Medical & Biological Engineering & Computing最新文献

{"title":"Slippage-suppression robot-assisted retraction for thyroid surgery with 5DoF contact force sensing.","authors":"Shouhui Deng, Haojun Li, Yuxuan Lin, Aiguo Song, Lifeng Zhu","doi":"10.1007/s11517-025-03420-z","DOIUrl":"https://doi.org/10.1007/s11517-025-03420-z","url":null,"abstract":"<p><p>Thyroid nodules often necessitate surgical intervention, where traditional retractors may cause muscle damage due to prolonged use. This study introduces a slippage-suppression robotic system for thyroid surgery, featuring a conformal force and torque sensing module integrated with a robotic manipulator for compliant force control. The system features five-dimensional (5DoF) contact force sensing, achieving accurate force measurement with a relative error of <math><mrow><mo>≤</mo> <mn>1.5</mn> <mo>%</mo></mrow> </math> . Experiments performed on phantoms and porcine tissues demonstrate the system's ability to suppress slippage effectively, ensure reliable force feedback, and improve safety and precision during thyroid surgery.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144700159","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}

{"title":"Towards a radiation-free clinical decision support system for intraoperative spinal alignment assessment.","authors":"Luis Serrador, Pedro Varanda, Bruno Direito-Santos, Cristina P Santos","doi":"10.1007/s11517-025-03412-z","DOIUrl":"https://doi.org/10.1007/s11517-025-03412-z","url":null,"abstract":"<p><p>This paper introduces SpineAlign, a novel radiation-free clinical decision support system (CDSS) designed to address the challenge of intraoperative spinal alignment assessment during spinal deformity (SD) correction surgeries. SpineAlign aims to overcome the current limitations of existing systems by providing a quantitative assessment without radiation exposure in the operating room (OR), thus enhancing the safety and precision of computer-assisted spinal surgeries (CASS). The system focuses on spinal alignment calculation, leveraging Bézier curves and algorithm development to track vertebrae and estimate spinal curvature. Collaborative meetings with clinical experts identified challenges such as patient positioning complexities and limitations of minimal invasiveness. Thus, the method developed involves four algorithms: (1) tracking anatomical planes; (2) estimating the Bézier curve; (3) determining vertebrae positions; and (4) adjusting orientation. A proof of concept (PoC) using a porcine spinal segment validated SpineAlign's integrated algorithms and functionalities. The PoC demonstrated the system's accuracy and clinical applicability, successfully transitioning a spine without curvature to a lordotic spine. Quantitative evaluation of spinal alignment by the system showed high accuracy, with a maximum root mean squared error of 6 <math><mmultiscripts><mrow></mrow> <mrow></mrow> <mo>∘</mo></mmultiscripts> </math> . The successful PoC marks an initial step towards developing a reliable CDSS for intraoperative spinal alignment assessment without medical image acquisition. Future steps will focus on enhancing system robustness and performing multi-surgeon serial studies to advance SpineAlign towards widespread clinical adoption.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144700160","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}

{"title":"A web-based system for real-time ECG monitoring using MySQL database and DigiMesh technology: design and implementation.","authors":"Abdelkader Tigrine, Moufida Houamria, Halima Sahraoui, Ameur Dahani, Noureddine Doumi, Khaled Dine","doi":"10.1007/s11517-025-03421-y","DOIUrl":"https://doi.org/10.1007/s11517-025-03421-y","url":null,"abstract":"<p><p>In today's world, rapid advancements in wireless sensor network (WSN) technologies hold the potential to revolutionize healthcare through future ubiquitous patient monitoring systems. Essential for continuous monitoring without restricting patient mobility, these systems comprise wearable or implanted sensors continuously tracking physiological parameters. Enabling seamless patient-doctor interaction, they monitor and transmit patient physiological data. This project involves designing an ECG monitoring system utilizing DigiMesh technology for wireless transmission to a remote device. Patient data is stored in the IoT-cloud via a MySQL database, enabling real-time remote monitoring by medical staff. The sensor node processes ECG data, transmitted to the Sink Node, and the MySQL database facilitates data storage. Utilizing a web-based system accessible on all devices, the proposed monitoring system displays ECG results, reports, and patient information. The goal is to create a reliable, cost-effective, low-power vital signs monitoring system transmitting various body parameters wirelessly to medical professionals. In hospitals, continuous monitoring is crucial for patients requiring extended medical care, ensuring constant surveillance even in non-emergency situations.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144692216","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}

