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Population-scale modeling of the natural knee: automated segmentation, finite element simulation, and machine learning prediction of time-series joint mechanics. 自然膝关节的人口规模建模：时间序列关节力学的自动分割、有限元模拟和机器学习预测。
IF 1.9 4区 医学
Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2026-09-03 DOI: 10.1080/10255842.2026.2724359
Kalin D Gibbons, Vahid Malbouby, Ethan J Cooper, Amanda K Ivy, Clare K Fitzpatrick
{"title":"Population-scale modeling of the natural knee: automated segmentation, finite element simulation, and machine learning prediction of time-series joint mechanics.","authors":"Kalin D Gibbons, Vahid Malbouby, Ethan J Cooper, Amanda K Ivy, Clare K Fitzpatrick","doi":"10.1080/10255842.2026.2724359","DOIUrl":"https://doi.org/10.1080/10255842.2026.2724359","url":null,"abstract":"<p><p>This study combined automated medical image segmentation, hexahedral meshing, and dynamic finite element stance-phase gait simulations with statistical shape modeling and supervised learning for 483 natural knees from the Osteoarthritis Initiative. Ridge regression and recurrent neural networks were trained to predict 38 time-series kinematic, loading, contact, and soft-tissue outputs from anatomic features. The best model, a bidirectional LSTM, achieved an average 1σ-normalized RMSE of 0.45, with inference in seconds. This population-scale framework enables rapid, subject-specific estimates of knee mechanics from imaging alone, supporting future clinical translation.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-16"},"PeriodicalIF":1.9,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148889220","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
MAIDTA: an interpretable attention-based multi-modal model for drug-target affinity prediction. MAIDTA：一个可解释的基于注意力的药物靶点亲和力预测多模态模型。
IF 1.9 4区 医学
Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2026-09-03 DOI: 10.1080/10255842.2026.2727109
Minghao Li, Minglei Dong, Hongle Li, Yuanxing Peng, Zhen Li
{"title":"MAIDTA: an interpretable attention-based multi-modal model for drug-target affinity prediction.","authors":"Minghao Li, Minglei Dong, Hongle Li, Yuanxing Peng, Zhen Li","doi":"10.1080/10255842.2026.2727109","DOIUrl":"https://doi.org/10.1080/10255842.2026.2727109","url":null,"abstract":"<p><p>High-performance computer-aided drug design is a promising field, in which drug-target affinity (DTA) prediction serves as a core step to reduce R&D costs and improve efficiency. However, current DTA methods suffer from insufficient multi-modal representation and lack interpretability. We propose MAIDTA, an interpretable attention-based multi-modal model integrating sequence and graph dual-modal representations of drugs and proteins. An attention-driven binding region module quantifies drug atom-protein residue interaction strength, and a cross-scale feature fusion module integrates multi-scale features. Experiments on Davis and KIBA show MAIDTA outperforms state-of-the-art models with higher accuracy, robustness, and interpretability, offering new insights for drug discovery.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-18"},"PeriodicalIF":1.9,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148889273","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 novel approach to dyslexia detection: combining EEG data and cognitive tasks with advanced machine learning. 一种新的阅读障碍检测方法：将脑电图数据和认知任务与先进的机器学习相结合。
IF 1.9 4区 医学
Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2026-09-02 DOI: 10.1080/10255842.2026.2724550
Tabassum Gull Jan, Sajad Mohammad Khan, Sajid Yousuf Bhat, Zaid Ahmad Wani
{"title":"A novel approach to dyslexia detection: combining EEG data and cognitive tasks with advanced machine learning.","authors":"Tabassum Gull Jan, Sajad Mohammad Khan, Sajid Yousuf Bhat, Zaid Ahmad Wani","doi":"10.1080/10255842.2026.2724550","DOIUrl":"https://doi.org/10.1080/10255842.2026.2724550","url":null,"abstract":"<p><p>Existing dyslexia detection methods typically rely on either EEG or screening tests. This study introduces a multimodal two-stage methodology for dyslexia detection that integrates both EEG signals and screening test data, using spectral features (Shannon entropy and Power Spectral Density) across various frequency bands. The novelty of the proposed approach stems in collecting firsthand paired EEG recordings and screening data from patients, and combining them <i>via</i> two-stage