Computer Methods in Biomechanics and Biomedical Engineering最新文献

{"title":"Predictive stress analysis in simplified spinal disc model using physics-informed neural networks.","authors":"Kwang Hyeon Kim, Hae-Won Koo, Byung-Jou Lee","doi":"10.1080/10255842.2025.2471504","DOIUrl":"https://doi.org/10.1080/10255842.2025.2471504","url":null,"abstract":"<p><p>This study develops a physics-informed neural network (PINN) model to predict stress distribution in a simplified spinal disc structure. The model incorporates 3D spatial inputs and enforces equilibrium conditions through a custom loss function. Trained on synthetic elasticity-based data, it achieves an MAE of 0.026 and an R² of 74.6%. Stress patterns under various loading conditions were visualized, with peak stress occurring at <i>z</i> = 1 under top compression. Results demonstrate PINNs' potential for biomechanical modeling, improving predictive accuracy in spinal biomechanics and informing clinical interventions.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-13"},"PeriodicalIF":1.7,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143525053","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":"Dynamical analysis of hepatitis B virus through the stochastic and the deterministic model.","authors":"Nabeela Anwar, Iftikhar Ahmad, Hijab Javaid, Adiqa Kausar Kiani, Muhammad Shoaib, Muhammad Asif Zahoor Raja","doi":"10.1080/10255842.2025.2470798","DOIUrl":"https://doi.org/10.1080/10255842.2025.2470798","url":null,"abstract":"<p><p>In the current work, the deterministic hepatitis B virus epidemic (DHBVE) model and the stochastic hepatitis B virus epidemic (SHBVE) model are two nonlinear mathematical models that serve as the framework to illustrate and predict the dynamic virus behavior of hepatitis B. We employ an approximation based on the outcomes of the deterministic model to solve the stochastic model numerically. Euler-Maruyama method is employed to investigate the SHBVE model, whereas an explicit Runge-Kutta method is exploited to calculate the solution to the DHBVE model. Finally, comparisons between the DHBVE and SHBVE models' frameworks are presented.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-17"},"PeriodicalIF":1.7,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143528000","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 characteristics of braided composite stents for carotid artery stenosis using finite element method.","authors":"Gaiping Zhao, Zhengnan Zhang, Eryun Chen, Fenghua Ding, Ruosen Yuan, Chengli Song, Wentao Yan, Kunneng Wu, Jie Wu","doi":"10.1080/10255842.2025.2471501","DOIUrl":"https://doi.org/10.1080/10255842.2025.2471501","url":null,"abstract":"<p><p>Self-expandable braided stents are widely used for carotid artery stenosis due to flexibility. Finite element (FE) models of braided composite stents with different proportions of NiTi and Mg-2.1Nd-0.2Zn-0.5Zr (JDBM) alloy (24NiTi (24:0), 20NiTi (20:4), 16NiTi (16:8), 12NiTi (12:12), 8NiTi (8:16), 4NiTi (4:20)) were constructed to analyze their radial compression strength and longitudinal bending flexibility. The 4NiTi stent showed the highest radial support (16.39N) and decreased bending strength by 7.72%. These results suggest that replacing more NiTi with JDBM wires enhances the stent's radial strength and reduces bending stiffness, providing new insights into stent design.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-9"},"PeriodicalIF":1.7,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143517263","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":"Numerical investigation of the flow induced by a transcatheter intra-aortic entrainment pump.","authors":"Yeojin Park, Osman Aycan, Lyes Kadem","doi":"10.1080/10255842.2025.2471517","DOIUrl":"https://doi.org/10.1080/10255842.2025.2471517","url":null,"abstract":"<p><p>This study evaluates the fluid dynamics inside and outside transcatheter blood pump positioned in the aorta. We focus on the pump's impact on blood component damage and arterial wall stress. CFD simulations were performed for rotational speeds ranging from 6000 to 15000 rpm, with a blood flow rate of 1.6 L/min. Results show that significant blood damage may occur at speeds as low as 12000 rpm, and the pump's outflow jet induces elevated wall shear stress, potentially leading to arterial aneurysms. These findings suggest the need for further design improvements to reduce risks when used in prolonged or transplant-related applications.