Computer Methods in Biomechanics and Biomedical Engineering最新文献_第10页

A novel intramedullary nail design of intertrochanteric fracture fixation improved by proximal femoral nail antirotation. 一种新型股骨近端防旋转髓内钉固定股骨粗隆间骨折的方法。

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-02-01 Epub Date: 2023-11-25 DOI: 10.1080/10255842.2023.2286917

Ze She, Fan Yang, Siyuan Zhang, Liang Yang, Xin Wang

引用次数: 0

Dynamic properties of deterministic and stochastic SIIIRS models with multiple viruses and saturation incidences. 具有多病毒和饱和发生率的确定性和随机SIIIRS模型的动态特性。

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-02-01 Epub Date: 2023-11-28 DOI: 10.1080/10255842.2023.2286213

Xiaoyu Li, Zhiming Li, Shuzhen Ding

引用次数: 0

Development of a three-dimensional muscle-driven lower limb model developed using an improved CFD-FE method. 利用改进的CFD-FE方法开发了一个三维肌肉驱动的下肢模型。

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-02-01 Epub Date: 2023-11-28 DOI: 10.1080/10255842.2023.2286921

Luming Feng, Qinglin Duan, Rongwu Lai, Wenhang Liu, Xiaoshuang Song, Yongtao Lyu

{"title":"Development of a three-dimensional muscle-driven lower limb model developed using an improved CFD-FE method.","authors":"Luming Feng, Qinglin Duan, Rongwu Lai, Wenhang Liu, Xiaoshuang Song, Yongtao Lyu","doi":"10.1080/10255842.2023.2286921","DOIUrl":"10.1080/10255842.2023.2286921","url":null,"abstract":"Analysis of the musculoskeletal movements (gait analysis) is needed in many scenarios. The in vivo method has some difficulties. For example, recruiting human subjects for the gait analysis is challenging due to many issues. In addition, when plenty of subjects are required, the follow-up experiments take a long period and the dropout of subjects always occurs. An efficient and reliable in silico simulation platform for gait analysis has been desired for a long time. Therefore, a technique using three-dimensional (3D) muscle modeling to drive the 3D musculoskeletal model was developed and the application of the technique in the simulation of lower limb movements was demonstrated. A finite element model of the lower limb with anatomically high fidelity was developed from the MRI data, where the main muscles, the bones, the subcutaneous tissues, and the skin were reconstructed. To simulate the active behavior of 3D muscles, an active, fiber-reinforced hyperelastic muscle model was developed using the user-defined material (VUMAT) model. Two typical movements, that is, hip abduction and knee lifting, were simulated by activating the responsible muscles. The results show that it is reasonable to use the improved CFD-FE method proposed in the present study to simulate the active contraction of the muscle, and it is feasible to simulate the movements by activating the relevant muscles. The results from the present technique closely match the physiological scenario and thus the technique developed has a great potential to be used in the in silico human simulation platform for many purposes.","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"314-325"},"PeriodicalIF":1.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138452996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-scale EMG classification with spatial-temporal attention for prosthetic hands. 假手的时空注意多尺度肌电图分类。

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-02-01 Epub Date: 2023-11-30 DOI: 10.1080/10255842.2023.2287419

Emimal M, W Jino Hans, Inbamalar T M, N Mahiban Lindsay

引用次数: 0

Identifying the cancer-associated fibroblast signature to predict the prognosis and immunotherapy response in patients with lung squamous cell carcinoma. 鉴别癌症相关成纤维细胞特征以预测肺鳞状细胞癌患者的预后和免疫治疗反应。

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-02-01 Epub Date: 2023-11-28 DOI: 10.1080/10255842.2023.2287418

