Chang Yuan , Xiaotong Yan , Kai Yue , Haixin Wang , Xilong Zhang , Dong Yan , Chao An
{"title":"Optimization of surgical parameters for liver tumor microwave ablation assisted by hydrodissection: Solution space features and active learning approach","authors":"Chang Yuan , Xiaotong Yan , Kai Yue , Haixin Wang , Xilong Zhang , Dong Yan , Chao An","doi":"10.1016/j.cmpb.2025.108967","DOIUrl":"10.1016/j.cmpb.2025.108967","url":null,"abstract":"<div><h3>Background and Objective</h3><div>Hydrodissection effectively protects adjacent tissues from thermal burns during microwave ablation of liver tumors. However, residual heat in the tumor and adipose tissues following the removal of the hydrodissection layer can result in thermal injuries to nearby organs. The objective of this study is to develop an optimization method for surgical parameters based on a solution space features and active learning approach to minimize the risk of postoperative thermal injury.</div></div><div><h3>Methods</h3><div>A method was developed in this study to optimize surgical parameters by constructing a solution space to prevent such injuries. A three-dimensional model of the liver and colon was reconstructed using medical imaging, and an electromagnetic-thermal-porous medium flow coupling model was established. Heat transfer and tissue damage characteristics during the surgical procedure were analyzed. Tumor ablation completeness, carbonization zone size, intestinal wall damage, and surgery duration were treated as objective functions and constraints for optimization. Expressions describing the solution space features were fitted based on simulation data. Optimal surgical parameters were determined by combining active learning with neural network training, incorporating individual patient physiological parameters. Ex vivo bovine liver ablation experiments were performed to validate the accuracy of the simulation model.</div></div><div><h3>Results</h3><div>The model effectively simulated tissue damage and temperature variations, with an error of approximately 5.34 % compared to ex vivo experiments. For each fitting of the solution space parameters, the goodness of fit exceeded 0.99. The optimization method theoretically achieved optimal solutions within 16 simulations, significantly reducing computation time. Additionally, the active learning algorithm reduced the root mean square error of model predictions by 25.7 %, keeping the prediction error below 6 %. Tumor conductivity was the most influential factor impacting optimization results.</div></div><div><h3>Conclusions</h3><div>This study can provide a theoretical framework for optimizing ablative treatment planning to reduce the risk of adjacent tissue burns in the therapy area after microwave ablation surgery.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"270 ","pages":"Article 108967"},"PeriodicalIF":4.9,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144694286","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}
Hongfei Sun , Jie Duan , Jiarui Zhu , Zhihui Li , Yufen Liu , Changhao Liu , Jie Li , Zihan Shi , Ningning Li , Jie Gong , Xiaokai Li , Zhongfei Wang , Dong Li , Mei Shi , Lina Zhao
{"title":"A novel multimodal adaptive delineation model for primary tumors and lymph node metastases in multi-center nasopharyngeal carcinoma radiotherapy","authors":"Hongfei Sun , Jie Duan , Jiarui Zhu , Zhihui Li , Yufen Liu , Changhao Liu , Jie Li , Zihan Shi , Ningning Li , Jie Gong , Xiaokai Li , Zhongfei Wang , Dong Li , Mei Shi , Lina Zhao","doi":"10.1016/j.cmpb.2025.108965","DOIUrl":"10.1016/j.cmpb.2025.108965","url":null,"abstract":"<div><h3>Purpose</h3><div>Nasopharyngeal carcinoma (NPC) features uncertain and complex gross tumor volume (GTV) distributions in terms of location, size, and shape. A novel deep learning model with adaptability was proposed to improve the accuracy of automatic GTV delineation for NPC primary and metastatic lesions.</div></div><div><h3>Methods and Materials</h3><div>This study comprised 529 retrospective cases and 4 prospective cases from multiple centers, all with CT and MRI modalities. GTV adaptive delineation was achieved via a conditional denoising diffusion model (DDPM) with \"inter-modal and intra-modal\" attention aware mechanism. During one training epoch, the inter-modal aware mechanism linked the frequency of potentially effective features at identical coordinates across multimodal images to tumor locations. The model progressively focused on high-frequency GTV-related features. Across multiple trainings, the intra-modal aware mechanism established the repeatability of feature extraction within each modality at identical coordinates, enhancing stable feature extraction. By leveraging both mechanisms, the model dynamically calibrated fusion weights for multimodal features, ascertaining each feature's significance in GTV identification based on its positional frequency. The GTV delineation accuracy was assessed using Dice Similarity Coefficient (DSC) ( %), 95 % Hausdorff Distance (HD95 %) (mm), and Mean Surface Distance (MSD) (mm) metrics.