Biomedizinische Technik. Biomedical engineering最新文献_第2页

A machine learning-based framework for analyzing mitral regurgitation-related signal patterns from photoplethysmography. 一个基于机器学习的框架，用于分析光容积脉搏图中二尖瓣反流相关信号模式。

Biomedizinische Technik. Biomedical engineering Pub Date : 2026-09-04 DOI: 10.1515/bmt-2025-0417

Mehmet Recep Bozkurt, Pınar Özen

{"title":"A machine learning-based framework for analyzing mitral regurgitation-related signal patterns from photoplethysmography.","authors":"Mehmet Recep Bozkurt, Pınar Özen","doi":"10.1515/bmt-2025-0417","DOIUrl":"https://doi.org/10.1515/bmt-2025-0417","url":null,"abstract":"Objectives: This study investigates the feasibility of using photoplethysmography (PPG) signals for supporting mitral regurgitation (MR) assessment and monitoring using machine learning techniques.Methods: MR occurs when blood flows backward from the left ventricle to the left atrium due to mitral valve insufficiency. Although echocardiography is the standard diagnostic method, it requires expert interpretation. Other techniques, such as magnetic resonance imaging, cardiac catheterization, and angiography, are costly and invasive. This study investigates the feasibility of using PPG signals for exploring MR-related signal patterns and supporting non-invasive monitoring via machine learning techniques.Results: From 1,590 ten-second PPG recordings, 37 features were extracted. Following feature selection, 11 features were retained. Using 10-fold cross-validation, the proposed ensemble classifier achieved an accuracy of 91.07 %, sensitivity of 0.9462, specificity of 0.8765, AUC of 0.9113, and MCC of 0.8249 using 11 selected PPG features. The results indicate that PPG-derived features may capture signal characteristics associated with MR.Conclusions: These findings demonstrate the feasibility of PPG-based machine learning approaches for supporting accessible and non-invasive monitoring of MR-related cardiovascular patterns.","PeriodicalId":93905,"journal":{"name":"Biomedizinische Technik. Biomedical engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148893144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Federated learning for multi-institutional AI in healthcare via digital pathology. 通过数字病理学实现医疗保健领域多机构人工智能的联合学习。

Biomedizinische Technik. Biomedical engineering Pub Date : 2026-09-03 DOI: 10.1515/bmt-2025-0489

Ramin Soleimani, Mohammadreza Azimi, Nazanin Talebi

引用次数: 0

Abstracts of the 60th Annual Conference of the German Society of Biomedical Engineering, 6 - 8 October 2026, Augsburg. 第60届德国生物医学工程学会年会摘要，2026年10月6日至8日，奥格斯堡。

Biomedizinische Technik. Biomedical engineering Pub Date : 2026-08-21 Print Date: 2026-08-01 DOI: 10.1515/bmt-2026-1001

引用次数: 0

A novel medical image segmentation network for colorectal polyp small targets and fuzzy boundaries. 基于模糊边界和小目标的结直肠息肉医学图像分割网络。

Biomedizinische Technik. Biomedical engineering Pub Date : 2026-08-18 DOI: 10.1515/bmt-2025-0383

