Cardiovascular Engineering and Technology最新文献

{"title":"Filling Fraction and Single Beat Method for Estimating Dead-Space Volume of Left Ventricle.","authors":"Melanie P Hager, George V Letsou, John C Criscione","doi":"10.1007/s13239-025-00809-7","DOIUrl":"10.1007/s13239-025-00809-7","url":null,"abstract":"<p><p>Accurate assessment of cardiac preload remains a clinical challenge, particularly in heart failure patients where preload manipulation is impractical or contraindicated. Ejection fraction (EF), while widely used, reflects a combination of preload, contractility, and afterload, and may obscure the specific contribution of preload. Inspired by a preload recruitable stroke work (PRSW) model for heart function, a concise, three factor expression for ejection fraction (EF) can be obtained where EF is directly proportional to preload, directly proportional to contractility, and inversely proportional to afterload. The contractility and afterload factors are common metrics; whereas the preload factor, filling fraction (FF), is a novel, dimensionless preload metric that sets a physiologic upperbound on EF. Unfortunately, FF measurement requires an estimate of the volume intercept or dead-space volume of the left ventricle, and such a measure has, conventionally, required preload manipulation with extrapolation to the volume axis. To address this, we propose a single-beat extrapolation method for estimating the volume intercept using end-diastolic and end-systolic measurements. In-silico testing across 10,000 synthetic left ventricular geometries demonstrates strong agreement between the estimated intercept and actual, prescribed dead-space volumes. This method enables non-invasive computation of FF from single-beat data without the need for in vivo preload manipulation. Potential clinical applications include characterizing cardiac function, stratifying heart failure subtypes, and enhancing diagnostic precision when EF is preserved but preload reserve is impaired. Future validation in clinical imaging datasets is warranted to assess real-world applicability.</p>","PeriodicalId":54322,"journal":{"name":"Cardiovascular Engineering and Technology","volume":" ","pages":"237-247"},"PeriodicalIF":1.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13102796/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147345621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Coronary CT Angiography-Based Prediction Model for Hemodynamically Significant Coronary Stenosis Integrating Morphological and Plaque Characteristics.","authors":"Chenxi Wang, Shuang Leng, Ru-San Tan, Ping Chai, Jiang Ming Fam, Adrian Fatt Hoe Low, Lohendran Baskaran, Lynette Teo, Felix Yung Jih Keng, Chee Yang Chin, Ching Ching Ong, John C Allen, Mark Yan-Yee Chan, Aaron Sung Lung Wong, Terrance Chua, Swee Yaw Tan, Soo Teik Lim, Liang Zhong","doi":"10.1007/s13239-026-00831-3","DOIUrl":"10.1007/s13239-026-00831-3","url":null,"abstract":"<p><strong>Purpose: </strong>A rising trend involves the application of machine learning to coronary computed tomography angiography (CCTA) for predicting physiological significance of coronary lesions. This study aimed to formulate a clinical model using CCTA-derived features and the least absolute shrinkage and selection operator (LASSO) regression method to predict hemodynamically significant coronary stenosis.</p><p><strong>Methods: </strong>The study population comprised individuals prospectively recruited from two tertiary medical centres with suspected or known coronary artery disease. The model was developed using patients from Centre 1 and independently validated in patients from Centre 2. All participants underwent CCTA followed by invasive coronary angiography with fractional flow reserve (FFR) measurements. The LASSO model incorporated coronary morphological and plaque features derived semi-automatically from CCTA, along with clinical and demographic variables, as input predictors. Model performance was assessed against the reference standard of invasive FFR ≤ 0.80.</p><p><strong>Results: </strong>The analysis included 210 diseased vessels from 133 patients-141 vessels from 84 patients in Centre 1 (development cohort) and 69 vessels from 49 patients in Centre 2 (validation cohort). Using the LASSO algorithm, nine predictive variables were selected from an initial set of 33 candidate features: heart rate, minimal lumen diameter (MLD), area stenosis, diameter stenosis ≥ 50%, lesion length/MLD⁴, necrotic core plaque volume, lesion entrance angle, remodeling index, and vessel-specific calcium score. In the validation cohort, the LASSO model demonstrated strong diagnostic performance at both vessel and patient levels. Per-vessel accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 93%, 86%, 98%, 96%, and 91%, respectively. Comparable performance was observed at the per-patient level, with corresponding values of 92%, 89%, 95%, 96%, and 88%, respectively. The area under the receiver operating characteristic curve (AUC) for the LASSO model at the vessel level was 0.950, significantly higher than that of CT-derived FFR (AUC = 0.857, P = 0.046) and diameter stenosis ≥ 50% (AUC = 0.641, P < 0.0001).