Computer methods and programs in biomedicine最新文献

{"title":"Human-computer interaction on virtual reality-based training system for vascular interventional surgery","authors":"Pan Li ,&nbsp;Xinxin Zhang ,&nbsp;Xiaowei Hu ,&nbsp;Fangting Ding ,&nbsp;Cunman Liang","doi":"10.1016/j.cmpb.2025.108731","DOIUrl":"10.1016/j.cmpb.2025.108731","url":null,"abstract":"<div><h3>Background and objective</h3><div>Currently, precision control and interaction between virtual hand models and ultrafine, ultra-long flexible guidewires in virtual vascular interventional surgery training systems still remain challenging.</div></div><div><h3>Methods</h3><div>To tackle this issue, this study utilized a hybrid approach combining Cosserat rod theory with quaternions to develop a model for ultra-long flexible guidewires. Through the implementation of a spatial hash-based continuous collision detection (CCD) algorithm, the system achieved precise collision detection between the guidewire and blood vessels. Additionally, adhesive collision particles were integrated into the fingers of the virtual hand model involved in interactions, facilitating the simulation of intervention tasks such as grasping and delivering. CCD technology, in conjunction with extended bounding volume, was employed in the blood vessel model to prevent tunneling effects resulting from rapid hand manipulations.</div></div><div><h3>Results</h3><div>Experiments were conducted to assess the picking, delivery, and consistency of delivery distance, showcasing the alignment of manipulation between the virtual hand models and real hands when handling the guidewire. The virtual hand model successfully navigated the flexible guidewire model into vessels curved at angles of 30°, 60°, 90°, and 120°, achieving an average response time of 12.64 ms. Moreover, across vessel models curved at various angles, the average disparity between the delivery distance along the <em>x</em>-axis by the hand in a real environment and the guidewire's delivery distance within the virtual vessel model was approximately 3.71 mm, showcasing a high level of smoothness and stability in the interaction between the hand model and the guidewire model.</div></div><div><h3>Conclusions</h3><div>Finally, within the virtual system, the successful navigation of the hand delivering the guidewire through the femoral artery and radial artery towards the heart further demonstrates the excellent interaction performance between the virtual hand model and the ultrafine, ultra-long flexible guidewires. This success provides both theoretical and experimental support for the interactive training of virtual hand models and guidewires within virtual surgical training systems.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"265 ","pages":"Article 108731"},"PeriodicalIF":4.9,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting protein-protein interaction with interpretable bilinear attention network","authors":"Yong Han ,&nbsp;Shao-Wu Zhang ,&nbsp;Ming-Hui Shi ,&nbsp;Qing-Qing Zhang ,&nbsp;Yi Li ,&nbsp;Xiaodong Cui","doi":"10.1016/j.cmpb.2025.108756","DOIUrl":"10.1016/j.cmpb.2025.108756","url":null,"abstract":"<div><h3>Background and Objective</h3><div>Protein-protein interactions (PPIs) play the key roles in myriad biological processes, helping to understand the protein function and disease pathology. Identification of PPIs and their interaction types through wet experimental methods are costly and time-consuming. Therefore, some computational methods (e.g., sequence-based deep learning method) have been proposed to predict PPIs. However, these methods predominantly focus on protein sequence information, neglecting the protein structure information, while the protein structure is closely related to its function. In addition, current PPI prediction methods that introduce the protein structure information use independent encoders to learn the sequence and structure representations from protein sequences and structures, respectively, without explicitly learn the important local interaction representation of two proteins, making the prediction results hard to interpret.