Yutong Ji, Chuantao Wang, Xiumin Wang

{"title":"A novel medical image segmentation network for colorectal polyp small targets and fuzzy boundaries.","authors":"Yutong Ji, Chuantao Wang, Xiumin Wang","doi":"10.1515/bmt-2025-0383","DOIUrl":"https://doi.org/10.1515/bmt-2025-0383","url":null,"abstract":"Objectives: To address the challenges of complex feature variations and unclear boundary definitions between segmented targets and surrounding regions in medical images, a novel segmentation model based on Deformable Large Kernel Convolutional Attention (D-LKA) and Transformer is proposed.Methods: The model first uses Vision Transformer as the encoder to enhance the ability to capture global information, overcoming the limitations of convolutional neural networks' receptive field. In the decoder, a D-LKA decoder with deformable large kernel convolution attention is used, allowing the model to adapt to complex target features. Finally, the TRR module is introduced to coordinate information transfer between the convolutional neural network and Transformer, reducing semantic loss.Results: The model is trained, validated, and tested on the Kvasir-SEG colon polyp dataset, with multiple ablation experiments. To validate generalization, experiments are also conducted on the CVC-ClinicDB dataset. Experimental results show that TDU-Net outperforms other methods in both segmentation accuracy and generalization.Conclusions: TDU-Net achieves excellent segmentation results, addressing small target and incomplete feature extraction issues, significantly improving clinical diagnosis efficiency and accuracy.","PeriodicalId":93905,"journal":{"name":"Biomedizinische Technik. Biomedical engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148803120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Biocompatibility of self-etching primer in orthodontics: an in vitro study. 自蚀刻引物在口腔正畸中的生物相容性：体外研究。

Biomedizinische Technik. Biomedical engineering Pub Date : 2026-08-06 DOI: 10.1515/bmt-2026-0390

Thomas Wendl, Clemens Kittinger, Rebecca Jungbauer, Brigitte Wendl, Peter Proff

{"title":"Biocompatibility of self-etching primer in orthodontics: an in vitro study.","authors":"Thomas Wendl, Clemens Kittinger, Rebecca Jungbauer, Brigitte Wendl, Peter Proff","doi":"10.1515/bmt-2026-0390","DOIUrl":"https://doi.org/10.1515/bmt-2026-0390","url":null,"abstract":"Objectives: Self-etching primers have simplified the process of direct bonding of dental resins and orthodontic brackets. The aim of this study was to evaluate the cytotoxic impact of the leached components to L-929 fibroblast cultures.Methods: Transbond™ Plus self-etching primer (3M Unitek, 2724 South Peck Road, Monrovin CA 91016 USA) was applied to enamel pieces in three different conditions and the specimens were added to L-929 fibroblast cultures. The incubation time was 72 h and the cells were counted by flow cytometry. Statistics was carried out using descriptive and explorative data analyses.Results: In direct contact assays, the self-etching primer without bracket exhibited mild to moderate cytotoxicity. The application of primer, composite and bracket showed no relevant decrease of cell viability. After seven days, the cytotoxic effects had largely disappeared. Overall, the combination of self-etching primer, composite, bracket exhibits no significant cytotoxicity during the observation period.Conclusions: When self-etching primers are used according to the recommended clinical protocols in orthodontics, the method is regarded as safe and the release of leachables seems to have no adverse clinical effect.","PeriodicalId":93905,"journal":{"name":"Biomedizinische Technik. Biomedical engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148681443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Clinical integration and translational challenges in dental robotics: a narrative review. 牙科机器人的临床整合和转化挑战：叙述回顾。

Biomedizinische Technik. Biomedical engineering Pub Date : 2026-08-04 DOI: 10.1515/bmt-2026-0339

Ezgi Demir, Didem Atabek

{"title":"Clinical integration and translational challenges in dental robotics: a narrative review.","authors":"Ezgi Demir, Didem Atabek","doi":"10.1515/bmt-2026-0339","DOIUrl":"https://doi.org/10.1515/bmt-2026-0339","url":null,"abstract":"Introduction: Robotic technologies are increasingly used in dentistry to improve procedural precision and standardization. However, clinical implementation remains heterogeneous due to differences in autonomy, technological maturity, and available clinical evidence. This review evaluates current applications using a dual-perspective framework.Content: A narrative review of the literature was conducted using PubMed/MEDLINE, Scopus, and Web of Science. English-language publications from 2004 onwards were considered and selected according to their relevance to robotic system design, clinical applications, autonomy levels, technical performance, and translational challenges in dentistry.Summary: Robotic applications have expanded across surgical, therapeutic, educational, and preventive domains. Most systems operate at assistive or semi-autonomous levels (Level 1-2), with the strongest clinical evidence observed in implant dentistry, where improved placement accuracy has been reported. Clinical adoption remains limited by high costs, technical complexity, and insufficient long-term outcome data.Outlook: Dental robotics has evolved into a clinically applicable component of digital dentistry, but routine clinical implementation remains heterogeneous across dental specialties. The proposed dual-perspective framework facilitates evaluation of robotic technologies and their clinical readiness, while further clinical validation is needed to support routine implementation.","PeriodicalId":93905,"journal":{"name":"Biomedizinische Technik. Biomedical engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148665082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Classification of malignant/benign groups in lung cancer by machine learning and investigation of feature significance of parameters. 基于机器学习的肺癌良/恶性分类及参数特征意义研究。