</p><p><strong>Conclusions: </strong>A prediction model integrating coronary and plaque parameters outperformed existing CCTA-based methods in identifying hemodynamically significant coronary stenosis, offering improved accuracy for ischemia detection.</p>","PeriodicalId":54322,"journal":{"name":"Cardiovascular Engineering and Technology","volume":" ","pages":"175-187"},"PeriodicalIF":1.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147596213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Synthetic, Population-Based Virtual Patient Database Using a Digital Twin of the Cardiovascular System.","authors":"Richárd Wéber, Márta Viharos, Benjamin Csippa, Dániel Gyürki, György Paál","doi":"10.1007/s13239-026-00822-4","DOIUrl":"10.1007/s13239-026-00822-4","url":null,"abstract":"<p><strong>Purpose: </strong>The goal is to develop a cardiovascular virtual patient database (VPD) combining physiological and demographic data to provide the foundation for future applications in medical diagnostics, decision-making, credibility testing, and formal uncertainty analysis, and to enable its integration with three-dimensional (3D) hemodynamic models and to train neural networks.</p><p><strong>Methods: </strong>We generate an initial VPD by treating input parameters of a low-dimensional cardiovascular model as stochastic variables. Literature data and sensitivity analysis ensured physiological plausibility, while resampling improved physiological accuracy. Key physiological quantities are included such as systolic and diastolic aortic pressure, radial and carotid pressure, cardiac output, and diagnostic pulse wave velocities. Demographic factors (sex and age) are assigned based on their physiological impact. The open-source hemodynamic solver, first_blood, ensures accuracy and low computational time.</p><p><strong>Results: </strong>The initial VPD consists of 50,000 Virtual Patients; after resampling, 34,347 remain in the final VPD. The difference of diastolic and systolic aortic pressures between the VPD ( <math><mrow><mn>70.24</mn> <mo>±</mo> <mn>14.3</mn></mrow> </math> and <math><mrow><mn>116.8</mn> <mo>±</mo> <mn>16.11</mn></mrow> </math> mmHg) and the literature ( <math><mrow><mn>75.6</mn> <mo>±</mo> <mn>12.7</mn></mrow> </math> and <math><mrow><mn>113.0</mn> <mo>±</mo> <mn>11.2</mn></mrow> </math> mmHg) is low. The differences caused by the sex of the patient are reproduced well by the VPD: increased diastolic aortic pressure for males ( <math><mrow><mn>72.1</mn> <mo>±</mo> <mn>12.6</mn></mrow> </math> and <math><mrow><mn>68.5</mn> <mo>±</mo> <mn>15.4</mn></mrow> </math> for males and females respectively). The VPD also accurately includes higher pulse wave velocities with age, patients below year 30 have <math><mrow><mn>6.3</mn> <mo>±</mo> <mn>0.5</mn></mrow> </math> and above 70 have <math><mrow><mn>8.8</mn> <mo>±</mo> <mn>1.2</mn></mrow> </math> m/s with a linear increment in-between.</p><p><strong>Conclusions: </strong>The proposed methodology and first_blood solver effectively generate physiologically realistic virtual patient waveforms and demographic variability, providing a robust database for 3D cardiovascular simulations, machine-learning training datasets, and potential clinical decision support applications.</p>","PeriodicalId":54322,"journal":{"name":"Cardiovascular Engineering and Technology","volume":" ","pages":"188-205"},"PeriodicalIF":1.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13102896/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146183393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Image-Based Medical Navigation Systems for Cardiac Interventions: Recent Technological Advances.","authors":"Luís C N Barbosa, João L Vilaça, Siobhán Moane, Pedro Morais","doi":"10.1007/s13239-026-00830-4","DOIUrl":"10.1007/s13239-026-00830-4","url":null,"abstract":"<p><strong>Background: </strong>The application of catheter-based treatments for a growing range of structural heart diseases (SHD) has significantly increased over the past five years, driven by technological advances in medical navigation systems, particularly those based on medical imaging. Multimodal cardiac imaging plays a crucial role in these systems by combining complementary anatomical, morphological, and functional information, thereby increasing diagnostic accuracy and improving the effectiveness of cardiovascular interventions and clinical outcomes. However, multimodal imaging poses challenges, including intermodality misalignment and the need to determine optimal integration methods for data from different imaging modalities.