Computer methods and programs in biomedicine最新文献

{"title":"Early detection of Multidrug Resistance using Multivariate Time Series analysis and interpretable patient-similarity representations","authors":"Óscar Escudero-Arnanz ,&nbsp;Antonio G. Marques ,&nbsp;Inmaculada Mora-Jiménez ,&nbsp;Joaquín Álvarez-Rodríguez ,&nbsp;Cristina Soguero-Ruiz","doi":"10.1016/j.cmpb.2025.108920","DOIUrl":"10.1016/j.cmpb.2025.108920","url":null,"abstract":"<div><h3>Background and Objectives:</h3><div>Multidrug Resistance has been identified by the World Health Organization as a major global health threat. It leads to severe social and economic consequences, including extended hospital stays, increased healthcare costs, and higher mortality rates. In response to this challenge, this study proposes a novel interpretable Machine Learning (ML) approach for predicting MDR, developed with two primary objectives: accurate inference and enhanced explainability.</div></div><div><h3>Methods:</h3><div><em>For inference</em>, the proposed method is based on patient-to-patient similarity representations to predict MDR outcomes. Each patient is modeled as a Multivariate Time Series (MTS), capturing both clinical progression and interactions with similar patients. To quantify these relationships, we employ MTS-based similarity metrics, including feature engineering using descriptive statistics, Dynamic Time Warping, and the Time Cluster Kernel. These methods are used as inputs for MDR classification through Logistic Regression, Random Forest, and Support Vector Machines, with dimensionality reduction and kernel transformations applied to enhance model performance. <em>For explainability</em>, we employ graph-based methods to extract meaningful patterns from the data. Patient similarity networks are generated using the MTS-based similarity metrics mentioned above, while spectral clustering and t-SNE are applied to identify MDR-related subgroups, uncover clinically relevant patterns, and visualize high-risk clusters. These insights improve interpretability and support more informed decision-making in critical care settings.</div></div><div><h3>Results:</h3><div>We validate our architecture on real-world Electronic Health Records from the Intensive Care Unit (ICU) dataset at the University Hospital of Fuenlabrada, achieving a Receiver Operating Characteristic Area Under the Curve of 81%. Our framework surpasses ML and deep learning models on the same dataset by leveraging graph-based patient similarity. In addition, it offers a simple yet effective interpretability mechanism that facilitates the identification of key risk factors—such as prolonged antibiotic exposure, invasive procedures, co-infections, and extended ICU stays—and the discovery of clinically meaningful patient clusters. For transparency, all results and code are available at <span><span>https://github.com/oscarescuderoarnanz/DM4MTS</span><svg><path></path></svg></span>.</div></div><div><h3>Conclusions:</h3><div>This study demonstrates the effectiveness of patient similarity representations and graph-based methods for MDR prediction and interpretability. The approach enhances prediction, identifies key risk factors, and improves patient stratification, enabling early detection and targeted interventions, highlighting the potential of interpretable ML in critical care.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"270 ","pages":"Article 108920"},"PeriodicalIF":4.9,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144633137","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":"Deep Neural Network Hybrid Simulations to Evaluate the Poynting Effect in 3D Ogden Hyperelastic Modeling of Brain White Matter","authors":"M. Agarwal,&nbsp;Assimina A. Pelegri","doi":"10.1016/j.cmpb.2025.108961","DOIUrl":"10.1016/j.cmpb.2025.108961","url":null,"abstract":"<div><h3>Background</h3><div>Modeling and characterization of brain white matter (BWM) are challenging due to its anisotropic 3D microarchitecture and complex interactions among the constituent phases of axons, myelin, and glia. Shear biomechanics is critical for understanding traumatic brain injury (TBI), as shear forces dominate during such events. Simple shear tests reveal the non-linear Poynting effect (PE), characterized by elongation or contraction normal to the applied shear. Accurately simulating BWM’s anisotropic hyperelastic (HE) behavior using finite element methods (FEM) is computationally intensive.</div></div><div><h3>Methods</h3><div>This study proposes a hybrid computational workflow to simulate the Poynting effect in BWM using the Ogden HE material model. Representative volume elements (RVEs) of BWM, including detailed axon–myelin–glia interactions, are generated with varying microarchitectures and material properties to train surrogate ML/DL models. Deep 3D convolutional neural networks process voxelized BWM microarchitecture as input and are trained on FEM-derived stress and stiffness tensors as output.</div></div><div><h3>Results</h3><div>The multiscale 3D ResNet architecture provided the most accurate predictions of HE stress tensors (with normal stress terms capturing PE) and stiffness matrices across simple shear scenarios. Quantitative analysis revealed that PE was most pronounced when shear was applied perpendicular to the axonal cross-sections, with triphasic RVEs demonstrating up to four times greater PE than prior biphasic (axon, glia) models.