生物医学工程学杂志最新文献

{"title":"[Application of hybrid attention guided dual-path residual learning in magnetic resonance imaging diagnosis].","authors":"Weipeng Zhu, Xianfa Cai, Yong Liu","doi":"10.7507/1001-5515.202510033","DOIUrl":"10.7507/1001-5515.202510033","url":null,"abstract":"<p><p>Multimodal magnetic resonance imaging (MRI) often faces challenges such as insufficient model generalization ability and high misdiagnosis rates in intelligent neuroimaging diagnosis. Existing methods, including single-path classification networks such as residual networks and EfficientNet, and segmentation-oriented workflow models such as U-Net, have the following limitations when processing multi-sequence MRI data: ① single-path structures have difficulty in effectively decoupling modality-specific features; ② conventional convolutions lack adaptive enhancement in the channel and spatial dimensions. Therefore, this paper proposes a dual-path residual channel-spatial attention network (DRCSA-Net). The model learns complementary representations through a parallel dual-branch structure (channel enhancement and spatial enhancement), introduces a squeeze-and-excitation module and a channel-spatial attention module to achieve channel recalibration and spatial attention focusing, and finally integrates information and completes classification through lightweight fusion convolution and fully connected layers. To comprehensively evaluate the proposed model, five-fold cross-validation, single-/dual-path comparison, ablation experiments, and robustness analysis were conducted on four public datasets. The experimental results show that DRCSA-Net achieves high accuracy on all four public datasets and demonstrates good effectiveness and stability in the single-/dual-path comparison, ablation experiments, and robustness analysis, providing a structurally clear and stable implementation scheme for brain tumor MRI classification.</p>","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"43 4","pages":"828-835"},"PeriodicalIF":0.0,"publicationDate":"2026-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13519812/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148833515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Research on blood viscoelasticity detection based on electrowetting technology].","authors":"Chao Wang, Wenxing Huo, Liuqing Yang, Xian Huang, Wei Yang","doi":"10.7507/1001-5515.202508032","DOIUrl":"10.7507/1001-5515.202508032","url":null,"abstract":"<p><p>Parameters related to coagulation function are key clinical biomarker reflecting the coagulation status of the human blood system. The disorder of coagulation function could lead to multiple diseases such as hemophilia and ischemic stroke. Blood viscoelasticity can effectively characterize the rheological variations during blood coagulation, thus enabling comprehensive assessment of coagulation function. In this paper, electrowetting technology was adopted to develop the detection approach for blood viscoelasticity. A dynamic mathematical model of the detection system was established, and the core influencing factors of the detection system were deduced. The effects of critical parameters such as driving voltage, excitation frequency and dielectric layer thickness on detection performance were investigated systematically. The results indicate that the above parameters significantly affect the operating performance of the detection system. Optimized matching of system parameters can effectively improve the stability and detection accuracy of the system. Conclusively, the electrowetting based method achieves high-precision measurement of blood viscoelasticity, which provides a novel technical scheme for coagulation detection.</p>","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"43 4","pages":"777-783"},"PeriodicalIF":0.0,"publicationDate":"2026-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13519824/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148832875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Dual-stream temporal-frequency convolutional network with additive attention for auditory attention decoding].","authors":"Xiao Lin, Sunjie Zhang, Shuai Huang","doi":"10.7507/1001-5515.202509064","DOIUrl":"10.7507/1001-5515.202509064","url":null,"abstract":"<p><p>In complex auditory environments, humans can selectively attend to a target speaker while suppressing interfering sources. Electroencephalography (EEG)-based auditory attention decoding (AAD) aims to identify the speech source an individual is attending to, holding significant potential for applications in hearing aids and human-machine interaction. However, existing methods still exhibit notable limitations in modeling dynamic time-frequency features, extracting multi-scale representations, and computational efficiency.To address these challenges, we propose a dual-stream time-frequency convolutional network with additive attention. The temporal branch employs a temporal convolutional additive attention module to integrate temporal and channel attention, enhancing the modeling of key EEG time segments and feature channels. The spectral branch adopts a spatial-spectral feature extractor and a multi-scale feature enhancement module to improve sensitivity to and modeling of frequency variation patterns. The global features from both branches are ultimately fused for attention direction classification. Under a 1-second decision window, our model achieved accuracy rates of 96.5%, 86.3%, 73.1%, and 73.5% on the KUL, DTU, AVED (audio-only), and AVED (audio-visual) datasets, respectively. These results significantly outperform state-of-the-art methods and demonstrate the effectiveness and superiority of the proposed approach.