International Journal for Numerical Methods in Biomedical Engineering最新文献

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":50349,"journal":{"name":"International Journal for Numerical Methods in Biomedical Engineering","volume":"42 4","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148084698","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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":50349,"journal":{"name":"International Journal for Numerical Methods in Biomedical Engineering","volume":"42 4","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148082199","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":"Osteoporosis-Induced Biomechanical Alterations in the Sacroiliac Joint","authors":"Wei Fan,&nbsp;Zhi-Xu Chen,&nbsp;Sen Yang,&nbsp;Sheng-Nan Liu,&nbsp;Zhen-Ya Sun,&nbsp;Jie Chen","doi":"10.1002/cnm.70171","DOIUrl":"10.1002/cnm.70171","url":null,"abstract":"<div>\u0000 \u0000 <p>Osteoporosis is a prevalent systemic disease primarily affecting the skeletal system, with the spine being one of the most commonly affected areas. Numerous studies have demonstrated detrimental biomechanical effects of osteoporosis on the lumbar spine. However, its influence on adjacent SIJ remains poorly understood. This study aimed to determine how osteoporosis alters SIJ biomechanics under physiological and vibrational load. A validated, 3D finite element model of the normal lumbopelvic segment (L1–pelvis) was modified to simulate osteoporosis by decreasing bone mechanical properties. Biomechanical responses within the SIJs to both static loading (flexion, extension, lateral bending, rotation) and vibration loading (cyclic axial compression) were analyzed and compared between the normal and osteoporotic conditions. Static analysis revealed that osteoporosis significantly increased SIJ range of motion (ROM) by 20.6%–52.7% and elevated maximum von Mises stress by 30.8%–90.3% compared to the normal condition. Also, forced vibration analysis revealed a 35%–36% increase in stress amplitudes in the osteoporotic model. These alterations correlated with reduced bone stiffness, suggesting compromised joint stability. These findings demonstrate that osteoporosis adversely affects SIJ biomechanics by increasing motion and internal stress, thereby potentially elevating the risks of SIJ instability, degeneration, and subsequent joint dysfunction and pain. This study provides novel insights into the overlooked role of SIJ pathology in osteoporotic patients, emphasizing the need for targeted diagnostic and therapeutic strategies.</p>\u0000 </div>","PeriodicalId":50349,"journal":{"name":"International Journal for Numerical Methods in Biomedical Engineering","volume":"42 4","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147640436","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":"Red Blood Cell Membrane Mechanics Using Discrete Exterior Calculus (DEC) and Optimization","authors":"Keith C. Afas,&nbsp;Daniel Goldman","doi":"10.1002/cnm.70154","DOIUrl":"10.1002/cnm.70154","url":null,"abstract":"<p>In this study, a novel algorithm for computing red blood cell (RBC) geometry was developed as the first step of a quantitative model for RBC-ATP release. This model relied on the developing coordinate-invariant computational framework of discrete exterior calculus (DEC). The algorithm for the first time in literature was formulated in an implicit manner, utilized a Lie-derivative based vertex drift contribution to ensure the mesh was well-behaved throughout deformation, and was able to obtain RBC equilibrium geometries in an efficient manner. This algorithm was shown to be highly stable, quantified through tracking the RBC membrane energy. Equilibrium geometries were shown to agree with literature in in vivo observations, and qualitatively reproduced phenomena seen in in vivo experiments where RBCs are subjected to solutions of varying osmolarity. This DEC algorithm will be applied in future work to fluid–structure interactions of RBCs, and has application to a multitude of open cell biology problems.</p>","PeriodicalId":50349,"journal":{"name":"International Journal for Numerical Methods in Biomedical Engineering","volume":"42 4","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13062631/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147640451","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":"A Deep Learning Model for the Identification of Active Contraction Properties of the Myocardium Using Limited Clinical Metrics","authors":"Igor A. P. Nobrega,&nbsp;Wenbin Mao","doi":"10.1002/cnm.70170","DOIUrl":"10.1002/cnm.70170","url":null,"abstract":"<div>\u0000 \u0000 <p>With the increasing prevalence of cardiovascular diseases globally and limitations in current constitutive models for myocardium mechanics, this study aims to develop a deep learning (DL) model geared toward patient-specific evaluation of left ventricular (LV) myocardium behavior. The primary aim is to bridge the gap between complex constitutive models and their practical clinical applications. We employed DL techniques to construct a model that operates under synthetic clinical metrics and pressure–volume loop data to predict the behavior of the LV myocardium for a whole cardiac cycle. This model generates a waveform of the active contraction parameter and estimates fiber angles at the endocardium and epicardium within a single forward pass. The outputs of our model can be used to infer the unknown parameters in finite element analysis of the myocardium. The DL model demonstrates consistency across various synthetic clinical data scenarios. While the model was tested using both idealized and a single patient-derived geometry, it is important to note that the results have not yet been validated against real clinical data. Overall, the developed DL model offers an innovative approach to understanding and predicting myocardium mechanics in patient-specific clinical settings. By reducing the barrier between the clinical applications and theoretical constitutive models, the proposed method has the potential to support more targeted cardiovascular interventions and diagnostics.