</div></div><div><h3>Significance</h3><div>For the first time, a hybrid, microarchitecture-inspired model has been developed to facilitate near real-time simulations of PE response in BWM. This approach significantly reduces computational costs while retaining model scalability and ease of parameterization. The framework could improve medical imaging interpretation and support advanced medical interventions.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"270 ","pages":"Article 108961"},"PeriodicalIF":4.9,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144680513","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":"Handwritten signature verification using a wearable surface-EMG armband","authors":"Jing Zheng ,&nbsp;Minwei Zhou ,&nbsp;Zhehao Zhou ,&nbsp;Jieyi Ge ,&nbsp;Hang Chen ,&nbsp;Xiaobai Li ,&nbsp;Wanlin Chen ,&nbsp;Shulin Chen","doi":"10.1016/j.cmpb.2025.108908","DOIUrl":"10.1016/j.cmpb.2025.108908","url":null,"abstract":"<div><div>The growing demand for remote authentication underscores the importance of robust signature verification systems. A major challenge in this domain is the substantial intra-class variability inherent in handwritten signatures. This study investigates the use of surface electromyography (sEMG) for signature verification through wearable armbands, aiming to address this issue. We introduce a dual-model deep learning framework that integrates muscle co-activation patterns with raw sEMG signal waveforms. A 4-channel armband was employed to collect sEMG data from 20 individuals signing Chinese characters, resulting in the first sEMG signature dataset centered on wearable acquisition. Experimental results show that conventional feature-based machine learning methods are limited in performance, yielding 80.90% accuracy and a 12.82% equal error rate (EER), primarily due to high intra-class variability. The proposed framework comprises: (1) a CNN-LSTM architecture that processes encoded muscle activation sequences, and (2) a multi-branch CNN designed to learn from raw sEMG signals. Fusion at the decision level between these models achieves 91.65% accuracy and 5.25% EER, reflecting a 10.75% improvement in accuracy compared with traditional techniques. These findings confirm the effectiveness of the proposed approach in reducing intra-class variability while preserving the usability of wearable devices, offering a practical and secure biometric authentication solution.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"270 ","pages":"Article 108908"},"PeriodicalIF":4.9,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144623567","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 novel homogeneity index based on the area integration of dose volume histogram","authors":"Huanfan Su ,&nbsp;Suyan Bi ,&nbsp;Xiangshang Sun ,&nbsp;Qingqing Yuan ,&nbsp;Meiling Xu ,&nbsp;Zhitao Dai","doi":"10.1016/j.cmpb.2025.108935","DOIUrl":"10.1016/j.cmpb.2025.108935","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Background and Objective:&lt;/h3&gt;&lt;div&gt;The Homogeneity Index (HI) is a critical clinical metric for evaluating the conformity of radiation dose distribution to the prescribed dose in the target volume during radiotherapy, where cold spots may reduce tumor control probability and hot spots increase toxicity risks to adjacent critical organs. Conventional HIs based on limited dose-volume histogram (DVH) points fail to distinguish between cases with similar dose values but different spatial distributions, while voxel-based HIs offer higher accuracy but face challenges in clinical adoption due to computational complexity. This study introduces a novel HI through DVH area integration, systematically quantifying both cold and hot spot effects in target volumes to provide an intuitive and clinically practical tool for dose uniformity assessment, with rigorous validation of its dose evaluation accuracy.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods:&lt;/h3&gt;&lt;div&gt;The novel HIs comprise three parameters: a cold spot index (&lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;H&lt;/mi&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;I&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;c&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;), a hot spot index (&lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;H&lt;/mi&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;I&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;h&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;), and a global homogeneity index (&lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;H&lt;/mi&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;I&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;g&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;), as defined in Eqs. &lt;span&gt;&lt;span&gt;(1)&lt;/span&gt;&lt;/span&gt;-&lt;span&gt;&lt;span&gt;(4)&lt;/span&gt;&lt;/span&gt;. &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;H&lt;/mi&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;I&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;c&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; and &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;H&lt;/mi&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;I&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;h&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; quantify the impacts of cold and hot spots on dose homogeneity, with ideal values approaching zero (smaller values indicate better uniformity). This study involved 11 patients with brain metastases who underwent CyberKnife (CK) stereotactic radiosurgery (SRS) with varying prescription isodose lines (PIDLs). The novel HIs were calculated from DVHs to differentiate dose uniformity among SRS plans. Pearson correlations between the novel HIs and corresponding point doses or previously established HIs were also examined.