</p>","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"43 4","pages":"695-703"},"PeriodicalIF":0.0,"publicationDate":"2026-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13519811/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148833525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[An auxiliary diagnosis system for cervical intraepithelial neoplasia based on colposcopic images].","authors":"Xuelian Gu, Yizhu Zhang, Zhiyang Xu, Rui Guan, Renling Zou, Shengxuan Chu, Qingbin Fang","doi":"10.7507/1001-5515.202411017","DOIUrl":"10.7507/1001-5515.202411017","url":null,"abstract":"<p><p>Cervical intraepithelial neoplasia is the primary type of cervical precancerous lesion; however, manual clinical diagnosis is prone to bias and has limited grading accuracy. To achieve precise automated grading of CIN, this paper proposes a multimodal fusion Swin Transformer model and develops a corresponding computer-aided diagnosis system. This method employs three-channel fusion of raw images, cervical mask images, and directional gradient histogram features to enhance lesion texture and location information. Within the Swin Transformer backbone, an atrous spatial pyramid pooling module channel attention module and a convolutional feature extraction module are embedded to balance global semantic and local detail features. A focal loss function is adopted to address class imbalance in the dataset and improve the model's ability to identify difficult-to-classify samples. On a dataset of 3 915 clinical colposcopy images, the model achieved an overall accuracy of 90.01%, precision of 87.55%, recall of 86.17%, F1 score of 89.13%, outperforming baseline models such as VGG, ResNet, and Swin Transformer. The developed system integrates image quality screening, lesion identification, and three-level classification functions, providing an effective tool for the rapid and objective screening of clinical cervical precancerous lesions.</p>","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"43 3","pages":"496-503"},"PeriodicalIF":0.0,"publicationDate":"2026-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13311087/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148346597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Research on automated orthodontic tooth arrangement by integrating multi-source structural information and dental arch prediction].","authors":"Yuhang Fang, Zheng Zhao, Hongbo Zheng, Xujia Qin, Yuanxiang Zhu, Zhengqiang Wu","doi":"10.7507/1001-5515.202510064","DOIUrl":"10.7507/1001-5515.202510064","url":null,"abstract":"<p><p>To address the issues of time-consuming manual tooth alignment, reliance on physician experience, and insufficient structural constraints in automated methods in traditional orthodontic treatment planning, this paper proposes an automated tooth alignment network model that integrates multi-source geometric information learning. First, this study uses tooth mesh data as input to construct a graph convolution-based network for extracting tooth geometric features, fully utilizing the topological connectivity and local geometric features of the tooth model to enhance the extraction of tooth morphological features. Then, an arch line prediction network is designed to guide the teeth to align orderly along the natural arch line by explicitly modeling the tooth alignment trajectory, thereby ensuring the anatomical rationality and aesthetics of the overall structure. Based on this, this study designs collision avoidance loss and arch alignment loss to reduce geometric conflicts between adjacent teeth and constrain the overall posture, making the alignment results more stable and realistic. To verify the effectiveness of the method, validation experiments were conducted on multiple sets of real dental arch data, and comparisons were made with mainstream methods such as tooth alignment network (TaligNet), parameterized spatial transformation network (PSTN), and Transformer-based network for tooth alignment (TANet). The results show that the proposed method improves the evaluation metrics to varying degrees, fully integrates global and local geometric information, improves tooth posture coordination and alignment smoothness, and provides efficient and standardized intelligent support for digital orthodontic treatment planning.