</div></div><div><h3>Methods</h3><div>Considering that current protein structure prediction methods (e.g., AlphaFold2) can accurately predict protein 3D structures and also provide a large number of protein 3D structures, here we present a novel end-to-end framework (called PPI-BAN) to predict PPIs and their interaction types by integrating protein sequence information and 3D structure information. PPI-BAN uses one-dimensional convolution operation (Conv1D) to extract the protein sequence features, employes GeomEtry-Aware Relational Graph Neural Network (GearNet) to learn protein 3D structure features, and adopts a deep bilinear attention network (BAN) to learn the joint features between one protein sequence and its 3D structure. The sequence features, structure features and joint features are concatenated to fed into a fully connected network for predicting PPIs and their interaction types.</div></div><div><h3>Results</h3><div>Experimental results show that PPI-BAN achieves the best overall performance against other state-of-the-art methods.</div></div><div><h3>Conclusions</h3><div>PPI-BAN can effectively predict PPIs and their interaction types, and identify the significant interaction sites by computing attention weight maps and mapping them to specific amino acid residues.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"265 ","pages":"Article 108756"},"PeriodicalIF":4.9,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Combating Medical Label Noise through more precise partition-correction and progressive hard-enhanced learning","authors":"Sanyan Zhang ,&nbsp;Surong Chu ,&nbsp;Yan Qiang ,&nbsp;Juanjuan Zhao ,&nbsp;Yan Wang ,&nbsp;Xiao Wei","doi":"10.1016/j.cmpb.2025.108734","DOIUrl":"10.1016/j.cmpb.2025.108734","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Computer-aided diagnosis systems based on deep neural networks heavily rely on datasets with high-quality labels. However, manual annotation for lesion diagnosis relies on image features, often requiring professional experience and complex image analysis process. This inevitably introduces noisy labels, which can misguide the training of classification models. Our goal is to design an effective method to address the challenges posed by label noise in medical images.</div></div><div><h3>Methods:</h3><div>we propose a novel noise-tolerant medical image classification framework consisting of two phases: fore-training correction and progressive hard-sample enhanced learning. In the first phase, we design a dual-branch sample partition detection scheme that effectively classifies each instance into one of three subsets: clean, hard, or noisy. Simultaneously, we propose a hard-sample label refinement strategy based on class prototypes with confidence-perception weighting and an effective joint correction method for noisy samples, enabling the acquisition of higher-quality training data. In the second phase, we design a progressive hard-sample reinforcement learning method to enhance the model’s ability to learn discriminative feature representations. This approach accounts for sample difficulty and mitigates the effects of label noise in medical datasets.</div></div><div><h3>Results:</h3><div>Our framework achieves an accuracy of 82.39% on the pneumoconiosis dataset collected by our laboratory. On a five-class skin disease dataset with six different levels of label noise (0, 0.05, 0.1, 0.2, 0.3, and 0.4), the average accuracy over the last ten epochs reaches 88.51%, 86.64%, 85.02%, 83.01%, 81.95%, 77.89%, respectively; For binary polyp classification under noise rates of 0.2, 0.3, and 0.4, the average accuracy over the last ten epochs is 97.90%, 93.77%, 89.33%, respectively.</div></div><div><h3>Conclusions:</h3><div>The effectiveness of our proposed framework is demonstrated through its performance on three challenging datasets with both real and synthetic noise. Experimental results further demonstrate the robustness of our method across varying noise rates.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"265 ","pages":"Article 108734"},"PeriodicalIF":4.9,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Brain age prediction based on brain region volume modeling under broad network field of view","authors":"Jianjie Zheng ,&nbsp;Junkai Wang ,&nbsp;Zeyin Zhang ,&nbsp;Kuncheng Li ,&nbsp;Huimin Zhao ,&nbsp;Peipeng Liang","doi":"10.1016/j.cmpb.2025.108739","DOIUrl":"10.1016/j.cmpb.2025.108739","url":null,"abstract":"<div><h3>Background and objective</h3><div>Brain region volume from Structural Magnetic Resonance Imaging (sMRI) can directly reflect abnormal states in brain aging. While promising for clinical brain health assessment, existing volume-based brain age prediction methods fail to explore both linear and nonlinear relationships, resulting in weak representation and suboptimal estimates.