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results:&lt;/h3&gt;&lt;div&gt;The novel HIs effectively differentiated dose homogeneity across various PIDLs, with values decreasing as PIDL increased. The maximum variations in the mean &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;H&lt;/mi&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;I&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;c&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;, &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;H&lt;/mi&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;I&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;h&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;, and &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;H&lt;/mi&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;I&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;g&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; were -66.86%, -84.04%, and -83.95%, respectively, for PIDLs ranging from 50% to 90%. Strong negative correlations were observed between the novel HIs (&lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;H&lt;/mi&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;I&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;c&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"270 ","pages":"Article 108935"},"PeriodicalIF":4.9,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144656195","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":"FMCW radar based high-precision gridless non-contact vital signs monitoring for Internet of Medical Things","authors":"Yutian Lei ,&nbsp;Zhenmiao Deng ,&nbsp;Du Li ,&nbsp;Mingjuan Wu","doi":"10.1016/j.cmpb.2025.108932","DOIUrl":"10.1016/j.cmpb.2025.108932","url":null,"abstract":"<div><h3>Background:</h3><div>Vital signs monitoring is of paramount importance in healthcare, serving as a crucial component in disease prevention, diagnosis, and management. Traditional contact-based devices, including electrocardiographs and pulse oximeters, while providing vital data, face limitations in long-term use owing to patient discomfort.</div></div><div><h3>Objective:</h3><div>This study aims to propose a non-contact monitoring system utilizing Frequency Modulated Continuous Wave (FMCW) radar for continuous, non-invasive health monitoring. The objective is to overcome the constraints of traditional methods and enhance the feasibility of remote chronic disease management.</div></div><div><h3>Methods:</h3><div>The proposed system employs the Multiple Signal Classification (MUSIC) algorithm to estimate respiration and heart rates. To tackle challenges such as noise interference and signal overlap, an enhanced root-MUSIC algorithm is introduced. This algorithm transforms the single-channel model into a multi-channel one and optimizes signal estimation through semi-definite programming (SDP) and the Alternating Direction Method of Multipliers (ADMM). Simulations and real-world experiments were conducted to validate the system’s effectiveness.</div></div><div><h3>Results:</h3><div>The validation process demonstrated the system’s efficacy, revealing that the multi-channel model significantly reduces theoretical error bounds. In experimental trials, the method achieved a respiration rate Root Mean Squared Error (RMSE) of 0.0131 Hz and a heart rate RMSE of 0.0394 Hz, with corresponding accuracies of 96.05% and 90%. Bland–Altman analysis further corroborated strong concordance with contact-based devices.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"270 ","pages":"Article 108932"},"PeriodicalIF":4.9,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144656194","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":"Design a bistable polymeric vascular stent (BPVS) and evaluate the biomechanical properties","authors":"Chen Pan ,&nbsp;Zhifang Fan ,&nbsp;Jingjing Cao ,&nbsp;Hezong Li","doi":"10.1016/j.cmpb.2025.108960","DOIUrl":"10.1016/j.cmpb.2025.108960","url":null,"abstract":"<div><h3>Background and objectives</h3><div>Polymeric vascular stents generally have the disadvantages of poor biomechanical properties, which may not achieve the therapeutic purpose of supporting the blocked vascular vessels to restore normal blood flow. The bistable structure depending on the two stable configurations seems to improve the weak strength of stents. This paper mainly designs a polymeric vascular stent with bistable structure to enhance the radial force, and reduce the radial recoil and wall shear stress.</div></div><div><h3>Methods</h3><div>The bistable stents were derived from the bistable property of the tilted strut and the planar cell systematically. The mapping relationship between the tilted struts with different geometries and the bistable performance was revealed by finite element method, and then the bistable characteristics of the planar cells were further explored. Furthermore, the biomechanical performance involving radial force and radial recoil of bistable polymeric stents, and wall shear stress of vascular vessels were analyzed and evaluate by combining numerical simulation and experiments.