</p>","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"43 3","pages":"571-579"},"PeriodicalIF":0.0,"publicationDate":"2026-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13311048/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148346723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Applications and challenges of ultra-high molecular weight polyethylene fibers in minimally invasive medical devices].","authors":"Shuangyou Shi, Yongming Shi, Jintang Zhu, Xianning Shi, Ning Cui, Huashuai Cui, Zetian Zhang, Zongzi Hou, Weixin Wu, Pengfei Wu","doi":"10.7507/1001-5515.202511021","DOIUrl":"10.7507/1001-5515.202511021","url":null,"abstract":"<p><p>Ultra-high molecular weight polyethylene (UHMWPE) fiber has emerged as a critical material advancing the development of minimally invasive medical devices, owing to its exceptional specific strength, outstanding wear resistance, and inherent bio-inertia. We systematically review the current application landscape of UHMWPE fibers in minimally invasive medicine, highlighting their broad use in orthopedic sutures and fixation systems, reinforcement layers for cardiovascular interventional devices, cables to drive surgical robots, and materials in frontier neural interfaces. These applications underscore the material's core advantages across diverse scenarios. However, its broader clinical translation faces multiple challenges, including surface bio-inertia, long-term dynamic durability, difficulty in processing, and a lack of standardization. To address these challenges, this article delves into comprehensive strategies encompassing surface engineering, composite material development, structural optimization, and intelligent control algorithms. Looking forward, UHMWPE fibers are poised to evolve towards intelligence and functional integration. Through deep convergence with flexible electronics and data-driven research, coupled with the establishment of robust standardization systems, UHMWPE fibers are expected to play an even more pivotal role in the next generation of advanced minimally invasive medical devices, ultimately propelling the field towards greater precision and personalization.</p>","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"43 3","pages":"644-653"},"PeriodicalIF":0.0,"publicationDate":"2026-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13311037/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148346515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[A reweighted graph matching-based algorithm for automatic extraction of corresponding landmarks in medical images].","authors":"Yi Xu, Zhengang Jiang, Weili Shi, Yu Miao, Huamin Yang","doi":"10.7507/1001-5515.202510040","DOIUrl":"10.7507/1001-5515.202510040","url":null,"abstract":"<p><p>In the field of medical image registration, corresponding landmarks can serve as an crucial reference for the quantitative evaluation of registration algorithm errors. However, the corresponding landmarks extracted by existing methods generally lack anatomical correspondence. While some approaches have begun to take bifurcation points of tubular structures as landmarks, the establishment of accurate one-to-one correspondences between these points is hindered by highly similar local features and missing correspondences, thereby impeding effective quantification of registration errors. To address these limitations, this paper proposes a reweighted graph matching-based algorithm for automatic extraction of corresponding landmarks in medical images. First, the bifurcation points of tubular structures with anatomical correspondence are extracted as landmarks. Secondly, to address the issues of highly similar local features and missing correspondences, a reweighted graph matching model is constructed base on a local similarity strategy and a modality independent neighborhood descriptor, which iteratively identifies correct matches and rectifies mismatches to progressively refine the correspondence between landmarks. Finally, the <i>K</i>-nearest neighbor graph of landmarks is dynamically updated, and outlier matches are filtered based on a local similarity strategy, thereby achieving accurate landmark matching under partial correspondence conditions. Experimental results on two public datasets demonstrate that the proposed method can accurately extract corresponding landmarks and effectively evaluate registration error, thereby providing technical support for enhancing the reliability of medical image registration algorithms in clinical applications.</p>","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"43 3","pages":"461-470"},"PeriodicalIF":0.0,"publicationDate":"2026-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13311033/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148346609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[A generalizable epilepsy detection network based on dual-attention mechanism].","authors":"Lei Zhang, Fengwen Zhai, Jing Jin, Xiangde Jiang, Wenwen Chang","doi":"10.7507/1001-5515.202508053","DOIUrl":"10.7507/1001-5515.202508053","url":null,"abstract":"<p><p>Existing deep learning models for epileptic electroencephalogram (EEG) signal analysis frequently overlook intrinsic pathological characteristics during feature extraction and exhibit insufficient cross-dataset generalization. To address these limitations, this study proposes an innovative dual-attention epilepsy detection network (EDDANet). The model integrates a multi-band and multi-scale dual-attention module with a dynamic kernel sampling adaptive convolutional module to classify interictal and ictal EEG signals. Extensive experiments conducted on four heterogeneous public datasets demonstrate that EDDANet consistently outperforms state-of-the-art models across key evaluation metrics, including accuracy and recall. Notably, this work is the first to achieve robust generalization across varying lead configurations, sampling rates, and electrode layouts. In conclusion, this study provides a valuable methodological framework for the design and optimization of automated epilepsy detection systems in complex scenarios, providing reference for enhancing the generalizability and clinical utility of deep learning models in real-world environments.