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-14"},"PeriodicalIF":1.7,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143517265","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":"Biomechanical modeling and assessment of patient positioning to facilitate spinal deformity instrumentation.","authors":"Xiaoyu Wang, Guillaume Imbleau-Chagnon, Christiane Caouette, A Noelle Larson, Carl-Eric Aubin","doi":"10.1080/10255842.2025.2470796","DOIUrl":"https://doi.org/10.1080/10255842.2025.2470796","url":null,"abstract":"<p><p>Finite element models (FEM) were built based on clinical documentation of five AIS surgical cases to simulate patient positioning and spinal instrumentation. Various patient positioning and instrumentation configurations were simulated, and the associated corrections and screw pull-out forces were analyzed. Patient prone-positioning resulted in Cobb angle reduction of over 5°. Vertical, caudal, and cephalad displacement of thoracic cushions had significant impact on thoracic kyphosis. Pelvic rotation through lower-limb extension/flexion had significant effect on lumbar lordosis. The validated FEM enabled simulations of patient positioning and spinal instrumentation. Patient positioning configurations had significant effects on deformity correction and screw pull-out forces.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-10"},"PeriodicalIF":1.7,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143505761","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":"Prognostic model for cervical cancer based on apoptosis-related genes.","authors":"Lin Zhang, Shunjie Zheng, Pan Chen","doi":"10.1080/10255842.2025.2468324","DOIUrl":"https://doi.org/10.1080/10255842.2025.2468324","url":null,"abstract":"<p><p>This study attempts to develop a novel apoptosis-related predictive model for cervical cancer. Differentially expressed apoptosis-related genes were identified using TCGA, GEO, and MSigDB databases. A 13-gene prognostic model was constructed using multiple regression analyses. The low-risk group exhibited low tumor purity and high ESTIMATE and immune scores. Most of the immune checkpoints in the low-risk group were expressed at higher levels than those in the high-risk group. The low-risk group also had relatively more infiltrating immune cells. An independent prognostic model pertaining to cell apoptosis has been built by this work, which performs well in prediction.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-17"},"PeriodicalIF":1.7,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143505837","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":"Design of fish-scale microstructured stents and their biomechanical effects on cerebral aneurysm.","authors":"Xuanze Fan, Yanru Xue, Boya Liu, Aohua Zhang, Lijuan Song, Cungen Ma, Qingli Zheng, Yongwang Zhao, Meng Zhang, Xiaogang Wu, Dong Ma, Yonghong Wang","doi":"10.1080/10255842.2025.2465343","DOIUrl":"https://doi.org/10.1080/10255842.2025.2465343","url":null,"abstract":"<p><p>The addition of microstructures to the inner surface of the stent reduces resistance and inhibits the phenomenon of blood adhesion. In this study, the design of a fish-scale microstructured vascular stent was proposed based on bionics, and its main design parameters were optimized using the finite element method. In addition, the hemodynamic effects of a standard stent and a fish-scale microstructured stent on an ideal cerebral aneurysm were comparatively analyzed. The results showed that the fish-scale microstructured stent significantly accelerated intraluminal blood flow velocity by 11.6% compared to the standard stent. In addition, the fish-scale microstructured stent was able to reduce blood flow into the aneurysm lumen by 28.6%.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-21"},"PeriodicalIF":1.7,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143473217","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":"The accuracy of the 3D-printed navigation template for the location of tibial tunnel in posterior cruciate ligament reconstruction: an in-vitro experimental study.","authors":"Yuanjun Teng, Kangrui Zhang, Nian Tan, Yannan Wang, Wenduo Niu, Jia Jiang, Wenming Chen, Bin Yang, Xin Ma","doi":"10.1080/10255842.2025.2465348","DOIUrl":"https://doi.org/10.1080/10255842.2025.2465348","url":null,"abstract":"<p><p>Ten porcine tibiae were scanned by computed tomography (CT) and the three‑dimensional (3D)-printed navigation template for posterior cruciate ligament (PCL) reconstruction was designed using the Rhinoceros software. The outcomes of the control group and experimental group were obtained from the preoperative design and the navigation template, respectively. This paper focuses on evaluating the accuracy of the 3D-printed navigation template used to assist the anatomical location of tibial tunnel in PCL reconstruction in an in-vitro experimental study.