Medical Engineering & Physics最新文献

{"title":"A user-defined element for simulating hydrogel injection into trabecular bone: Numerical simulations and experimental validation","authors":"Georgios F. Samaras ,&nbsp;Vincent Dischl ,&nbsp;Anita Fung ,&nbsp;Vincent A. Stadelmann ,&nbsp;Ulrike Kettenberger ,&nbsp;Stephen J. Ferguson ,&nbsp;Benedikt Helgason","doi":"10.1016/j.medengphy.2025.104411","DOIUrl":"10.1016/j.medengphy.2025.104411","url":null,"abstract":"<div><div>In this study, we present a comprehensive numerical model to simulate the injection of hydrogel into femurs. The model is designed to capture the complex interactions between the hydrogel rheological properties and the biomechanical environment of the femur. The coupled mechanical-flow formulation, based on the Theory of Porous Media, is implemented in an open source Abaqus UEL subroutine, where displacements, pressure and saturation are the unknowns. The rheological properties of the hydrogel were calibrated against experimental augmentations in three femurs and the calibrated model was then applied to three different femurs where the hydrogel patterns were compared to experimental data. Furthermore, the simulations demonstrated the effect of injection flow rate and heterogeneous permeability on the hydrogel patterns and quantified the trabecular matrix's solid strains developed during the injection process. The simulations captured well the volume distribution with an average dice coefficient of 0.75 for the three tested specimens. In addition, the calculated solid strains were below the tensile yield limit for the tested flow rate range. A description of the constitutive equations and the implementation into an Abaqus user element subroutine is provided. Overall, our modeling methodology provides a computational tool that can be used to more accurately model bone augmentation and furthermore plan more safely the treatment of osteoporotic patients.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"145 ","pages":"Article 104411"},"PeriodicalIF":2.3,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144841540","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":"Fast geometric deep learning for intraoperative soft tissue deformation estimation: Towards real-time AR guidance in liver surgery","authors":"Zixuan Zhai ,&nbsp;Enpeng Wang ,&nbsp;Xiaojun Chen","doi":"10.1016/j.medengphy.2025.104409","DOIUrl":"10.1016/j.medengphy.2025.104409","url":null,"abstract":"<div><div>The real-time computation of the intraoperative spatial positioning of soft tissues, particularly those not visible within the body, such as blood vessels, is crucial for augmented reality navigation systems. Conventional biomechanical models face challenges in real-time computation and the acquisition of boundary conditions. A novel deep learning framework is proposed, integrating an optimized PointNet++ architecture for modelling liver and vascular deformation. The framework utilizes multi-scale feature extraction, lightweight self-attention mechanisms, and residual feature propagation to predict vascular displacement fields and normal vectors. A hybrid loss function that integrates Chamfer distance and MSE losses improves geometric consistency and deformation accuracy. The proposed approach, utilizing finite element method (FEM)-simulated datasets of liver stretching procedures, exhibits enhanced performance with root mean square errors (RMSE) of 2.78 ± 0.69 mm for hepatic veins and 1.81 ± 0.74 mm for portal veins. This method surpasses conventional techniques by 37.5% in accuracy and reduces inference time to 0.25 seconds. The optimized network exhibits a computation speed that is 83.9% faster than leading non-rigid registration algorithms. Subsequent tumour localization experiments demonstrate a targeting accuracy of 3.2 mm via vascular topology analysis, confirming clinical relevance. This research develops an effective framework for predicting deformation in real-time, providing a significant advancement for navigation in AR-guided hepatobiliary surgery.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"145 ","pages":"Article 104409"},"PeriodicalIF":2.3,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144809762","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":"Comparative analysis of Electrospun PLA fibers incorporating bioactive glass nanoparticles: morphological, biological, and osteogenic properties for bone regeneration","authors":"Brunna da Silva Nobrega Souza ,&nbsp;Lilian de Siqueira ,&nbsp;Marina Santos Fernandes ,&nbsp;Joyce Rodrigues de Souza ,&nbsp;Elisa Camargo Kukulka ,&nbsp;Letícia Adrielly Dias Grisante ,&nbsp;Tiago Moreira Bastos Campos ,&nbsp;Luana Marotta Reis de Vasconcellos ,&nbsp;Alexandre Luiz Souto Borges","doi":"10.1016/j.medengphy.2025.104410","DOIUrl":"10.1016/j.medengphy.2025.104410","url":null,"abstract":"<div><div>Polylactic acid (PLA) is widely studied for bone repair due to its biodegradability, biocompatibility, and bioresorbability. However, its limited bioactivity and hydrophobic surface hinder optimal cell interaction and integration. Incorporating bioactive glass (BG) particles into PLA scaffolds via electrospinning and electrospray techniques has emerged as a promising strategy to improve biological performance. This study aimed to fabricate