</div></div><div><h3>Methods</h3><div>This paper proposes a brain age prediction method, RFBLSO, based on Random Forest (RF), Broad Learning System (BLS), and Leave-One-Out Cross Validation (LOO). Firstly, RF is used to eliminate redundant brain regions with low correlation to the target value. The objective function is constructed by integrating feature nodes, enhancement nodes, and optimal regularization parameters. Subsequently, the pseudo-inverse method is employed to solve for the output coefficients, which facilitates a more accurate representation of the linear and nonlinear relationships between volume features and brain age.</div></div><div><h3>Results</h3><div>Across various datasets, RFBLSO demonstrates the capability to formulate brain age prediction models, achieving a Mean Absolute Error (MAE) of 4.60 years within the Healthy Group and 4.98 years within the Chinese2020 dataset. In the Clinical Group, RFBLSO achieves measurement and effective differentiation among Healthy Controls (HC), Mild Cognitive Impairment (MCI), and Alzheimer's disease (AD) (MAE for HC, MCI, and AD: 4.46 years, 8.77 years, 13.67 years; the effect size η2 of the analysis of variance for AD/MCI vs. HC is 0.23; the effect sizes of post-hoc tests are Cohen's <em>d</em> = 0.74 (AD vs. MCI), 1.50 (AD vs. HC), 0.77 (MCI vs. HC)). Compared to other linear or nonlinear brain age prediction methods, RFBLSO offers more accurate measurements and effectively distinguishes between Clinical Groups. This is because RFBLSO can simultaneously explore both linear and nonlinear relationships between brain region volume and brain age.</div></div><div><h3>Conclusion</h3><div>The proposed RFBLSO effectively represents both linear and nonlinear relationships between brain region volume and brain age, allowing for more accurate individual brain age estimation. This provides a feasible method for predicting the risk of neurodegenerative diseases.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"265 ","pages":"Article 108739"},"PeriodicalIF":4.9,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Relationship Between the Elastic Modulus of the Novel Pedicle Screw-Plate System and Biomechanical Properties Under Osteoporotic Condition: A Power-Law Regression Analysis Based on Parametric Finite Element Simulations","authors":"Kaibin Wang ,&nbsp;Chongyi Wang ,&nbsp;Haipeng Si ,&nbsp;Yanwei Zhang ,&nbsp;Shaowei Sang ,&nbsp;Runtong Zhang ,&nbsp;Wencan Zhang ,&nbsp;Junfei Chen ,&nbsp;Chen Liu ,&nbsp;Kunpeng Li ,&nbsp;Bingtao Hu ,&nbsp;Xiangyu Lin ,&nbsp;Yunze Feng ,&nbsp;Qingyang Fu ,&nbsp;Zhihao Kang ,&nbsp;Mingyu Xu ,&nbsp;Dingxin Zhang ,&nbsp;Wanlong Xu ,&nbsp;Le Li","doi":"10.1016/j.cmpb.2025.108760","DOIUrl":"10.1016/j.cmpb.2025.108760","url":null,"abstract":"<div><h3>Background and objective</h3><div>The novel pedicle screw-plate system (NPSPS) is a new internal fixation method for the thoracic spine that we proposed, which has demonstrated effectiveness through clinical practice and biomechanical testing. Nevertheless, the optimal elastic modulus of NPSPS (NPSPS-E) remains debated, particularly for osteoporosis patients. We propose a more efficient method to predict the biomechanical effects of NPSPS across varying elastic moduli in osteoporosis using parametric finite element (FE) analysis, establishing the regression relationship between NPSPS-E and biomechanical properties.</div></div><div><h3>Methods</h3><div>An FE surgical model of NPSPS under osteoporotic conditions was developed. The NPSPS-E was linearly varied from 3.6 GPa (polyether ether ketone) to 110 GPa (titanium alloy). Using power-law regression analysis, a functional equation was established to correlate NPSPS-E with biomechanical properties under osteoporotic condition.</div></div><div><h3>Results</h3><div>Power-law equations and regression models were successfully established between NPSPS-E and biomechanical prediction indices under osteoporotic condition (<em>P</em>&lt;0.0001). As NPSPS-E increased, the range of motion (ROM) of the T8-T10 spinal segments decreased from 0.51°-4.06° to 0.24°-1.45°. The mean von Mises stress in the T8-T10 vertebrae declined from 1.36 MPa-2.03 MPa to 1.15 MPa-1.79 MPa. Concurrently, the stress shielding ratios and the total stress ratios of the NPSPS increased from 3.66%-48.07% and 13.96%-26.96% to 10.70%-56.20% and 52.62%-64.40%, respectively.</div></div><div><h3>Conclusion</h3><div>The functional equations derived from these models serve as a predictive tool to directly estimate the biomechanical effects of NPSPS across a range of elastic modulus under osteoporotic conditions, thereby facilitating the design and optimization of NPSPS materials.