Biomedizinische Technik. Biomedical engineering Pub Date : 2026-07-28 DOI: 10.1515/bmt-2024-0628

Gonul Caliskan, Emre Alatas, Sevilay Koksal, Safak Yigit, Resit Akyel, Iffet Cavdar, Goksel Alcin, Esra Arslan, Tuba Ciftci-Kusbeci, Elcin Dereli, Elife Akgun, Handan Tanyildizi-Kokkulunk

{"title":"Classification of malignant/benign groups in lung cancer by machine learning and investigation of feature significance of parameters.","authors":"Gonul Caliskan, Emre Alatas, Sevilay Koksal, Safak Yigit, Resit Akyel, Iffet Cavdar, Goksel Alcin, Esra Arslan, Tuba Ciftci-Kusbeci, Elcin Dereli, Elife Akgun, Handan Tanyildizi-Kokkulunk","doi":"10.1515/bmt-2024-0628","DOIUrl":"https://doi.org/10.1515/bmt-2024-0628","url":null,"abstract":"Objectives: This study aimed to apply machine learning (ML) models to enhance lung cancer (LC) classification, distinguishing malignant from benign tumors, using data from 73 patients.Methods: The dataset included PET/CT biomarkers and demographic factors, with SMOTE applied to address class imbalance. Three models were evaluated using 10-fold cross-validation to compare Random Forest (RF), Decision Tree (DT), Extra Trees Classifier (ETC), and XGBoost algorithms based on accuracy and AUC.Results: ETC performed best in Model 1 (86 % accuracy, AUC 0.95), RF in Model 2 (94 % accuracy, AUC 0.97), and XGBoost in Model 3 (94 % accuracy, AUC 0.98). XGBoost consistently outperformed others, particularly in Model 3, which included age and smoking. Feature importance analysis highlighted SUVmax as the most predictive variable, with smoking having a moderate influence and gender being minimal.Conclusions: Integrating clinical and lifestyle data with PET/CT parameters significantly improved LC classification. XGBoost emerged as the most effective model, demonstrating that comprehensive models enhance diagnostic accuracy beyond traditional metrics.","PeriodicalId":93905,"journal":{"name":"Biomedizinische Technik. Biomedical engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148595039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

State-based prognostics with similarity enhancement for CT X-ray tubes using operational logs. 使用操作日志对CT x射线管进行基于状态的相似性增强预测。

Biomedizinische Technik. Biomedical engineering Pub Date : 2026-07-13 DOI: 10.1515/bmt-2026-0184

Changjun Chen, Xiangjie Wang, Linze Qian, Ling Wang, Chenting Ye, Fengjie Zhu, Panpan Xu, Yonghua Chu, Quchao Zou

