote rapid, complete endothelialization. As a result, aneurysm necks may remain exposed to persistent flow, delayed healing, and thrombosis. To address this, we propose magnetically guided endothelial BioBots as a next-generation therapeutic strategy. BioBots are biodegradable hydrogel carriers embedded with magnetic nanoparticles and coated w