Yinhui Zhu, Yingqun Zhu, Sirui Chen, Qian Cai

{"title":"Identifying the cancer-associated fibroblast signature to predict the prognosis and immunotherapy response in patients with lung squamous cell carcinoma.","authors":"Yinhui Zhu, Yingqun Zhu, Sirui Chen, Qian Cai","doi":"10.1080/10255842.2023.2287418","DOIUrl":"10.1080/10255842.2023.2287418","url":null,"abstract":"Cancer-associated fibroblasts (CAFs) are an important component of the tumor microenvironment that contribute toward the development of tumors. This study aimed to establish a new algorithm based on CAF scores to predict the prognosis and immunotherapy response in patients with lung squamous cell carcinoma (LUSC). The RNA-seq data of LUSC patients were obtained from two databases and merged after removing inter-batch differences. The CAF-related data for each sample were obtained through three different algorithms. Consistency cluster analysis was performed to obtain different CAF clusters, which were analyzed to identify differentially expressed genes. These were subjected to uniform cluster analysis to obtain different gene clusters. The Boruta algorithm was used to calculate the CAF score. Three CAF clusters and two gene clusters were obtained, all of which differed in their patient prognoses and the content of infiltrating immune cells. Patients with high CAF scores exhibited worse overall survival, higher expression of biomarkers related to immune checkpoints and immune activity, and lower tumor mutation burden. The CAF score could also predict the immunotherapy response of patients. This study suggests that the CAF score can accurately predict the prognosis and immunotherapy response of LUSC patients.","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"326-336"},"PeriodicalIF":1.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138446832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Discovering effect of intuitionistic fuzzy transformation in multi-layer perceptron for heart disease prediction: a study. 直觉模糊变换在多层感知器心脏病预测中的应用研究。

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-02-01 Epub Date: 2023-11-27 DOI: 10.1080/10255842.2023.2284095

Chandan Pan, Tamalika Chaira, Ajoy Kumar Ray

{"title":"Discovering effect of intuitionistic fuzzy transformation in multi-layer perceptron for heart disease prediction: a study.","authors":"Chandan Pan, Tamalika Chaira, Ajoy Kumar Ray","doi":"10.1080/10255842.2023.2284095","DOIUrl":"10.1080/10255842.2023.2284095","url":null,"abstract":"Cardiovascular disease (CVD) is the one of the most fatal diseases in the world we have seen in last two decades. For heart disease detection, imprecision in clinical parameters may occur due to error in taking readings or in measuring devices or environmental conditions etc. Hence, introducing fuzzy set theory in feature engineering may give better results as it deals with uncertainty. But in fuzzy set theory, only one uncertainty is considered, which is membership degree or degree of belongingness. Intuitionistic fuzzy set (IFS) considers two uncertainties - membership degree and non-membership degree and so IFS may provide efficient results. To reduce the risk of heart disease, an advanced deep learning algorithm will play a significant role in heart disease prediction that will help physicians to diagnose early. In this paper, we have established a transformation of patient features using i) intuitionistic fuzzy parameters, where Sugeno-type fuzzy complement is used and ii) fuzzy parameters, where gamma membership function is used. These transformed attributes are applied on Deep Learning prediction algorithm as Multi-layer Perceptron (MLP). The novelty of the paper lies from feature transformation to deep learning. It is observed that intuitionistic fuzzy transformation approach, keeping model parameters intact, significantly outperforms non-fuzzy method and gammy fuzzy Transformation, which is reflected in evaluation mechanisms.","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"197-211"},"PeriodicalIF":1.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138446831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing pancreatic cancer classification through dynamic weighted ensemble: a game theory approach. 通过动态加权集合增强胰腺癌分类:一种博弈论方法。

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-02-01 Epub Date: 2023-11-20 DOI: 10.1080/10255842.2023.2281277

Dhanasekaran S, Silambarasan D, Vivek Karthick P, Sudhakar K

{"title":"Enhancing pancreatic cancer classification through dynamic weighted ensemble: a game theory approach.","authors":"Dhanasekaran S, Silambarasan D, Vivek Karthick P, Sudhakar K","doi":"10.1080/10255842.2023.2281277","DOIUrl":"10.1080/10255842.2023.2281277","url":null,"abstract":"The significant research carried out on medical healthcare networks is giving computing innovations lots of space to produce the most recent innovations. Pancreatic cancer, which ranks among of the most common tumors that are thought to be fatal and unsuspected since it is positioned in the region of the abdomen beyond the stomach and can't be adequately treated once diagnosed. In radiological imaging, such as MRI and CT, computer-aided diagnosis (CAD), quantitative evaluations, and automated pancreatic cancer classification approaches are routinely provided. This study provides a dynamic weighted ensemble framework for pancreatic cancer classification inspired by game theory. Grey Level Co-occurrence Matrix (GLCM) is utilized for feature extraction, together with Gaussian kernel-based fuzzy rough sets theory (GKFRST) for feature reduction and the Random Forest (RF) classifier for categorization. The ResNet50 and VGG16 are used in the transfer learning (TL) paradigm. The combination of the outcomes from the TL paradigm and the RF classifier paradigm is suggested using an innovative ensemble classifier that relies on the game theory method. When compared with the current models, the ensemble technique considerably increases the pancreatic cancer classification accuracy and yields exceptional performance. The study improves the categorization of pancreatic cancer by using game theory, a mathematical paradigm that simulates strategic interactions. Because game theory has been not frequently used in the discipline of cancer categorization, this research is distinctive in its methodology.","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"145-169"},"PeriodicalIF":1.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138048413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Diagonal loading common spatial patterns with Pearson correlation coefficient based feature selection for efficient motor imagery classification.