and characterize PLA scaffolds, both with incorporated and surface-coated BG, and to assess their osteogenic potential for tissue engineering applications. Scaffold morphology was evaluated by scanning electron microscopy, and biological performance was assessed through in vitro assays using mesenchymal stem cells derived from Wistar rat bone marrow. Cell viability, total protein content, alkaline phosphatase (ALP) activity, and mineralized nodule formation were analyzed. The scaffolds displayed porous, interconnected structures with fiber diameters influenced by BG incorporation method. All groups demonstrated cytocompatibility, while scaffolds containing BG both incorporated and sprayed—showed significantly higher ALP activity, suggesting enhanced osteogenic differentiation. Mineralization nodules further confirmed the induction of osteogenesis. These findings highlight the potential of PLA/BG composite scaffolds, especially when functionalized via combined electrospinning and electrospray methods, as a promising platform for bone tissue engineering.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"145 ","pages":"Article 104410"},"PeriodicalIF":2.3,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144908034","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":"Reduced model aided fluid-structure interaction design framework for shunt systems","authors":"Elizabeth Hayman ,&nbsp;Van Dung Nguyen ,&nbsp;Ian S. McFarlane ,&nbsp;Juliette Pech ,&nbsp;Jayaratnam Jayamohan ,&nbsp;José-Maria Peña Sánchez ,&nbsp;Sarah Waters ,&nbsp;Antoine Jerusalem","doi":"10.1016/j.medengphy.2025.104403","DOIUrl":"10.1016/j.medengphy.2025.104403","url":null,"abstract":"<div><div>Traditionally, clinical devices are designed, tested and improved through lengthy and expensive laboratory experiments and clinical trials <span><span>[1]</span></span>. More recently, computational methods have allowed for rapid testing, speeding up the design process and enabling far more complete searches of design space. While computational models cannot fully capture the complexities of biological systems, they provide valuable insights into crucial underlying mechanisms, such as the effects of fluid-structure interactions (FSIs). In this paper we present a modular, partitioned, computational FSI pipeline whereby 2D reduced order models guide the 3D design of the problem of interest. This framework is applied to the problem of hydrocephalus shunt occlusion. Hydrocephalus is a medical condition characterised by an excess of cerebrospinal fluid (CSF) in the brain, and is commonly treated with the insertion of a shunt system. This system includes a ventricular catheter component – a hollow tube with inlet holes arranged in the tube wall close to the closed tip – which is positioned in the lateral ventricles of the brain. Despite recent improvements in the catheter material, this treatment still has high failure rates, most often due to the blockage of the catheter by the Choroid Plexus (ChP) tissue. We use an idealised FSI model to compare existing catheter designs by considering the deformation of the ChP under CSF flow in the ventricle environment in an hydrocephalus scenario. To the best of our knowledge, this is the first computational framework to directly incorporate the deformation of the ChP to discriminate between catheter designs. The faster 2D model is used in a comprehensive parameter sweep of the catheter design domain, and motivates a new design, then confirmed to be an improvement when tested in the full 3D domain. This approach demonstrates the success of using reduced order methods to guide the design of a more complex problem.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"144 ","pages":"Article 104403"},"PeriodicalIF":2.3,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144772749","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":"A numerical investigation of the kinematic and fluid dynamic behaviour of an intramuscular autoinjector designed for optimising injection efficiency","authors":"Sudesh Sivarasu ,&nbsp;Ntokozo Magubane ,&nbsp;Chibuike Mbanefo ,&nbsp;Malebogo Ngoepe","doi":"10.1016/j.medengphy.2025.104407","DOIUrl":"10.1016/j.medengphy.2025.104407","url":null,"abstract":"<div><div>The usability and versatility of autoinjectors in managing chronic and autoimmune diseases have made them increasingly attractive in medicine. However, investigations into autoinjector designs require an understanding of the kinematic properties and fluid behaviour during injection. To optimise injection efficiency, this study develops a mathematical and computational fluid dynamics (CFD) model of an IM autoinjector by investigating the effects of viscosity, needle length, needle diameter, and medication volume on the injection process. The model was verified and validated using a comparator experiment and optimised using a parameter sensitivity analysis. The mathematical model results show plunger displacement increases linearly in low viscous fluids (<em>v</em> &lt; 20 cP), allowing faster injections. CFD simulations show that high-viscosity fluids (<em>v</em> &gt; 20 cP) reduce injectability and increase syringeability. Needle gauges below 20 exhibited constant dynamic pressure and negligible shear stress, while gauges between 20 and 25 showed higher shear stress and pressure variability. Longer needles and larger medication volumes increase dynamic pressure and shear stress, prolonging injection time. The mathematical and CFD models matched experimental measurements within a 1.1 % and 4.8 % margin of error, respectively. These findings inform the design of efficient autoinjectors, enhancing drug delivery, patient comfort, and compliance.