</p><p><strong>Methods: </strong>This article reviews the state-of-the-art image-based medical navigation systems used in catheter-based cardiac procedures. The review covers the period from 2019 to 2023 and includes a total of 44 articles. The methodologies in these studies are grouped into six main categories: Image Enhancement and Tracking, Image Fusion and Reconstruction, 3D Modeling/Printing, Extended Reality, and Artificial Intelligence (AI).</p><p><strong>Results: </strong>Most studies involve multimodality imaging, combining or transferring information across different modalities. The review emphasizes that current multimodal imaging techniques enhance the accuracy of procedures such as left atrial appendage closure (LAAC), transcatheter aortic valve implantation (TAVI), and catheter ablation therapy. These techniques rely on combinations of imaging modalities such as fluoroscopy, ultrasound, and computed tomography (CT) to enable real-time guidance and precise navigation during minimally invasive interventions.</p><p><strong>Conclusion: </strong>By integrating data from multiple sources, these systems improve diagnostic reliability and procedural success, meeting the complex demands of SHD treatment. Despite advances, real-time surgical guidance remains a major challenge, underscoring the need for continued research in this area.</p>","PeriodicalId":54322,"journal":{"name":"Cardiovascular Engineering and Technology","volume":" ","pages":"99-118"},"PeriodicalIF":1.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13102890/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147582753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning Based Approach for Lossless ECG Compression.","authors":"Anumita Mitra, Palash Kundu, Rajarshi Gupta","doi":"10.1007/s13239-026-00821-5","DOIUrl":"10.1007/s13239-026-00821-5","url":null,"abstract":"<p><strong>Objective: </strong>Tele-monitoring is a useful platform for remote monitoring of cardiac patients, where compression plays a significant role in reducing the link burden and memory utilization of the source device. This paper describes a new approach for lossless ECG compression based on a deep-learning method via an adaptive autoregressive integrated moving average (ARIMA) model.</p><p><strong>Methods: </strong>Raw ECG signals were denoised and preprocessed to generate beat-cells for further processing. The ARIMA model uses the individual cardiac cycles to generate model parameters, which are then compressed. In this research, the optimal model hyperparameters were predicted by a deep autoencoder followed by a multilayer perceptron neural network (MLPNN) regressor combination. The predictor was tuned offline via particle swarm optimization (PSO), which produced the reference data for MLPNN tuning.</p><p><strong>Results: </strong>The technique uses 46 records of mitdb under PhysioNet, including 10 major abnormal beats: H, A, V, P, L, R, a, f, F and j. Because of the adaptive nature, compression quality is high with negligible loss. No deviations in the clinical features of the reconstructed beats are found. The mean CR and PRD% values were 41.51 and 0.209%, respectively, which are superior to those reported in published research on ECG compression.</p><p><strong>Conclusion: </strong>The proposed adaptive ECG compression model can be useful for real-time telemonitoring applications, efficient storage and transmission of streamlined data of critical patients under continuous monitoring.</p>","PeriodicalId":54322,"journal":{"name":"Cardiovascular Engineering and Technology","volume":" ","pages":"155-174"},"PeriodicalIF":1.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147318781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Chordae Rupture Alters Tricuspid Valve Leaflet Biomechanics.","authors":"Julia Clarin, Keyvan A Khoiy, Samuel D Salinas, Dipankar Biswas, Kourosh T Asgarian, Francis Loth, Rouzbeh Amini","doi":"10.1007/s13239-025-00815-9","DOIUrl":"10.1007/s13239-025-00815-9","url":null,"abstract":"<p><strong>Purpose: </strong>Tricuspid valve chordae tendineae rupture is a valvular lesion that is often overlooked, though is postulated to be more prevalent than currently known. We examined the hemodynamics and biomechanical response of the tricuspid valve leaflets following chordae rupture to understand how acute changes in the post-rupture mechanical environment may contribute to long-term remodeling responses.</p><p><strong>Methods: </strong>Porcine valve leaflet deformation was studied in an intact heart in an ex vivo setup using sonomicrometry techniques before and after chordae rupture, which was induced by severing a chordae bundle connected to the septal leaflet.</p><p><strong>Results: </strong>Following chordae rupture, pulmonary artery pressure dropped approximately 5 mmHg ( <math><mrow><mi>p</mi> <mo>=</mo> <mn>0.048</mn></mrow> </math> ), indicating that valvular regurgitation occurred immediately after rupture. Mean maximum principal stretch of the septal leaflet increased 12% after rupture ( <math><mrow><mi>p</mi> <mo>=</mo> <mn>0.006</mn></mrow> </math> ).