framework, which improves predictive accuracy and yields a comprehensive neurological assessment. Several machine learning models were trained and, upon evaluation, the Random Forest classifier performed best, achieving 98.36% accuracy, 99.2% precision, and 97.63% recall using only EEG data. When both EEG and screening data were combined, performance improved to 98.69% accuracy, 98.90% precision, and 98.90% recall, significantly outperforming existing methodologies, with results validated <i>via</i> statistical tests. Dyslexic individuals exhibited higher delta and theta energy, particularly in the central, parietal, and temporal regions, indicating deficits in phonological processing and working memory. Lower Shannon entropy values in dyslexics suggest increased randomness in neural activity and reduced processing ability. Feature importance analysis identified delta, theta, and gamma energies as the most discriminative features. This study highlights the effectiveness of multimodal data fusion in enhancing dyslexia detection, improving early diagnosis, and facilitating interventions for children aged 5-9 years. These results underscore the potential of advanced spectral feature extraction and robust machine learning in dyslexia diagnostics, setting a benchmark in the field, with future work focusing on validating these results using larger datasets and exploring additional predictive features.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-28"},"PeriodicalIF":1.9,"publicationDate":"2026-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148881997","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-branch heterogeneous network of exploiting complementary multi-view features for decoding finger motor imagery EEG. 利用互补多视角特征的多分支异构网络解码手指运动意象脑电。
IF 1.9 4区 医学
Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2026-09-02 DOI: 10.1080/10255842.2026.2726433
Kun Yang, Yubin Hu, Renjian Zheng, Jing Xu, Ning Wang, Jianhai Zhang, Yong Peng, Wanzeng Kong
{"title":"Multi-branch heterogeneous network of exploiting complementary multi-view features for decoding finger motor imagery EEG.","authors":"Kun Yang, Yubin Hu, Renjian Zheng, Jing Xu, Ning Wang, Jianhai Zhang, Yong Peng, Wanzeng Kong","doi":"10.1080/10255842.2026.2726433","DOIUrl":"https://doi.org/10.1080/10255842.2026.2726433","url":null,"abstract":"<p><p>In contrast to general motor imagery involving large body parts, research on finger motor imagery is very scarce. Due to more refined motor functions, the decoding of finger motor imagery is more arduous and has lower accuracy than that of general motor imagery. In order to improve the decoding accuracy of finger motor imagery, this paper proposes the problem of identifying complementary multi-view decoding features and the problem of electrode channel difference of convolution kernels. A novel multi-branch heterogeneous network (MBHN) consisting of three groups of diversified branches is proposed to effectively extract and exploit complementary features of raw-view, frequency-decomposition-view and wavelet-view. Moreover, the channel adaptive kernel (CAK) module is proposed as a solution to the problem of channel difference of convolution kernels. The experimental results on the public finger motor imagery dataset show that our MBHN model achieves the state-of-the-art decoding accuracy of 58.49%. Additionally, the integration of the auxiliary supervision mechanism and the hybrid loss function is a very effective approach to fully leverage the complementarity of multi-view deep features. Our code is publicly available at https://github.com/ykhdu/MBHN.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-15"},"PeriodicalIF":1.9,"publicationDate":"2026-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148881991","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
Identification of immune-related biomarkers and immune infiltrations of intracranial aneurysm with subarachnoid hemorrhage by machine-learning strategies. 应用机器学习策略识别颅内动脉瘤伴蛛网膜下腔出血的免疫相关生物标志物和免疫浸润。
IF 1.9 4区 医学
Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2026-09-01 Epub Date: 2025-04-23 DOI: 10.1080/10255842.2025.2495250
Xiao Jin, Xiang Zhao