{"title":"ISENet: a deep learning model for detecting ischemic ST changes in long-term ECG monitoring.","authors":"Chun-Cheng Lin, Cheng-Yu Yeh, Jian-Hong Lin","doi":"10.1007/s11517-025-03416-9","DOIUrl":"https://doi.org/10.1007/s11517-025-03416-9","url":null,"abstract":"<p><p>Long-term ECG monitoring is crucial for detecting asymptomatic or intermittent myocardial ischemia, as it mitigates irreversible cardiac damage and prevents disease progression. Myocardial ischemia appears on ECG as transient ST-segment level and morphology alterations, known as ischemic ST change events (ISE). However, automatically identifying ISE based on ECG signals is challenging, as its recognition is highly susceptible to interference from non-ischemic ST change events, including heart rate-related ST change events (HRE), axis shift events (ASE), and conduction change events (CCE). To address this challenge, this study proposes ISENet, a lightweight deep learning-based neural network for ISE detection. The model was trained and evaluated using ECG signals and annotations from the PhysioNet long-term ST database, with tenfold cross-validation to ensure robustness and generalizability. Experimental results show that ISENet achieves an average ISE detection accuracy of 83.5%, surpassing benchmark models like VGG19 and ResNet50 while significantly reducing model complexity. This study is the first to apply a deep learning-based neural network for ISE detection using ECG signals from the long-term ST database. Compared to previous feature-engineering and feature-learning approaches, ISENet addresses key limitations in experimental design and methodology, representing a significant advancement in automated myocardial ischemia detection.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144668869","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}

{"title":"Piezoresistive plantar pressure sensors and CNN-based body weight and load estimation.","authors":"Zhiyuan Zhang, Xuemeng Li, Weihao Ma, Shuo Gao","doi":"10.1007/s11517-025-03409-8","DOIUrl":"https://doi.org/10.1007/s11517-025-03409-8","url":null,"abstract":"<p><p>Monitoring user weight, including body weight and afforded load, is crucial for post-fracture rehabilitation. Inappropriate weight levels can delay recovery and increase re-fracture risk. In recent years, insole sensor systems have proven effective in monitoring gait parameters, including plantar pressure and gait cycles. Among all gait parameters, plantar pressure is particularly useful for monitoring and predicting user weight due to its strong correlation. However, previous studies were limited in scenarios and accuracy. To address these issues, this study proposes a piezoresistive plantar pressure sensor system (PPS) integrated with a CNN model. The system uses 96 piezoresistive force sensors to collect plantar pressure data from 107 subjects in both walking and standing conditions with varying loads (0 kg, 5 kg, 10 kg, 15 kg). The data is input into the CNN model for user weight prediction. Results show standing without load achieves an R² of 0.9997 and relative error of 0.0027, while walking with load shows the lowest R² of 0.8857 and relative error of 0.0416. This work enables accurate user weight estimation and supports gait-based healthcare research, particularly in relation to plantar pressure.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144668870","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}

{"title":"SAID-Net: enhancing segment anything model with implicit decoding for echocardiography sequences segmentation.","authors":"Yagang Wu, Tianli Zhao, Shijun Hu, Qin Wu, Xin Huang, Yingxu Chen, Pengzhi Yin, Zhoushun Zheng","doi":"10.1007/s11517-025-03419-6","DOIUrl":"https://doi.org/10.1007/s11517-025-03419-6","url":null,"abstract":"<p><p>Echocardiography sequence segmentation is vital in modern cardiology. While the Segment Anything Model (SAM) excels in general segmentation, its direct use in echocardiography faces challenges due to complex cardiac anatomy and subtle ultrasound boundaries. We introduce SAID (Segment Anything with Implicit Decoding), a novel framework integrating implicit neural representations (INR) with SAM to enhance accuracy, adaptability, and robustness. SAID employs a Hiera-based encoder for multi-scale feature extraction and a Mask Unit Attention Decoder for fine detail capture, critical for cardiac delineation. Orthogonalization boosts feature diversity, and I <math><mmultiscripts><mrow></mrow> <mrow></mrow> <mn>2</mn></mmultiscripts> </math> Net improves handling of misaligned contextual features. Tested on CAMUS and EchoNet-Dynamics datasets, SAID outperforms state-of-the-art methods, achieving a Dice Similarity Coefficient (DSC) of 93.2% and Hausdorff Distance (HD95) of 5.02 mm on CAMUS, and a DSC of 92.3% and HD95 of 4.05 mm on EchoNet-Dynamics, confirming its efficacy and robustness for echocardiography sequence segmentation.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144651066","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}

{"title":"Thermal therapy of atherosclerotic plaques using ultrasonic phased-array system.","authors":"Sha Yuan, Jiwen Hu, Chuangjian Xia, Qinlin Li, Chang Li","doi":"10.1007/s11517-025-03407-w","DOIUrl":"https://doi.org/10.1007/s11517-025-03407-w","url":null,"abstract":"<p><p>How to utilize focused ultrasound to achieve rapid, efficient, and safe ablation of atherosclerotic plaques (APs) is a significant challenge in clinical medicine. On the basis of the thermal damage effect of ultrasound on biological tissues, this paper proposes a thermal ablation mode for AP therapy with a single-focus, variable-frequency scanning model using a phased array. An AP model combined with fluid‒solid‒thermal conjugation is established and solved by the finite element method. The results show that the acoustic energy excited by a phased array can be precisely localized at the preset focal points in the plaque, and auto-focused heating is achieved under temperature control at 43 °C. Multiple autofocus scans increase the area of plaque thermal ablation while protecting the normal tissue surrounding the plaque. This model provides a potential treatment option for the thermal ablation of plaques with different depths and sizes.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144643969","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}