</p>\u0000 </div>","PeriodicalId":50349,"journal":{"name":"International Journal for Numerical Methods in Biomedical Engineering","volume":"42 4","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147619239","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":"Finite Element and Computational Fluid Dynamics Analysis of a Biodegradable Implant for Large Femoral Bone Defects.","authors":"Sina Taghipour, Farid Vakili-Tahami","doi":"10.1002/cnm.70175","DOIUrl":"10.1002/cnm.70175","url":null,"abstract":"<p><p>The treatment of large bone defects represents a major clinical challenge. Biodegradable metallic 3D-printed scaffolds are promising for orthopedic applications due to their ability to provide sufficient mechanical support while gradually degrading and supporting bone healing. This study investigates the mechanical and fluid transport properties of Fe-10Mn-1Pd scaffolds for femoral fracture repair. The performance of 10 different lattice structures was first evaluated using finite element modeling (FEM) and validated by experimental compression testing, identifying the parallel face-centered cubic (PFCC) scaffold as optimal due to its superior stiffness-to-weight ratio. The selected PFCC scaffold was then geometrically optimized, integrated with a biodegradable ZK60 magnesium plate-screw system, and analyzed under walking cycle loading using FEM and computational fluid dynamics (CFD), which demonstrated uniform stress distribution, minimal stress shielding, and an average wall shear stress of 0.7 Pa, favorable for osteogenesis and fluid transport. These findings confirm that the PFCC Fe-10Mn-1Pd scaffold, combined with a magnesium-based fixation mechanism, provides both mechanical support and fluid transmission.</p>","PeriodicalId":50349,"journal":{"name":"International Journal for Numerical Methods in Biomedical Engineering","volume":"42 4","pages":"e70175"},"PeriodicalIF":2.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147787886","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":"Finite Element Simulation of the Three-Dimensional Residual Stress Field Using a Patient-Specific Aortic Model.","authors":"Ming Zhang, Zongyao Li, Bei Zhang, Zijuan Wu, Yanzhang Dong, Dongxiong Wang, Haofei Liu","doi":"10.1002/cnm.70173","DOIUrl":"10.1002/cnm.70173","url":null,"abstract":"<p><p>Residual stress (RS) in the aorta, key to the in vivo stress distribution, plays a significant role in aortic physiology and pathology. However, quantification of the three-dimensional (3D) residual stress field is challenging because its distribution is patient-specific and heterogeneous. In this study, a stress-driven anisotropic growth model is proposed to qualitatively predict the 3D residual stress field in a patient-specific human aortic wall model. The 3D residual stress field is predicted under the condition that the in vivo stress state is achieved. The in vivo stress is adjusted using the transmural stress heterogeneity. Virtual opening angle tests demonstrate that the simulated opening angles vary between axial positions, and the opening angles peak at the aortic arch. Lateral bending of the axial strips is also simulated. The results of both tests show good agreement with the experimental results in the literature. A parametric study reveals that both the opening and lateral bending angles vary with the in vivo transmural stress heterogeneity. The proposed method can be applied to predict patient-specific 3D heterogeneous residual stress and in vivo stress fields to explore the pathogenesis and treatment of aortic disease.</p>","PeriodicalId":50349,"journal":{"name":"International Journal for Numerical Methods in Biomedical Engineering","volume":"42 4","pages":"e70173"},"PeriodicalIF":2.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147724525","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":"Implant Abutment Screw Loosening: Biomechanical Analysis of Bruxism-Induced Mesiodistal Loads With FEA and Their Effect on Shear Stress.","authors":"Zeynep Canan Karip, Kadir Gök","doi":"10.1002/cnm.70174","DOIUrl":"10.1002/cnm.70174","url":null,"abstract":"<p><p>Abutment screw loosening in dental implant systems is one of the most common causes of long-term clinical failure. This study investigated the biomechanical effects of bruxism-induced mesiodistal loads on shear stress and screw loosening tendency at the implant-abutment connection using finite element analysis. In this study, a titanium alloy (Ti6Al4V) implant-abutment-screw system was three-dimensionally modeled, and mesiodistal loads (23.4, 128, and 234 N), representing mesiodistal forces, were applied. The friction coefficient between the contact surfaces was defined, and the screw preload and load transfer mechanism were analyzed. The results showed that shear stress in the connection area increased significantly with increasing mesiodistal loads, and that loads applied in a mesiodistal direction caused microrotation in the screw connection, increasing the risk of loosening. These findings emphasize the need to optimize load, screw preload, and connection geometry in implant design for individuals with bruxism and provide a scientific basis for material and design improvements to prevent screw loosening clinically.