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"265 ","pages":"Article 108760"},"PeriodicalIF":4.9,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Breaking through scattering: The H-Net CNN model for image retrieval","authors":"Roger Chiu-Coutino ,&nbsp;Miguel S. Soriano-Garcia ,&nbsp;Carlos Israel Medel-Ruiz ,&nbsp;S.M. Afanador-Delgado ,&nbsp;Edgar Villafaña-Rauda ,&nbsp;Roger Chiu","doi":"10.1016/j.cmpb.2025.108723","DOIUrl":"10.1016/j.cmpb.2025.108723","url":null,"abstract":"<div><h3>Background:</h3><div>In scattering media, traditional optical imaging techniques often find it significantly challenging to accurately reconstruct images owing to rapid light scattering. Thus, to address this problem, we propose a convolutional neural network architecture called H-Net, which is specifically designed to recover structural information from images distorted by scattering media.</div></div><div><h3>Method:</h3><div>Our approach involves the use of dilated convolutions to capture local and global features of the distorted images, allowing for the effective reconstruction of the underlying structures. First, we developed a diffuse image dataset by projecting handwritten numbers through diffusers with different thicknesses, capturing the resulting distorted images. Second, we generated a synthetic speckle images dataset, composed of simulated speckle patterns. These datasets were designed to train the model to recover structures within scattering media. To evaluate the model’s performance, we calculated the Structural Similarity Measure Index between the model’s predictions and the original images on unseen data.</div></div><div><h3>Result:</h3><div>This proposed architecture achieves reconstructions with an average structural similarity index measure of 0.8 while maintaining low computational costs.</div></div><div><h3>Conclusion:</h3><div>The results of this study indicate that H-Net offers an alternative to more complex and computationally expensive models, providing efficient and reliable image reconstruction in scattering media.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"265 ","pages":"Article 108723"},"PeriodicalIF":4.9,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143714963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fusion of multi-scale feature extraction and adaptive multi-channel graph neural network for 12-lead ECG classification","authors":"Teng Chen ,&nbsp;Yumei Ma ,&nbsp;Zhenkuan Pan ,&nbsp;Weining Wang ,&nbsp;Jinpeng Yu","doi":"10.1016/j.cmpb.2025.108725","DOIUrl":"10.1016/j.cmpb.2025.108725","url":null,"abstract":"<div><h3>Background and objective:</h3><div>The 12-lead electrocardiography (ECG) is a widely used diagnostic method in clinical practice for cardiovascular diseases. The potential correlation between interlead signals is an important reference for clinical diagnosis but is often overlooked by most deep learning methods. Although graph neural networks can capture the associations between leads through edge topology, the complex correlations inherent in 12-lead ECG may involve edge topology, node features, or their combination.</div></div><div><h3>Methods:</h3><div>In this study, we propose a multi-scale adaptive graph fusion network (MSAGFN) model, which fuses multi-scale feature extraction and adaptive multi-channel graph neural network (AMGNN) for 12-lead ECG classification. The proposed MSAGFN model first extracts multi-scale features individually from 12 leads and then utilizes these features as nodes to construct feature graphs and topology graphs. To efficiently capture the most correlated information from the feature graphs and topology graphs, AMGNN iteratively performs a series of graph operations to learn the final graph-level representations for prediction. Moreover, we incorporate consistency and disparity constraints into our model to further refine the learned features.</div></div><div><h3>Results:</h3><div>Our model was validated on the PTB-XL dataset, achieving an area under the receiver operating characteristic curve score of 0.937, mean accuracy of 0.894, and maximum F1 score of 0.815. These results surpass the corresponding metrics of state-of-the-art methods. Additionally, we conducted ablation studies to further demonstrate the effectiveness of our model.