{"title":"Reconfiguration planning and structure parameter design of a reconfigurable cable-driven lower limb rehabilitation robot.","authors":"Jinghang Li, Keyi Wang, Yanzhuo Wang, Yi Yuan","doi":"10.1007/s11517-025-03402-1","DOIUrl":"https://doi.org/10.1007/s11517-025-03402-1","url":null,"abstract":"<p><p>Reconfigurable cable-driven parallel robots (RCDPRs) have attracted much attention as a novel type of cable-driven robot that can change their cable anchor position. The reconfigurable cable-driven lower limb rehabilitation robot (RCDLR) employs RCDPRs in lower limb rehabilitation to achieve multiple training modes. This paper investigates the reconfiguration planning and structural parameter design of the RCDLR. The RCDLR aims to fulfill the requirements of early passive rehabilitation training. Therefore, motion capture data are analyzed and mapped to the target trajectory of the RCDLR. Through dynamics modeling, the Wrench-Feasible Anchor-point Space (WFAS) is defined, from which an objective function for optimal reconfiguration planning is derived. The genetic algorithm is used to solve the optimal reconfiguration planning problem. Additionally, we propose the reconfigurability and safety coefficients as components of a structure parameter design method aimed at satisfying multiple target rehabilitation trajectories. Finally, numerical simulations are implemented based on the instance data and target trajectories to compute the specific structure parameters and verify the effectiveness of the reconfiguration planning method.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144627590","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}

{"title":"Predicting internal carotid artery system risk based on common carotid artery by machine learning.","authors":"Yuhao Tong, Qingyi Zhang, Feng Zhang, Weidong Mu, Steven W Su, Lin Liu","doi":"10.1007/s11517-025-03413-y","DOIUrl":"https://doi.org/10.1007/s11517-025-03413-y","url":null,"abstract":"<p><p>Early identification of internal carotid artery (ICA) system diseases is critical for preventing stroke and other cerebrovascular events. Traditional diagnostic methods rely heavily on clinician expertise and costly imaging, limiting accessibility. This study aims to develop an interpretable machine learning (ML) model using common carotid artery (CCA) features to predict ICA disease risk, enabling efficient screening. Clinical data from 1612 patients (806 high-risk vs. 806 low-risk ICA disease) were analyzed. CCA features-blood flow, intima-media thickness, internal diameter, age, and gender-were used to train five ML models. Model performance was evaluated via accuracy, sensitivity, specificity, AUC-ROC, and F1 score. SHAP analysis identified key predictors. The support vector machine (SVM) achieved optimal performance (accuracy, 84.9%; AUC, 92.6%), outperforming neural networks (accuracy, 81.4%; AUC, 89.8%). SHAP analysis revealed CCA blood flow (negative correlation) and intima-media thickness (positive correlation) as dominant predictors. This study demonstrates that CCA hemodynamic and structural features, combined with interpretable ML models, can effectively predict ICA disease risk. The SVM-based framework offers a cost-effective screening tool for early intervention, particularly in resource-limited settings. Future work will validate these findings in multi-center cohorts.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144627589","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}

{"title":"Rapid trauma classification under data scarcity: an emergency on-scene decision model combining natural language processing and machine learning.","authors":"Jun Tang, Tao Li, Liangming Liu, Dongdong Wu","doi":"10.1007/s11517-025-03414-x","DOIUrl":"https://doi.org/10.1007/s11517-025-03414-x","url":null,"abstract":"<p><p>Trauma has become a major cause of increased morbidity and mortality worldwide. In emergency response, the classification of injuries is crucial as it helps to quickly determine the criticality of the injured, allocate rescue resources rationally, and decide the priority order of treatment. However, emergency scenes are often chaotic environments, making it difficult for rescue personnel to collect complete and accurate information about the injured in a short period. The combination of artificial intelligence and emergency rescue is gradually changing the rescue model, improving the efficiency of rescue operations. We selected data from 26,810 trauma patients admitted to Chongqing Daping Hospital between 2013 and 2024. We propose a fast tiered medical treatment method with a two-layer structure under emergency limited data conditions, which integrates natural language processing (NLP) and machine learning (ML) techniques. The tiered medical treatment model utilizes NLP to capture semantic features of unstructured text data, while utilizing four ML algorithms to process structured numerical data. Additionally, we conducted external validation using 245 data entries from the Chongqing Emergency Center. The experimental results show that gradient boosting and logistic regression have the best performance in the two-layer ML algorithms. Based on these two algorithms, our model outperformed the multilayer perceptron (MLP) model on the test dataset, achieving an accuracy of 91.17%, which is 4.33% higher than that of the MLP model. The specificity, F1-score, and AUC of our model were 97.06%, 86.85%, and 0.949, respectively. For the external dataset, the model achieved accuracy, specificity, F1-score, and AUC of 87.35%, 95.78%, 80.37%, and 0.848, respectively. These results demonstrate the model's high generalizability and prediction accuracy. A model integrating NLP and ML techniques enables rapid tiered medical treatment based on limited data from the emergency scene, with significant advantages in terms of prediction accuracy.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144610197","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}