</div></div><div><h3>Results</h3><div>For the internal test set, the adaptive delineation model yielded mean (SD) results of 81.36 ± 2.14 % for DSC, 4.30 ± 2.14 mm for HD95 %, and 3.70 ± 1.84 mm for MSD. The external test set showed corresponding values of 77.43 ± 3.41 %, 6.07 ± 3.10 mm, and 4.31 ± 2.64 mm. Paired T-tests confirmed statistically significant differences between our model and the current SOTA models for automatic GTV delineation. The adaptive delineation model attained DSC accuracies of 83.45 ± 1.75 % for primary tumor and 73.43 ± 5.32 % for metastatic lesions, while achieving HD95 precisions of 4.28 ± 2.08 mm and 7.33 ± 3.45 mm, respectively. The MSD measurements were 2.73 ± 1.70 mm and 5.90 ± 3.18 mm, respectively. These results highlighted better delineation accuracy for primary tumors. In prospective dosimetry validation, the average dose difference within primary tumors, comparing automatically and manually delineated GTVs, was 0.36 Gy—less than the 0.52 Gy difference in lymph node metastases. This aligned with retrospective validation patterns.</div></div><div><h3>Conclusion</h3><div>The novel deep learning model demonstrated high accuracy and stability GTV automatic delineation for NPC cases, indicating its potential for clinical application in NPC radiotherapy at different centers.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"270 ","pages":"Article 108965"},"PeriodicalIF":4.9,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144662753","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}
Óscar Escudero-Arnanz , Antonio G. Marques , Inmaculada Mora-Jiménez , Joaquín Álvarez-Rodríguez , Cristina Soguero-Ruiz
{"title":"Early detection of Multidrug Resistance using Multivariate Time Series analysis and interpretable patient-similarity representations","authors":"Óscar Escudero-Arnanz , Antonio G. Marques , Inmaculada Mora-Jiménez , Joaquín Álvarez-Rodríguez , 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}
Gunn E. Vist , Trine Husøy , Michael Guy Diemar , Hubert Dirven , Erwin L. Roggen , Maria E. Kalyva
{"title":"ExtractPDF: A data extraction tool for scientific papers applied to a systematic scoping review in public health","authors":"Gunn E. Vist , Trine Husøy , Michael Guy Diemar , Hubert Dirven , Erwin L. Roggen , Maria E. Kalyva","doi":"10.1016/j.cmpb.2025.108962","DOIUrl":"10.1016/j.cmpb.2025.108962","url":null,"abstract":"<div><h3>Background and Objectives</h3><div>Systematic reviews are widely used to identify the evidence and get an overview of the available knowledge for various questions related to public health and medical topics. They can provide a summary of all the available data and can be used to make knowledge-based decisions about policy, practice, and academic research. The conduct of systematic reviews can often be time‐consuming and costly.</div></div><div><h3>Methods</h3><div>We have developed a command-line based code in R to extract data in an automated manner from full-text scientific papers. ExtractPDF is a data extraction tool/software that provides a reliable computational workflow for extracting words or combinations of words from numerous portable document format (PDF) files.</div></div><div><h3>Results</h3><div>The software was applied to extract information from 299 papers that have been screened as included for a published systematic scoping review study within the field of risk assessment in public health. The output of the software is tables of extracted information per type of information of interest per PDF file. The tables were used during the data extraction stage as a second reviewer alongside a human reviewer to assist and/or validate data extraction items.</div></div><div><h3>Conclusions</h3><div>ExtractPDF tool has a novel pipeline architecture to automate extraction of information from unstructured format types, such as PDF files. ExtractPDF tool assisted in expediting the task of data extraction stage and reducing human related resources as well as errors. The tool’s performance and reliability were found to be very good with metrics of averagely 0.89 for precision, 0.92 for recall, 0.86 for accuracy and 0.91for F1-score.