</div></div><div><h3>Conclusions:</h3><div>Our study demonstrates that, in 12-lead ECG classification, by constructing topology graphs based on physiological relationships and feature graphs based on lead feature relationships, and effectively integrating them, we can fully explore and utilize the complementary characteristics of the two graph structures. By combining these structures, we construct a comprehensive data view, significantly enhancing the feature representation and classification accuracy.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"265 ","pages":"Article 108725"},"PeriodicalIF":4.9,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Physics-Integrated Deep Learning Approach for Patient-Specific Non-Newtonian Blood Viscosity Assessment using PPG","authors":"Hyeong Jun Lee ,&nbsp;Young Woo Kim ,&nbsp;Seung Yong Shin ,&nbsp;San Lee Lee ,&nbsp;Chae Hyeon Kim ,&nbsp;Kyung Soo Chung ,&nbsp;Joon Sang Lee","doi":"10.1016/j.cmpb.2025.108740","DOIUrl":"10.1016/j.cmpb.2025.108740","url":null,"abstract":"<div><h3>Background and Objective</h3><div>The aim of this study is to extract a patient-specific viscosity equation from photoplethysmography (PPG) data. An aging society has increased the need for remote, non-invasive health monitoring systems. However, the circulatory system remains beyond the scope of wearable devices. The solution might be found in the possibility of measuring blood viscosity from wearable devices. Blood viscosity information can be used to monitor and diagnose various circulatory system diseases. Therefore, if blood viscosity can be calculated from wearable photoplethysmography, the versatility of a non-invasive health monitoring system can be broadened.</div></div><div><h3>Methods</h3><div>A hybrid 1D CNN-LSTM architecture incorporating physics-informed constraints was developed to integrate rheological principles into data-driven PPG analysis. The shear-viscosity equation derived from the viscometer was used as ground-truth data. The signal obtained from the wearable devices was processed with noise filtering and wandering elimination to gain stable blood pressure waves. The neural network was trained using k-fold cross-validation and weight factor optimization, with the loss function incorporating rheological constraints from the Carreau–Yasuda model.</div></div><div><h3>Results</h3><div>The final estimation model achieved an accuracy of 81.1 %. The accuracy in the physiological shear range (50–300 s^-1) was 84.0 %, outperforming other low and high shear regions. Mean absolute errors of 0.67 cP in the physiological range align with clinical viscometry tolerances (&lt; 1 cP), demonstrating diagnostic feasibility. Statistical analysis revealed strong linear relationships between predicted and ground truth values across all shear rates (correlation coefficients: 0.619–0.742, <em>p</em> &lt; 0.0001), with mean absolute errors decreasing from 7.84 cP at low shear rates to 0.67 cP in the physiological range. The accuracy and contribution of each parameter to the Carreau–Yasuda model were also analyzed. The results show that the contribution of each parameter varies based on the shear range, providing insight into weight factor optimization.</div></div><div><h3>Conclusion</h3><div>By non-invasively estimating blood viscosity from PPG, the diagnostic capabilities of wearable healthcare systems can be expanded to target various diseases related to the circulatory system. The demonstrated accuracy in physiologically relevant shear ranges supports the potential clinical application of this methodology.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"265 ","pages":"Article 108740"},"PeriodicalIF":4.9,"publicationDate":"2025-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fluid-structure interaction simulations in patient-specific coronary arteries with aneurysms: Viscoelastic or shear-thinning property of blood","authors":"C. Paz ,&nbsp;E. Suárez ,&nbsp;C. Gil ,&nbsp;S.I.S. Pinto","doi":"10.1016/j.cmpb.2025.108736","DOIUrl":"10.1016/j.cmpb.2025.108736","url":null,"abstract":"<div><h3>Background and Objective</h3><div>Coronary artery aneurysm is a progressive and often asymptomatic condition with a prevalence ranging from 0.3 % to 5 %. This condition can lead to severe and potentially fatal complications. Given the challenges of conducting experiments on real patients, numerical simulations have emerged as a valuable alternative. This study aims to enhance the accuracy of hemodynamic analysis and fluid-structure interaction assessments by incorporating the viscoelastic properties of blood, which are often overlooked, in simulations of the right coronary artery with various aneurysm morphologies.