</p><p><strong>Conclusion: </strong>The immediate changes in post-rupture septal leaflet stretches show that chordae tendineae rupture acutely alters the biomechanical environment of the tricuspid valve, which may result in chronic tissue remodeling responses. The tricuspid valve is one of the four valves in the heart. Rupture of supporting structures of the tricuspid valve leaflet, known as chordae tendineae, may be more common than previously thought. In this study, we used excised pig hearts to examine how chordae rupture affects valvular function. With our experimental beating heart system, we pumped fluid through the hearts under realistic conditions and measured changes in pressure and leaflet motion before and after chordae rupture. After rupture, we observed a change in pressures and leaflet motion, causing the valve to leak and become less efficient. These changes may influence how the valve functions over time.</p>","PeriodicalId":54322,"journal":{"name":"Cardiovascular Engineering and Technology","volume":" ","pages":"223-236"},"PeriodicalIF":1.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13102865/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145907408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust Design of Nonlinear Adaptive Hammerstein Filter Structure Using Evolutionary Algorithm: Real-Time Application to ECG Signals.","authors":"Shubham Yadav, Suman Kumar Saha, Rajib Kar","doi":"10.1007/s13239-025-00814-w","DOIUrl":"10.1007/s13239-025-00814-w","url":null,"abstract":"<p><strong>Objective: </strong>Electrocardiogram (ECG) signals are well-known non-stationary heart signals of lower strength. Due to their small amplitude, it attracts other biomedical artefacts from the surrounding. This research mainly focuses on removing artefacts from the electrocardiogram signals.</p><p><strong>Method: </strong>The work presented uses the recent metaheuristic techniques to design a nonlinear adaptive Hammerstein filter-based structure efficiently. Many powerful metaheuristic optimisation algorithms, such as particle swarm optimisation algorithm with constriction factor, flower pollination algorithm, marine predators' algorithm and growth optimiser, have been applied for the optimal design of adaptive Hammerstein filter-based structures. The proposed structure has been analysed for electrocardiogram with various noise signals such as muscle artefact, white Gaussian noise etc. RESULTS: Among the adopted-metaheuristic algorithms applied to adaptive Hammerstein filter-based structures, the growth optimiser-optimised adaptive Hammerstein filter-based structures performed better with improved signal-to-noise ratio and minimal mean squared error values. A digital signal processor kit is used to authenticate the simulation outcomes.</p><p><strong>Conclusion: </strong>The results (mean squared error: 3.698E-08 and signal-to-noise ratio improvement: 12 dB) obtained through the proposed technique ensure its supremacy compared to other state-of-the-art techniques.</p><p><strong>Significance: </strong>Hence, the proposed method can be utilised for electrocardiogram signal enhancement.</p>","PeriodicalId":54322,"journal":{"name":"Cardiovascular Engineering and Technology","volume":" ","pages":"140-154"},"PeriodicalIF":1.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145907443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Shape and Scale in Quantifying Aortic Morphology Evolution and Chronicity.","authors":"Joseph A Pugar, David Jiang, Junsung Kim, Luka Pocivavsek","doi":"10.1007/s13239-026-00827-z","DOIUrl":"https://doi.org/10.1007/s13239-026-00827-z","url":null,"abstract":"<p><strong>Purpose: </strong>Decision-making in thoracic aortic disease primarily relies on diameter thresholds that compress rich three-dimensional morphology into a single length scale. Intrinsic shape measures have been shown to complement diameter, but their utility depends critically on the observation scale imposed during quantification. We aim to identify scales at which a size-invariant shape descriptor, specifically the normalized fluctuation in total integrated Gaussian curvature <math> <mover><mrow><mi>δ</mi> <mi>K</mi></mrow> <mo>~</mo></mover> </math> , most robustly captures thoracic aortic disease state and to relate the tuned <math> <mover><mrow><mi>δ</mi> <mi>K</mi></mrow> <mo>~</mo></mover> </math> signal to clinically relevant markers of chronicity in aortic dissection.</p><p><strong>Methods: </strong>We construct a scale space of aortic surface representations from CTA images via a factorial sweep across smoothing intensity, mesh density, and mesh partitioning (coarse-graining) size. For each construction we compute <math> <mover><mrow><mi>δ</mi> <mi>K</mi></mrow> <mo>~</mo></mover> </math> , quantify signal robustness and predictive clinical value, and identify a \"stable zone\" of scales where performance stabilizes. We then model clinical progression using Gaussian Process Regression and relate <math> <mover><mrow><mi>δ</mi> <mi>K</mi></mrow> <mo>~</mo></mover> </math> to markers of aortic chronicity in dissection, testing whether <math> <mover><mrow><mi>δ</mi> <mi>K</mi></mrow> <mo>~</mo></mover> </math> behaves as a proxy for phase transitions in disease progression.