</div></div><div><h3>Results</h3><div>The mapping relation between geometries and bistable properties of tilted struts was that the (<em>t/L, θ</em>) = (0.03, 10° ∼ 60°), and (<em>t/L, θ</em>) = (0.03 ∼ 0.1, 30° ∼ 40°) were the widest ranges of optional parameters. When the bistable evaluation factor <em>B</em> = <em>T</em>/<em>t</em> ≥ 4, the BREH cells had outstanding bistable properties. The finite element results of polymeric stents indicated that the bistable structure obviously greatened the radial force (2.52 N), and lessened the radial recoil (1.69 %) of the polymeric stent. Besides, the bistable structure minified the wall shear stress of vascular vessels to 0.04177 MPa.</div></div><div><h3>Conclusions</h3><div>It could be concluded that the bistable structure not only endowed polymeric stents with strong biomechanical properties, but also reduces the risk of secondary injury after its being implanted into vascular vessels. The bistable polymeric stents have the potential to support the blocked vessels and restore the blood flow.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"270 ","pages":"Article 108960"},"PeriodicalIF":4.9,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144632497","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 linear and nonlinear classifiers for distinguishing between healthy subjects and patients with valvular heart diseases based on electrocardiograms, seismocardiograms, and gyrocardiograms","authors":"Szymon Sieciński ,&nbsp;Marcin Grzegorzek","doi":"10.1016/j.cmpb.2025.108925","DOIUrl":"10.1016/j.cmpb.2025.108925","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Heart rate variability (HRV) is a prognostic marker in numerous cardiovascular and non-cardiovascular conditions. Valvular heart disease (VHD) is a cardiovascular disease that affects the heart valves (aortic valve, mitral valve, pulmonic valve and tricupsid valve) and is the third most common cardiovascular disease. Traditional methods, such as echocardiography, computed tomography, and magnetic resonance imaging, are effective, but their limitations in outpatient monitoring have led to the exploration of alternative techniques, such as electrocardiography (ECG), seismocardiography (SCG) and gyrocardiography (SCG). In this study, we evaluated seven methods for differentiation between healthy volunteers and patients with valvular heart diseases: three linear classifiers (Logistic Regression, Support Vector Machine with a linear kernel, Ridge Regression) and four decision tree-based models (Random Forest, Bagged Trees, Gradient Boosting, Extreme Gradient Boosting).</div></div><div><h3>Methods:</h3><div>The study was carried out in two publicly available data sets with concurrent electrocardiographic (ECG), seismocardiographic (SCG), and gyrocardiographic (GCG) signals (Mechanocardiograms with ECG reference and An Open-access Database for the Evaluation of Cardio-mechanical Signals from Patients with Valvular Heart Diseases) that have 29 and 30 simultaneous recordings, respectively. All classifiers were trained on HRV indices calculated from concurrent ECG, SCG, and GCG signals from both datasets. Heartbeats in the SCG and GCG signals were detected as local maxima delayed from the locations of QRS complexes in the ECG signal by maximally 150 ms.</div></div><div><h3>Results:</h3><div>The results showed that linear and tree-based classifiers that work on HRV indices derived from ECG, SCG and GCG signals (accuracy of 0.9492 in the best case, 0.7627 in the worst case) could be a useful tool to differentiate between different heart diseases.</div></div><div><h3>Conclusions:</h3><div>The use of multimodal recordings provides more comprehensive information on the state of the cardiovascular system that, in combination with machine learning-based classifiers, could help diagnose cardiovascular conditions more efficiently.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"271 ","pages":"Article 108925"},"PeriodicalIF":4.8,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144764693","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":"Computational methods used to investigate atherosclerosis progression in coronary arteries: structural FEA, CFD or FSI","authors":"Vittorio Lissoni ,&nbsp;Giulia Luraghi ,&nbsp;Marco Stefanati ,&nbsp;Jose Felix Rodriguez Matas ,&nbsp;Francesco Migliavacca","doi":"10.1016/j.cmpb.2025.108959","DOIUrl":"10.1016/j.cmpb.2025.108959","url":null,"abstract":"<div><h3>Background and objectives</h3><div>In recent years, computational simulations have emerged as valuable tools for the evaluation of atherosclerosis progression in coronary anatomies, although only a few studies have utilized more realistic Fluid-Structure Interaction (FSI) simulations. This work aims to compare the results of Computational Fluid Dynamics (CFD), Structural Finite Element Analysis (structural FEA) and FSI simulations in order to assess differences in plaque progression indices estimation.</div></div><div><h3>Methods</h3><div>We performed structural FEA, CFD and FSI on five patient-specific epicardial coronary anatomies using the commercial software LS-Dyna. To account for the vessel pre-stress, the zero-pressure configuration was calculated for each anatomy with an inverse elastostatic algorithm. CFD, structural FEA and FSI simulations were performed applying boundary conditions based on physiological values.