</p>","PeriodicalId":50349,"journal":{"name":"International Journal for Numerical Methods in Biomedical Engineering","volume":"42 4","pages":"e70174"},"PeriodicalIF":2.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147730473","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":"Verification of a Fluid–Structure Interaction Model for Aortic Stenosis Through Comparison With In Vitro Experiments","authors":"Sabine Verstraeten,&nbsp;Roel Meiburg,&nbsp;Koen Janssens,&nbsp;Martijn Hoeijmakers,&nbsp;Nils Karajan,&nbsp;Marcel Rutten,&nbsp;Marcel van't Veer,&nbsp;Pim Tonino,&nbsp;Frans van de Vosse,&nbsp;Wouter Huberts","doi":"10.1002/cnm.70169","DOIUrl":"10.1002/cnm.70169","url":null,"abstract":"<p>Aortic stenosis (AS) severity is typically assessed by measuring pressure drop across the aortic valve in rest. However, this flow-dependent metric is influenced by patients' cardiac function, complicating clinical decision-making on valve replacement. Moreover, assessment during rest does not reflect valvular dynamics under higher flow rates (e.g., during exercise), and may underestimate severity in moderate AS patients. Patient-specific Fluid–Structure Interaction (FSI) modelling offers a promising, non-invasive method to simulate valve dynamics under varying conditions, independent of pressure exerted by the left ventricle. Therefore, this study aimed to experimentally verify an aortic stenosis FSI model using a patient-specific aortic valve geometry, including calcifications, across flow conditions ranging from rest to exercise. To achieve this goal, in vitro experiments were conducted using a mock-loop circulatory system with patient-specific silicone rubber valve models, both calcified and non-calcified. These experiments were replicated in FSI simulations. Overall, good agreement was observed between simulated and experimental results for the non-calcified valve in terms of mean transvalvular flow (5% error on average) and aortic valve area (AVA) (10% error on average). Discrepancies were more pronounced in the calcified valve due to added complexity and uncertainty introduced by calcifications (8% and 7% error on average for mean transvalvular flow and AVA, respectively). For clinical implementation, two key challenges remain: (1) developing efficient and reliable methods to estimate valve leaflet material properties and pre-stress, and (2) verifying the model against a broader range of in vitro data, followed by validating it against real, clinical data. Despite the remaining challenges, this study demonstrated the feasibility of using FSI models as a complementary, non-invasive tool to assess AS severity and support clinical decision-making on valve replacement.</p>","PeriodicalId":50349,"journal":{"name":"International Journal for Numerical Methods in Biomedical Engineering","volume":"42 4","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13039782/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147596259","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":"Biomechanical Analysis of Stresses and Strains of the Healthy Aortic Valve and Its Congenital Variants Throughout the Cardiac Cycle: A Finite Element Approach","authors":"Aicha Boualiane,&nbsp;Lotfi Hamza Cherif","doi":"10.1002/cnm.70168","DOIUrl":"10.1002/cnm.70168","url":null,"abstract":"<div>\u0000 \u0000 <p>To fully understand valve function and evaluate valve dynamics in patients with valvular diseases, it is crucial to model the biomechanical behavior of the valve. The primary objective of this study is to develop an innovative, patient-specific computational framework to assess stress and strain deformation throughout the cardiac cycle using finite element analysis. Three aortic valve models—tricuspid (TAV), bicuspid type 0 (BAV Type 0), bicuspid type 1 (BAV Type 1), and quadricuspid (QAV)—were created from transesophageal echocardiography images using a combination of MATLAB and Blender functionalities. A dynamic stress and strain analysis, assuming a combination of linear elastic and nonlinear hyperelastic material properties, was employed for the simulation (FEATool). The valves were subjected to a set of stresses as initial conditions, with a surface-dependent variable pressure profile as a boundary condition. Two numerical solvers, linear (MUMPS) and nonlinear (FEniCS), were employed to ensure convergence of the results. Mechanically, the models reveal significant distinctions, primarily driven by differences in morphology, as demonstrated by the simulation performed. The BAV (Type 0 and Type 1) and QAV models, which have an asymmetric structure, provided greater concentrations of stress distribution as well as localized deformations. Our approach allows us to quantify the impact of valvular morphology on stress and strain distribution. These findings enhance our understanding of the biomechanical mechanisms that may impact disease progression in valvular abnormalities and inform personalized treatment approaches.</p>\u0000 </div>","PeriodicalId":50349,"journal":{"name":"International Journal for Numerical Methods in Biomedical Engineering","volume":"42 4","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147583042","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}