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-9"},"PeriodicalIF":1.7,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460551","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":"Comprehensive analysis of prefrontal cortex-directional rhythms categorization for rehabilitation.","authors":"Anna Latha M, Ramesh R","doi":"10.1080/10255842.2025.2467460","DOIUrl":"https://doi.org/10.1080/10255842.2025.2467460","url":null,"abstract":"<p><p>Prefrontal Cortex-Directional Rhythms (PFC-DR) classification plays a significant role in Brain-Computer Interface (BCI) research since it is crucial for the effective rehabilitation of injured voluntary movements. The primary aims of this study are to conduct a thorough examination of traditional classification techniques, while emphasizing the significance of radial basis functions within support vector machine (RBF-SVM) based approaches in the context of BCI systems. Consequently, in contrast to existing machine learning-based approaches, this generalized RBF-SVM classifier effectively identified observed data with an overall 96.91% accuracy validated with a 10-fold repeated random train test split cross validation technique using confusion matrix analysis.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-12"},"PeriodicalIF":1.7,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460546","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":"EBHOA-EMobileNetV2: a hybrid system based on efficient feature selection and classification for cardiovascular disease diagnosis.","authors":"Manjula Mandava, Surendra Reddy Vinta","doi":"10.1080/10255842.2025.2466081","DOIUrl":"https://doi.org/10.1080/10255842.2025.2466081","url":null,"abstract":"<p><p>The accurate prediction of cardiovascular disease (CVD) or heart disease is an essential and challenging task to treat a patient efficiently before occurring a heart attack. Many deep learning and machine learning frameworks have been developed recently to predict cardiovascular disease in intelligent healthcare. However, a lack of data-recognized and appropriate prediction methodologies meant that most existing strategies failed to improve cardiovascular disease prediction accuracy. This paper presents an intelligent healthcare framework based on a deep learning model to detect cardiovascular heart disease, motivated by present issues. Initially, the proposed system compiles data on heart disease from multiple publicly accessible data sources. To improve the quality of the dataset, effective pre-processing techniques are used including (i) the interquartile range (IQR) method used to identify and eliminate outliers; (ii) the data standardization technique used to handle missing values; (iii) and the 'K-Means SMOTE' oversampling method is used to address the issue of class imbalance. Using the Enhanced Binary Grasshopper Optimization Algorithm (EBHOA), the dataset's appropriate features are chosen. Finally, the presence and absence of CVD are predicted using the Enhanced MobileNetV2 (EMobileNetV2) model. Training and evaluation of the proposed approach were conducted using the UCI Heart Disease and Framingham Heart Study datasets. We obtained excellent results by comparing the results with the most recent methods. The proposed approach beats the current approaches concerning performance evaluation metrics, according to experimental results. For the UCI Heart Disease dataset, the proposed research achieves a higher accuracy of 98.78%, precision of 99%, recall of 99% and F1 score of 99%. For the Framingham dataset, the proposed research achieves a higher accuracy of 99.39%, precision of 99.50%, recall of 99.50%, and F1 score of 99%. The proposed deep learning-based classification model combined with an effective feature selection technique yielded the best results. This innovative method has the potential to enhance the accuracy and consistency of heart disease prediction, which would be advantageous for clinical practice and patient care.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-23"},"PeriodicalIF":1.7,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460548","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}