</div></div><div><h3>Results</h3><div>The comparison between structural FEA and FSI showed similar stress distribution and vessel expansions, with differences found only in the distal parts of the coronaries, where pressure reduction due to pressure loss affects the vessel walls. The elastic walls of the coronaries impact blood flow, resulting in a more disturbed flow. However, time averaged wall shear stress (TAWSS) and oscillatory shear index (OSI) distributions are similar across each coronary between CFD and FSI; TAWSS is slightly higher in CFD while OSI peaks are higher in FSI.</div></div><div><h3>Conclusion</h3><div>In conclusion, given the significantly higher computational costs of FSI, we believe that CFD and structural FEA offer a more practical and cost-effective approach, providing results comparable to those of FSI, making them preferable options.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"270 ","pages":"Article 108959"},"PeriodicalIF":4.9,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144614151","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":"Modeling changes in genetic heterogeneity using games with resources","authors":"Katarzyna Hajdowska ,&nbsp;Andrzej Swierniak ,&nbsp;Damian Borys","doi":"10.1016/j.cmpb.2025.108916","DOIUrl":"10.1016/j.cmpb.2025.108916","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>This study explores an extension of the classic Hawk and Dove evolutionary game model by considering the influence of environmental or external resources on the players’ fitness. This allows us to model the resulting heterogeneous population dynamics, which is of great importance for simulating cancer population growth and optimizing anti-cancer therapies.</div></div><div><h3>Methods:</h3><div>To model population heterogeneity, we are using an extension of classical spatial evolutionary game theory by introducing multidimensional spatial evolutionary games (MSEG). This allows for the study of genetic heterogeneity on a multidimensional lattice. The classic Hawk and Dove model is modified to reflect the impact of external resources. Various types and shapes of resource functions were included in the payoff matrix and then simulated to examine their impact on the model’s dynamics and population heterogeneity.</div></div><div><h3>Results:</h3><div>The results are presented in time-dependent plots for both mean-field and spatial models. Additionally, spatial 2D and 3D matrices are presented to show the spatial distribution of both phenotypes analyzed in the extended Hawk and Dove model. The results reveal significant differences between the mean-field and spatial models for the same parameter values. Furthermore, differences are observed when comparing models with different resource functions.</div></div><div><h3>Conclusion:</h3><div>The two-phenotype model was used to show the influence of external, time- and phenotype-specific resource functions on the dynamics of the game’s phenotypes. Moreover, the study highlights that spatial models, which provide more accurate information about population heterogeneity, can yield significantly different results compared to mean-field models.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"270 ","pages":"Article 108916"},"PeriodicalIF":4.9,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144613864","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":"Blind super-resolution for handheld ultrasound image: Two-stage degradation based unpaired deep learning","authors":"Zhencun Jiang ,&nbsp;Kangrui Ren ,&nbsp;Kefan Wang ,&nbsp;Zhongjie Wang","doi":"10.1016/j.cmpb.2025.108956","DOIUrl":"10.1016/j.cmpb.2025.108956","url":null,"abstract":"<div><h3>Background and Objective</h3><div>Handheld ultrasound devices are widely used in clinical diagnostics and examinations due to their portability. However, their imaging quality is often inferior to that of large-scale ultrasound devices due to hardware limitations.</div></div><div><h3>Methods</h3><div>To enhance the image quality of handheld ultrasound devices, a blind super-resolution method based on two-stage degradation is proposed. The first degradation stage, referred to as frequency probabilistic degradation, is designed to mitigate the structural distortion and texture loss commonly introduced by general probabilistic degradation. In this stage, high-quality ultrasound images acquired from large-scale ultrasound devices are decomposed into high-frequency and low-frequency components using wavelet transform. These two components are respectively processed with blur kernels and noise, both generated by neural networks, and then recombined to produce synthetic images. In the second degradation stage, Gaussian blur kernels and speckle noise are randomly generated and applied to the synthetic images, further degrading their quality and enhancing the diversity of the training samples. Additionally, recognizing that the general perceptual loss function is insufficient to capture the unique characteristics of ultrasound images, a new ultrasound perceptual loss function is introduced.</div></div><div><h3>Results</h3><div>Eventually, supervised learning is performed using the EDSR model on the synthetic images after two-stage degradation and high-quality images, and blind super-resolution of low-quality ultrasound images is realized.</div></div><div><h3>Conclusion</h3><div>Experiments are carried out on public datasets to demonstrate the proposed method, the experimental results show that the proposed method outperforms state-of-the-art techniques in terms of image quality improvement.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"270 ","pages":"Article 108956"},"PeriodicalIF":4.9,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144672666","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}