{"title":"Identification of immune-related biomarkers and immune infiltrations of intracranial aneurysm with subarachnoid hemorrhage by machine-learning strategies.","authors":"Xiao Jin, Xiang Zhao","doi":"10.1080/10255842.2025.2495250","DOIUrl":"10.1080/10255842.2025.2495250","url":null,"abstract":"<p><p><b>Background:</b> Subarachnoid hemorrhage (SAH) risk increases with intracranial aneurysms (IA), but their relationship remains unclear. <b>Methods:</b> We explored SAH-IA links using machine learning and bioinformatics, identifying 66 IA-related SAH genes. KEGG analysis highlighted pathways like NF-κB, TNF, and COVID-19. <b>Results:</b> Two immune-related genes (ZNF281, LRRN3) were identified, and a ceRNA network was constructed. Ten potential SAH-IA drugs were screened via CMAP. <b>Conclusion:</b> ZNF281 and LRRN3 may regulate immune pathways (T cells, NK cells, macrophages), influencing IA-related SAH development, and could serve as therapeutic targets.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"2808-2820"},"PeriodicalIF":1.9,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144065181","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 approach on classification of medical condition drug reviews using multiple traditional and neural network-based models. 基于传统和神经网络模型的药物审评分类比较研究。
IF 1.9 4区 医学
Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2026-09-01 Epub Date: 2026-04-01 DOI: 10.1080/10255842.2026.2641195
Honglei Fu
{"title":"A comparative approach on classification of medical condition drug reviews using multiple traditional and neural network-based models.","authors":"Honglei Fu","doi":"10.1080/10255842.2026.2641195","DOIUrl":"10.1080/10255842.2026.2641195","url":null,"abstract":"<p><p>This paper uses national language processing techniques to perform sentiment analysis, classifying user-written drug reviews for pain, depression, and birth control. Six machine learning (ML)-based models are employed for analysis, including neural networks (NNs) like the simple NN and convolutional neural networks (CNNs), as well as more conventional models like Adaptive Boosting (AdaBoost), Random Forest (RF), Decision Tree (DT), and Support Vector Classification (SVC). Tokenization used TF-IDF for traditional models and word embeddings for CNN and a simple NN. Using precision, accuracy, recall, and F1-score, CNN achieved the highest F1-score (0.991), followed by NN (0.985) and RF (0.980).</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"2995-3005"},"PeriodicalIF":1.9,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147596037","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
Evaluation of in vivo subject-specific mechanical behavior of lamina cribrosa in subjects with and without glaucoma. 青光眼患者和非青光眼患者筛板体内特异性力学行为的评价。
IF 1.9 4区 医学
Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2026-09-01 DOI: 10.1080/10255842.2026.2725230
Soumaya Ouhsousou, Lucy Q Shen, Chhavi Saini, Mengyu Wang, John C Brigham
{"title":"Evaluation of in vivo subject-specific mechanical behavior of lamina cribrosa in subjects with and without glaucoma.","authors":"Soumaya Ouhsousou, Lucy Q Shen, Chhavi Saini, Mengyu Wang, John C Brigham","doi":"10.1080/10255842.2026.2725230","DOIUrl":"https://doi.org/10.1080/10255842.2026.2725230","url":null,"abstract":"<p><p>Subject-specific finite element models were developed for 20 primary open-angle glaucoma (POAG) and 20 control eyes using swept-source OCT. The spatial distribution of tensile and compressive strains was computed across the lamina cribrosa. POAG subjects exhibited significantly higher compressive (-0.84 ± 0.15 vs -0.54 ± 0.16) and tensile strains (0.72 ± 0.12 vs 0.54 ± 0.08, p-value < 0.0001). Localized strains within the temporal and superior regions were identified as primary mechanical features distinguishing both among POAG subgroups and between POAG and control subjects. Significant correlation was found between features of strain distribution and visual field mean deviation (p-value < 0.05), defining POAG subgroups that may reflect different POAG mechanical phenotypes.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-13"},"PeriodicalIF":1.9,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148876649","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
Artificial neural network-driven optimization and mechanical energy evaluation of silica nanoparticle-enhanced Kevlar-Polypropylene socket composites for advanced biomedical implants. 纳米二氧化硅增强凯夫拉-聚丙烯嵌套复合材料的人工神经网络优化及机械能评价。
IF 1.9 4区 医学
Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2026-09-01 DOI: 10.1080/10255842.2026.2723107
Megavannan Mani