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"144 ","pages":"Article 104407"},"PeriodicalIF":2.3,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144739425","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":"Explainable deep learning framework for brain tumor detection: Integrating LIME, Grad-CAM, and SHAP for enhanced accuracy","authors":"Abdurrahim Akgündoğdu ,&nbsp;Şerife Çelikbaş","doi":"10.1016/j.medengphy.2025.104405","DOIUrl":"10.1016/j.medengphy.2025.104405","url":null,"abstract":"<div><div>Deep learning approaches have improved disease diagnosis efficiency. However, AI-based decision systems lack sufficient transparency and interpretability. This study aims to enhance the explainability and training performance of deep learning models using explainable artificial intelligence (XAI) techniques for brain tumor detection. A two-stage training approach and XAI methods were implemented. The proposed convolutional neural network achieved 97.20% accuracy, 98.00% sensitivity, 96.40% specificity, and 98.90% ROC-AUC on the BRATS2019 dataset. It was analyzed with explainability techniques including Local Interpretable Model-Agnostic Explanations (LIME), Gradient-weighted Class Activation Mapping (Grad-CAM), and Shapley Additive Explanations (SHAP). The masks generated from these analyses enhanced the dataset, leading to a higher accuracy of 99.40%, 99.20% sensitivity, 99.60% specificity, 99.60% precision, and 99.90% ROC-AUC in the final stage. The integration of LIME, Grad-CAM, and SHAP showed significant success by increasing the accuracy performance of the model from 97.20% to 99.40%. Furthermore, the model was evaluated for fidelity, stability, and consistency and showed reliable and stable results. The same strategy was applied to the BR35H dataset to test the generalizability of the model, and the accuracy increased from 96.80% to 99.80% on this dataset as well, supporting the effectiveness of the method on different data sources.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"144 ","pages":"Article 104405"},"PeriodicalIF":2.3,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144723271","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":"A machine learning approach to concussive group classification using discrete outcome measures from a low-cost movement-based assessment system","authors":"Jacob M. Thomas ,&nbsp;Jamie B. Hall ,&nbsp;Rebecca Bliss ,&nbsp;Emily Leary ,&nbsp;Stephen P. Sayers ,&nbsp;Praveen Rao ,&nbsp;Trent M. Guess","doi":"10.1016/j.medengphy.2025.104402","DOIUrl":"10.1016/j.medengphy.2025.104402","url":null,"abstract":"<div><div>Measurable neuromotor control deficits during functional task performance could provide objective criteria to aid in concussion diagnosis. However, many tools which measure these constructs are unidimensional and not clinically feasible. The purpose of this study was to assess the classification accuracy of a machine learning model using features measured by a clinically feasible movement-based assessment system (Mizzou Point-of-care Assessment System (MPASS) between athletes with and without concussion. Forty collegiate athletes participated. Twenty (19.40 ± 1.04 yrs., 11 females) suffered concussion within two weeks of data collection (5.40 ± 3.68 days). Twenty (19.85 ± 1.20 yrs.) sex, sport, and position-matched athletes had no concussions in the past year. All participants completed three 30-second static balance trials with eyes closed on foam surface under both single task and cognitive dual task conditions, four trials of gait under normal, head shaking, and dual task conditions, and reaction time tasks. Kinematics, kinetics, and reaction times were recorded by MPASS. Measures were used as features for a XGBoost machine learning model. Five-fold cross-validation yielded mean (across 5-folds): 82.5 % accuracy, 75 % sensitivity, 90 % specificity, 88.2 % positive predictive value, and 78.3 % negative predictive value. Results indicate promise for using movement-based features from a low-cost movement-based assessment system to improve the objectivity of concussion diagnosis decision-making.