</p>","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"43 3","pages":"530-538"},"PeriodicalIF":0.0,"publicationDate":"2026-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13311036/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148346559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Advances in research on neuroelectrophysiological characteristics of post-stroke cognitive impairment based on quantitative electroencephalography and acupuncture interventions].","authors":"Yachen Feng, Kaiqi Su, Qiongdi Ren, Rui Pan, Chunxiao Wang, Xu Zhang, Qi Li, Yaling Wang, Ming Zhang","doi":"10.7507/1001-5515.202602016","DOIUrl":"10.7507/1001-5515.202602016","url":null,"abstract":"<p><p>Post-stroke cognitive impairment (PSCI) is a common complication of stroke that severely affects patients' quality of life and social functional recovery. Quantitative electroencephalography (QEEG), as a non-invasive neuroelectrophysiological technique with high temporal resolution, can reveal PSCI-related brain dysfunction and plasticity changes from multiple levels and dimensions, including neural oscillations, brain network connectivity, and dynamic optimization of brain function. However, its feature integration and efficacy evaluation system still require further refinement. In recent years, acupuncture has attracted increasing attention in the intervention of PSCI owing to its holistic regulatory effects and favorable safety profile. This review systematically integrates the characteristics of brain rhythms, brain functional networks, and dynamic brain activity in patients with PSCI, analyzes the associations between different electroencephalography (EEG) indicators and cognitive domains, and summarizes the multidimensional changes in EEG features following acupuncture intervention in PSCI. The aim is to provide evidence for a deeper understanding of the neuroelectrophysiological mechanisms underlying PSCI, while also offering references for the objective evaluation and modernization of acupuncture interventions.</p>","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"43 3","pages":"661-668"},"PeriodicalIF":0.0,"publicationDate":"2026-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13311034/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148346574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[A two-stage model for predicting postoperative pulmonary infection in esophageal cancer patients].","authors":"Yu Wang, Lixia Yin, Bo Yao, Jiang Wu, Jiangbo Pu","doi":"10.7507/1001-5515.202601010","DOIUrl":"10.7507/1001-5515.202601010","url":null,"abstract":"<p><p>Postoperative pulmonary infection (PPI) after esophageal cancer surgery occurs frequently and severely impairs patients' prognosis. Most existing prediction models cannot realize staged classification of risk factors, which limits targeted risk identification and intervention. Based on machine learning algorithms, this study integrates preoperative baseline characteristics and perioperative indicators to construct a preoperative-perioperative two-stage risk prediction model for postoperative pulmonary infection. Clinical data of 2 200 patients undergoing esophageal cancer surgery admitted to the Cancer Hospital, Chinese Academy of Medical Sciences between October 2022 and August 2024 were retrospectively enrolled. The least absolute shrinkage and selection operator was combined with multivariate logistic regression to screen independent predictive variables for the two stages, and five machine learning models were established accordingly. Six independent predictive variables were identified. The preoperative predictors included gender, American Society of Anesthesiologists (ASA) physical status classification, and colonization of multidrug-resistant bacteria. All models yielded area under the curve values ranging from 0.71 to 0.72 with a specificity higher than 98%, which can be used for preoperative risk stratification of high-risk individuals. On the basis of preoperative variables, the perioperative stage additionally incorporated operation duration, postoperative intensive care unit (ICU) admission, and peak C-reactive protein level within 0-3 days after surgery, leading to a remarkable improvement in predictive performance with all area under the curve values greater than 0.82. The gradient boosting machine (GBM) model achieved a favorable balance between a sensitivity of 69.07% and a specificity of 82.59%, providing support for risk stratification and clinical management decision-making. Further multicenter studies are required to validate the generalization ability of the model.</p>","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"43 3","pages":"539-545"},"PeriodicalIF":0.0,"publicationDate":"2026-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13311042/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148346522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}