{"title":"Artificial neural network-driven optimization and mechanical energy evaluation of silica nanoparticle-enhanced Kevlar-Polypropylene socket composites for advanced biomedical implants.","authors":"Megavannan Mani","doi":"10.1080/10255842.2026.2723107","DOIUrl":"https://doi.org/10.1080/10255842.2026.2723107","url":null,"abstract":"<p><p>This study develops lightweight Kevlar-polypropylene hybrid composites for prosthetic socket applications using experimental and machine-learning approaches. Laminates containing 0-2 wt.% silica nanoparticles were fabricated and evaluated for mechanical and puncture-energy performance. The 1.5 wt.% silica composite showed the best overall response, reaching a puncture load of 3459.1 N, tensile strength of 225 MPa, flexural strength of 157.24 MPa, interlaminar shear strength of 34 MPa, impact strength of 1813.6 J/m, and net energy absorption of 18.44 J. An artificial neural network achieved R² = 0.983, confirming reliable predictive capability and supporting the composite's suitability for advanced prosthetic applications.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-35"},"PeriodicalIF":1.9,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148876695","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
Numerical evaluations of different foot arch types: effects of loading on plantar pressure distribution and bone stress. 不同足弓类型的数值评估：载荷对足底压力分布和骨应力的影响。
IF 1.9 4区 医学
Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2026-09-01 Epub Date: 2025-05-07 DOI: 10.1080/10255842.2025.2499620
Yue Sun, Yuanzhu Sun, Shichen Zhang, Mei-Ying Kwan, Kit-Lun Yick, Bingfei Gu
{"title":"Numerical evaluations of different foot arch types: effects of loading on plantar pressure distribution and bone stress.","authors":"Yue Sun, Yuanzhu Sun, Shichen Zhang, Mei-Ying Kwan, Kit-Lun Yick, Bingfei Gu","doi":"10.1080/10255842.2025.2499620","DOIUrl":"10.1080/10255842.2025.2499620","url":null,"abstract":"<p><p>This study aimed to investigate the biomechanical variations among different types of foot arches. Plantar pressure and foot bone stress of three arch types under bipedal standing, single foot standing and running mid-stance were investigated by simulation. Compared with the normal foot, the stress in the hind foot area of high arch foot increased significantly, while the contact area of the low arch foot increased. Bone stress in high arch increased more significantly when changed from bipedal standing to single foot standing or running. The results support the link between abnormal arches and foot pain, providing design basis for orthopedic insoles.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"2937-2950"},"PeriodicalIF":1.9,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144055651","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
Effect of foot-shaped bionic shoes on ground reaction forces and foot stress at various running speeds. 仿脚鞋对不同跑步速度下地面反作用力和足部应力的影响。
IF 1.9 4区 医学
Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2026-09-01 Epub Date: 2025-04-15 DOI: 10.1080/10255842.2025.2490139
Shunxiang Gao, Dong Sun, Yang Song, Xuanzhen Cen, Qiaolin Zhang, Zixiang Gao, Zhiyi Zheng, Monèm Jemni, Yaodong Gu
{"title":"Effect of foot-shaped bionic shoes on ground reaction forces and foot stress at various running speeds.","authors":"Shunxiang Gao, Dong Sun, Yang Song, Xuanzhen Cen, Qiaolin Zhang, Zixiang Gao, Zhiyi Zheng, Monèm Jemni, Yaodong Gu","doi":"10.1080/10255842.2025.2490139","DOIUrl":"10.1080/10255842.2025.2490139","url":null,"abstract":"<p><p>This study examined ground reaction forces(GRFs) and bone stress differences between bionic running shoes (with foot-mimicking soles) and traditional shoes during running.Sixteen experienced male runners ran at 10, 12, and 14 km/h in both shoe types. Two-way ANOVA and SPM1d showed that bionic shoes had significantly lower peak propulsive but higher peak braking forces than traditional shoes.Bionic shoes also exhibited lower vertical forces in early stance and altered anterior-posterior forces patterns in late stance; finite element analysis indicated lower metatarsal stress in the bionic midsoles. These findings provide insights for designing footwear to prevent running injuries.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"2775-2785"},"PeriodicalIF":1.9,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144051169","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
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