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"270 ","pages":"Article 108962"},"PeriodicalIF":4.9,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144633138","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, 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":"Malignant melanoma fractional-order mathematical model with stabilized fuzzy sliding mode control","authors":"David Amilo, Khadijeh Sadri, Evren Hincal","doi":"10.1016/j.cmpb.2025.108912","DOIUrl":"10.1016/j.cmpb.2025.108912","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Malignant melanoma, an aggressive form of skin cancer, poses significant challenges due to its rapid progression, metastatic potential, and resistance to therapies. This study aims to develop a fractional-order mathematical model capturing melanoma dynamics (tumor-immune interactions, extracellular matrix remodeling, nutrient dynamics) and introduce a Stabilized Fuzzy Sliding Mode Control (SFSMC) strategy to suppress tumor growth and restore microenvironmental homeostasis.</div></div><div><h3>Methods:</h3><div>A fractional-order model was derived using Caputo derivatives to incorporate memory effects and long-range dependencies. The SFSMC combines sliding mode control with fuzzy logic to manage uncertainties. Theoretical analysis included well-posedness, stability (via Lyapunov functions), and computation of the reproduction number <span><math><msub><mrow><mi>R</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span>. Numerical simulations were performed using a predictor–corrector method with parameters calibrated from clinical data.</div></div><div><h3>Results:</h3><div>The model demonstrated stability when <span><math><mrow><msub><mrow><mi>R</mi></mrow><mrow><mn>0</mn></mrow></msub><mo><</mo><mn>1</mn></mrow></math></span>, indicating tumor suppression. SFSMC reduced tumor cell populations by 78% and circulating tumor cells by 65% while improving immune response (45% increase in immune cells) and nutrient availability (30% recovery). Sensitivity analysis revealed <span><math><msub><mrow><mi>R</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span> is mostly influenced by tumor growth rate, natural degradation rate of extracellular matrix (ECM), rate of ECM degradation by tumor cells, and ECM production rate, suggesting their potential role in suppressing tumor growth.</div></div><div><h3>Conclusions:</h3><div>The fractional-order framework and SFSMC offer a robust approach to modeling and controlling melanoma, with potential clinical implications for adaptive therapy.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"270 ","pages":"Article 108912"},"PeriodicalIF":4.9,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144613865","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}
Jing Zheng , Minwei Zhou , Zhehao Zhou , Jieyi Ge , Hang Chen , Xiaobai Li , Wanlin Chen , Shulin Chen
{"title":"Handwritten signature verification using a wearable surface-EMG armband","authors":"Jing Zheng , Minwei Zhou , Zhehao Zhou , Jieyi Ge , Hang Chen , Xiaobai Li , Wanlin Chen , 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}
Huanfan Su , Suyan Bi , Xiangshang Sun , Qingqing Yuan , Meiling Xu , Zhitao Dai
{"title":"A novel homogeneity index based on the area integration of dose volume histogram","authors":"Huanfan Su , Suyan Bi , Xiangshang Sun , Qingqing Yuan , Meiling Xu , Zhitao Dai","doi":"10.1016/j.cmpb.2025.108935","DOIUrl":"10.1016/j.cmpb.2025.108935","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>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.</div></div><div><h3>Methods:</h3><div>The novel HIs comprise three parameters: a cold spot index (<span><math><mrow><mi>H</mi><msub><mrow><mi>I</mi></mrow><mrow><mi>c</mi></mrow></msub></mrow></math></span>), a hot spot index (<span><math><mrow><mi>H</mi><msub><mrow><mi>I</mi></mrow><mrow><mi>h</mi></mrow></msub></mrow></math></span>), and a global homogeneity index (<span><math><mrow><mi>H</mi><msub><mrow><mi>I</mi></mrow><mrow><mi>g</mi></mrow></msub></mrow></math></span>), as defined in Eqs. <span><span>(1)</span></span>-<span><span>(4)</span></span>. <span><math><mrow><mi>H</mi><msub><mrow><mi>I</mi></mrow><mrow><mi>c</mi></mrow></msub></mrow></math></span> and <span><math><mrow><mi>H</mi><msub><mrow><mi>I</mi></mrow><mrow><mi>h</mi></mrow></msub></mrow></math></span> 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.</div></div><div><h3>Results:</h3><div>The novel HIs effectively differentiated dose homogeneity across various PIDLs, with values decreasing as PIDL increased. The maximum variations in the mean <span><math><mrow><mi>H</mi><msub><mrow><mi>I</mi></mrow><mrow><mi>c</mi></mrow></msub></mrow></math></span>, <span><math><mrow><mi>H</mi><msub><mrow><mi>I</mi></mrow><mrow><mi>h</mi></mrow></msub></mrow></math></span>, and <span><math><mrow><mi>H</mi><msub><mrow><mi>I</mi></mrow><mrow><mi>g</mi></mrow></msub></mrow></math></span> 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 (<span><math><mrow><mi>H</mi><msub><mrow><mi>I</mi></mrow><mrow><mi>c</mi></mrow></msub></mrow></math><","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 , Zhenmiao Deng , Du Li , 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 , Zhifang Fan , Jingjing Cao , 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}