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"144 ","pages":"Article 104402"},"PeriodicalIF":2.3,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144772750","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":"TPC-GCN: Deep learning for pulse pattern classification in traditional Chinese medicine","authors":"Hui Li ,&nbsp;Yuetang Li ,&nbsp;Zhidong Zhang ,&nbsp;Chenyang Xue ,&nbsp;Zhenhua Li ,&nbsp;Xiaobo Li ,&nbsp;Jiuzhang Men","doi":"10.1016/j.medengphy.2025.104401","DOIUrl":"10.1016/j.medengphy.2025.104401","url":null,"abstract":"<div><div>Pulse diagnosis holds a pivotal role in traditional Chinese medicine (TCM) diagnostics, with pulse characteristics serving as one of the critical bases for its assessment. Accurate classification of these pulse pattern is paramount for the objectification of TCM. This study proposes an enhanced SMOTE approach to achieve data augmentation, followed by multi-domain feature extraction. Graph data structures with varying configurations are subsequently constructed to facilitate more profound insights into the intrinsic information within the data. Additionally, a multi-channel lightweight graph convolutional network (GCN) is devised. This network's core strategy lies in extracting diverse layers of information through parallel branches, integrating local structural information with adaptive weights, and employing attention-weighted fusion to improve classification accuracy and model robustness. The proposed network model achieved 91.68% accuracy, a mean F1 score of 92%, a mean recall rate of 92%, and a mean precision rate of 92% on the pulse dataset. The results demonstrate a marked improvement in pulse classification accuracy, validating the efficacy of this approach while offering new perspectives and methodologies for pulse signal classification research.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"144 ","pages":"Article 104401"},"PeriodicalIF":1.7,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144696800","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":"A cascade approach for the early detection and localization of myocardial infarction in 2D-echocardiography","authors":"Carolina Gomez ,&nbsp;Annalisa Letizia ,&nbsp;Vincenza Tufano ,&nbsp;Filippo Molinari ,&nbsp;Massimo Salvi","doi":"10.1016/j.medengphy.2025.104400","DOIUrl":"10.1016/j.medengphy.2025.104400","url":null,"abstract":"<div><div>Myocardial infarction (MI) detection and localization through echocardiography are crucial for effective patient management. However, current diagnostic approaches rely heavily on visual assessment, which can be subjective. In this work we developed a cascade framework for automated MI diagnosis and localization in echocardiograms. Our method combines deep learning for left ventricle wall segmentation with machine learning classification using clinically relevant features. Specifically, we employ a U-Net architecture for segmentation, followed by a two-stage Random Forest classifier for MI detection and localization. We trained and evaluated our approach on two public datasets – CAMUS and HMC-QU. The proposed method achieved 100 % sensitivity and 89.8 % specificity for segment identification, outperforming single-stage classification methods. To the best of our knowledge, this is the first study to apply a multi-step artificial intelligence system combining segmentation and classification for MI diagnosis from echocardiography. This interpretable cascade framework exhibits high performance for early detection and localization of myocardial infarction, demonstrating potential as a clinical decision support tool.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"143 ","pages":"Article 104400"},"PeriodicalIF":2.3,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144700158","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":"Study of a near-cortical over-drilling technique on plate constructs with a conical locking system in a rabbit femoral fracture using a finite element model","authors":"Geovane José Pereira ,&nbsp;Sheila Canevese Rahal ,&nbsp;Wendell Monteiro Barboza ,&nbsp;Ivan Moroz ,&nbsp;Alcides Lopes Leão ,&nbsp;Matheus Mesquita Alves ,&nbsp;Edson Antonio Capello Sousa ,&nbsp;Bruno Agostinho Hernandez","doi":"10.1016/j.medengphy.2025.104399","DOIUrl":"10.1016/j.medengphy.2025.104399","url":null,"abstract":"<div><div>This study aimed to evaluate the near-cortical over-drilling technique on the mechanical behaviour of bone-plate constructs in a rabbit transverse femoral fracture. In vitro biomechanical testing and finite element (FE) models were used for analyses. Rabbits' bones (<em>n</em> = 14) were divided into two groups: G1 - without near-cortical over-drilling, and G2 - with near-cortical over-drilling. Locking stainless-steel plates composed of five holes with titanium bushings were used. A compression test was carried out with load applied eccentrically to the femoral head at a rate of 5 mm/min with load cell capacity of 500 kgf. FE model was created to evaluate differences in stress distributions between G1 and G2. In the vitro tests, the maximum load supported by G2 was statistically higher than G1 (p-value = 0.01 &lt; 0.05), whilst there was no significant difference between the groups in bending stiffness (p-value = 0.12 &gt; 0.05). FE models demonstrated similar behaviour to experimental data in terms of stiffness and biomechanical behaviour for either G1 or G2 (p-value = 0.09 &gt; 0.05). Stress levels were higher for G1, and stress concentration areas were at the experimentally fractured sites. No evident pattern of fracture or stress distribution was observed in the bone for G2. In conclusion, over-drilling increased the maximum load-bearing capacity with a slight decrease in overall stiffness, which could potentially improve bone healing.</div></div>","PeriodicalId":49836,"journal":{"name":"Medical Engineering & Physics","volume":"144 ","pages":"Article 104399"},"PeriodicalIF":2.3,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144772751","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}