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-01-31 DOI: 10.1080/10255842.2025.2457122

Hanaa S Ali, Asmaa I Ismail, El-Sayed M El-Rabaie, Fathi E Abd El-Samie

{"title":"Diagonal loading common spatial patterns with Pearson correlation coefficient based feature selection for efficient motor imagery classification.","authors":"Hanaa S Ali, Asmaa I Ismail, El-Sayed M El-Rabaie, Fathi E Abd El-Samie","doi":"10.1080/10255842.2025.2457122","DOIUrl":"https://doi.org/10.1080/10255842.2025.2457122","url":null,"abstract":"The conversion of a person's intentions into device commands through the use of brain-computer interface (BCI) is a feasible communication method for individuals with nervous system disorders. While common spatial pattern (CSP) is commonly used for feature extraction in BCIs, it has limitations. It is known for its susceptibility to noise and tendency to overfit. Moreover, high-dimensional, and irrelevant features can make it harder for a classifier to learn effectively. To address these challenges, exploring potential solutions is crucial. This paper introduces Regularized CSP with diagonal loading (DL-CSP) and Pearson correlation coefficient (PCC) based feature selection to extract the most discriminative motor imagery EEG (MI-EEG) features. Three classifiers in an ensemble are considered; bidirectional long short-term memory (Bi-LSTM), K-nearest neighbors (KNN) and naïve Bayes (NB). Decision level fusion through majority voting is exploited to leverage diverse perspectives and increase the overall system robustness. Experiments have been implemented using three publicly available datasets for MI classification; BCI competition IV-IIA (data-1), BCI Competition III-IVa (data-2), and a stroke patients' dataset (data-3). The accuracy achieved, according to the results, is 86.96% for data-1, 91.70% for data-2, and 85.75% for data-3. These percentages outperform the accuracy achieved by any state-of-the-art techniques.","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-15"},"PeriodicalIF":1.7,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143069369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Non-Newtonian lacuno-canalicular fluid flow in bone altered by mechanical loading and magnetic field.

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-01-31 DOI: 10.1080/10255842.2025.2453924

Rakesh Kumar, Suja Laxmikant Vajire, Abhishek Kumar Tiwari, Dharmendra Tripathi

{"title":"Non-Newtonian lacuno-canalicular fluid flow in bone altered by mechanical loading and magnetic field.","authors":"Rakesh Kumar, Suja Laxmikant Vajire, Abhishek Kumar Tiwari, Dharmendra Tripathi","doi":"10.1080/10255842.2025.2453924","DOIUrl":"10.1080/10255842.2025.2453924","url":null,"abstract":"As humans age, they experience deformity and a decrease in their bone strength, such brittleness in the bones ultimately lead to bone fracture. Magnetic field exposure combined with physical exercise may be useful in mitigating age-related bone loss by improving the canalicular fluid motion within the bone's lacuno-canalicular system (LCS). Nevertheless, an adequate amount of fluid induced shear stress is necessary for the bone mechano-transduction and solute transport in the case of brittle bone diseases. The underlying mechanisms of how magnetic fields, in combination to mechanical loading, affects the canalicular fluid motion still need to be explored. Accordingly, this study aims to develop a computer model to investigate the role of magnetic fields on loading-induced canalicular fluid flow in a curvy lacunar canalicular space with irregular osteocyte cell processes and walls. Moreover, this study considers canalicular fluid as non-Newtonian fluid, i.e., Jeffery fluid. In addition, a machine learning model was further employed for the estimation of parameters which significantly influence the canalicular fluid flow in response to loading and magnetic field. The results show that static magnetic field modulates the loading-induced canalicular fluid flow. Additionally, present study accelerates the fluid induced wall shear stress in case of osteoporosis.","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-19"},"PeriodicalIF":1.7,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143069382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A comparative analysis of CNNs and LSTMs for ECG-based diagnosis of arrythmia and congestive heart failure.

IF 1.7 4区医学

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-01-30 DOI: 10.1080/10255842.2025.2456487

Nitish Katal, Hitendra Garg, Bhisham Sharma

引用次数: 0