</div></div><div><h3>Methods</h3><div>This research employs numerical simulations to analyse the hemodynamics and assess the one-way coupled fluid-structure interaction within the right coronary artery. The study utilised the simplified Phan-Thien/Tanner model to represent the viscoelastic properties of blood. Different aneurysm morphologies were simulated, and the results were compared with those obtained using the widely recognised Carreau model. The analysis focused on the time-average wall-shear stress, as well as the stress and deformation experienced by the aneurysm wall.</div></div><div><h3>Results</h3><div>The hemodynamic analysis demonstrated that the simplified Phan-Thien/Tanner model produced similar flow patterns to the Carreau model but resulted in a significant reduction of approximately 50 % in the time-average wall-shear stress. This reduction aligns with previous findings. Additionally, the study revealed substantial differences in the stress and deformation of the aneurysm wall, with the simplified Phan-Thien/Tanner model proving more accuracy. The largest deformations were observed in aneurysms with incipient and fusiform shapes, particularly in the divergent section of the proximal region. In the case of saccular aneurysms, the most compromised area was identified not within the aneurysm sac itself, but in the region of the artery just upstream.</div></div><div><h3>Conclusions</h3><div>Incorporating the viscoelastic properties of blood into fluid-structure interaction simulations significantly improves the accuracy of hemodynamic and structural assessments of coronary artery aneurysms. This study underscores the importance of considering these properties when evaluating aneurysm behaviour, which could have important implications for understanding the progression and potential rupture of aneurysms, thereby guiding more effective clinical interventions.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"265 ","pages":"Article 108736"},"PeriodicalIF":4.9,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of the impacts of neuromuscular electrical stimulation based on cortico-muscular-cortical functional network","authors":"Jianpeng Tang ,&nbsp;Xugang Xi ,&nbsp;Ting Wang ,&nbsp;Lihua Li ,&nbsp;Jian Yang","doi":"10.1016/j.cmpb.2025.108735","DOIUrl":"10.1016/j.cmpb.2025.108735","url":null,"abstract":"<div><h3>Background and objective</h3><div>Neuromuscular electrical stimulation (NMES) has been extensively applied for recovery of motor functions. However, its impact on the cortical network changes related to muscle activity remains unclear, which is crucial for understanding the changes in the collaborative working patterns within the sensory-motor control system post-stroke.</div></div><div><h3>Methods</h3><div>In this research, we have integrated cortico-muscular interactions, intercortical interactions, and intramuscular interactions to propose a novel closed-loop network structure, namely the cortico-muscular-cortical functional network (CMCFN). The framework is endowed with the capability to distinguish the directionality of causal interactions and local frequency band characteristics through transfer spectral entropy (TSE). Subsequently, the CMCFN is applied to stroke patients to elucidate the potential influence of NMES on cortical physiological function changes during motor induction.</div></div><div><h3>Results</h3><div>The results indicate that short-term modulation by NMES significantly enhanced the cortico-muscular interactions of the contralateral cerebral hemisphere and the affected upper limb (p &lt; 0.001), while coexistence of facilitatory and inhibitory effects is observed in the intermuscular coupling across different electromyography (EMG) signals. Furthermore, following NMES treatment, the connectivity of the brain functional network is significantly strengthened, particularly in the γ frequency band (30–45 Hz), with marked improvements in the clustering coefficient and shortest path length (p &lt; 0.001).</div></div><div><h3>Conclusions</h3><div>As a new framework, CMCFN offers a novel perspective for studying motor cortical networks related to muscle activity.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"265 ","pages":"Article 108735"},"PeriodicalIF":4.9,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143705882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}