</p><p><strong>Results: </strong>A reproducible stable zone emerges in which centimeter-scale partitioning consistently maximizes the informativeness of <math> <mover><mrow><mi>δ</mi> <mi>K</mi></mrow> <mo>~</mo></mover> </math> while remaining resilient to smoothing, meshing, and acquisition (CT resolution) variability. Within this zone, <math> <mover><mrow><mi>δ</mi> <mi>K</mi></mrow> <mo>~</mo></mover> </math> augments preoperative risk stratification and, when coupled with Gaussian Process Regression, delineates a nonstationary regime consistent with progressive remodeling in dissection, suggesting linkage to chronicity beyond diameter alone.</p><p><strong>Conclusion: </strong>Optimizing scale space yields a tuned, size-invariant shape signal that is both robust and clinically interpretable. The observed association between <math> <mover><mrow><mi>δ</mi> <mi>K</mi></mrow> <mo>~</mo></mover> </math> and chronicity supports its use as a complementary marker to diameter and motivates prospective validation of shape-aware, curvature-based decision tools.</p>","PeriodicalId":54322,"journal":{"name":"Cardiovascular Engineering and Technology","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147582760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interpretable Machine Learning for Feature-Based Classification of Platelet Activation in Rotary Blood Pumps.","authors":"Christopher Blum, Michael Neidlin","doi":"10.1007/s13239-026-00828-y","DOIUrl":"https://doi.org/10.1007/s13239-026-00828-y","url":null,"abstract":"<p><strong>Background: </strong>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 feature-based supervised machine learning framework for spatial assessment of activation-based thrombogenic risk based directly on computational fluid dynamics-derived flow features.</p><p><strong>Methods: </strong>A logistic regression 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 platelet-activation-based thrombosis model for two representative scenarios.</p><p><strong>Results: </strong>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 indicate that interpretable machine learning can link local flow features to activation-based thrombogenic risk while remaining computationally efficient and mechanistically transparent. The low computational cost enables rapid thrombogenicity screening without repeated or costly physics-based simulations.</p><p><strong>Conclusions: </strong>The proposed framework complements physics-based thrombosis and platelet-activation modeling and provides a methodological basis for integrating interpretable machine learning into CFD-driven thrombogenicity analysis and device design workflows.</p>","PeriodicalId":54322,"journal":{"name":"Cardiovascular Engineering and Technology","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147582716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mechanical Behavior of Valve Stents in Diverse Bicuspid Aortic Valves: Influence of Structural Design and Annular Eccentricity.","authors":"Xiang Shen, Huilin Yao, Zewen He, Yizhe Wang, Yue Xu, Qiang Liu, Jiahao Chen, Jianwei Gao, Lei Wang, Yan Wang, Hongyu Liang","doi":"10.1007/s13239-026-00829-x","DOIUrl":"https://doi.org/10.1007/s13239-026-00829-x","url":null,"abstract":"<p><strong>Objective: </strong>To evaluate the expansion mechanics of transcatheter heart valves in bicuspid aortic valve (BAV)anatomies, quantifying the infl uence of stent design and annular eccentricity.</p><p><strong>Methods: </strong>Finite element simulations were performed to compare two self-expanding (E, V) and one balloon-expandable (S) stent across four BAV morphologies (Type 0, LR, LN, RN) with varying annular eccentricity (0.2-0.4).Key metrics included post-implant stent ellipticity, stent stress, and aortic wall stress.</p><p><strong>Results: </strong>The eccentricity of the valve stent after implantation increased with the increase of the eccentricity of theBAV valve ring. Among the four BAV types, the balloon-expandable stent (S-stent) showed higher stent stress andaortic wall stress than the self-expanding stent, but its expansion uniformity was the best. The analysis reveals afundamental trade-off between achieving optimal expansion uniformity and minimizing implant-induced stress.</p><p><strong>Conclusion: </strong>This study provides a biomechanical framework for personalized stent selection in BAV-TAVR, indicatingthat the choice of the optimal device depends on the priority given to specifi c clinical outcomes (such as sealingvs.long-term durability).</p>","PeriodicalId":54322,"journal":{"name":"Cardiovascular Engineering and Technology","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147534444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}