{"title":"State-based prognostics with similarity enhancement for CT X-ray tubes using operational logs.","authors":"Changjun Chen, Xiangjie Wang, Linze Qian, Ling Wang, Chenting Ye, Fengjie Zhu, Panpan Xu, Yonghua Chu, Quchao Zou","doi":"10.1515/bmt-2026-0184","DOIUrl":"10.1515/bmt-2026-0184","url":null,"abstract":"Objectives: The tube is a core component of the CT system and is characterized by high cost and limited lifespan. This study aims to develop a method for estimating the remaining useful life (RUL) of CT X-ray tubes without requiring a preset failure threshold or prior knowledge of tube life.Methods: To reduce the impact of varying scan conditions, different scan types are first analyzed, and the one with the most stable tube current distribution is selected. Then, a composite metric is applied to degradation parameters to obtain a candidate health indicator (HI). Next, the candidate HI is normalized using the training set and transformed into a percentage health indicator (PHI) to represent tube degradation. Finally, a state-based prognostics with similarity enhancement is applied to the PHI to estimate RUL.Results: The proposed method is evaluated using real tube log data under leave-one-out cross-validation. The results demonstrate that it achieves optimal prediction performance and enables cross-tube RUL estimation. Moreover, the prediction results can reflect the actual degradation progress of the tube without requiring a preset failure threshold.Conclusions: The proposed approach provides an effective and practical framework for CT X-ray tube RUL estimation, demonstrating good potential for practical medical equipment maintenance.","PeriodicalId":93905,"journal":{"name":"Biomedizinische Technik. Biomedical engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148427244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluating FlexTail: a wearable device for spinal posture tracking. 评估FlexTail：一种脊柱姿势跟踪的可穿戴设备。

Biomedizinische Technik. Biomedical engineering Pub Date : 2026-07-09 DOI: 10.1515/bmt-2025-0369

Arwed Masch, Jonas Walkling, Luca Sander, Thomas M Deserno

{"title":"Evaluating FlexTail: a wearable device for spinal posture tracking.","authors":"Arwed Masch, Jonas Walkling, Luca Sander, Thomas M Deserno","doi":"10.1515/bmt-2025-0369","DOIUrl":"https://doi.org/10.1515/bmt-2025-0369","url":null,"abstract":"Objectives: Accurate, continuous spinal posture tracking is critical for understanding musculoskeletal disorders and developing personalized treatment. However, current solutions are costly, obtrusive, or insufficiently accurate in daily life. This study quantifies the accuracy of FlexTail, a flexible, wearable sensor that integrates printed strain-gauge electronics to capture three-dimensional (3D) spinal motion in real-time.Methods: FlexTail's performance is benchmarked with 3D-printed validation templates: three fixed-diameter S-shaped flexion paths (100, 150 and 250 mm flexion diameter), a spine-shaped path, and a 90° torsion path. The measured deformation is compared to the known geometry to quantify the precision of the device. We analyze 10 FlexTail devices and repeat each measure 10 times.Results: Across flexion templates, the mean positional error is 3.61 mm. The 250 mm flexion template yields the lowest error, 2.12 mm. The mean torsional error is 0.13°, and the spine-shaped template shows an angular deviation of 1.05° with a positional error of 2.95 mm. The intra-device deviations stay below 0.60 mm and 0.051° for flexion and torsion, respectively.Conclusions: FlexTail delivers reliable flexion and torsion tracking with accuracy equal to clinical systems. Its soft, integrated form factor supports long-term, unobtrusive use as a tool for biomechanical analysis and digital health applications.","PeriodicalId":93905,"journal":{"name":"Biomedizinische Technik. Biomedical engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148414164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Physics-informed neural network-based simulation of pulmonary arterial hemodynamics abnormalities. 基于物理信息的神经网络模拟肺动脉血流动力学异常。

Biomedizinische Technik. Biomedical engineering Pub Date : 2026-06-05 DOI: 10.1515/bmt-2025-0344

Ziteng Lu, Haisong Huang, Lanping Wu, Haibin Sun, Yanfeng Liu

{"title":"Physics-informed neural network-based simulation of pulmonary arterial hemodynamics abnormalities.","authors":"Ziteng Lu, Haisong Huang, Lanping Wu, Haibin Sun, Yanfeng Liu","doi":"10.1515/bmt-2025-0344","DOIUrl":"https://doi.org/10.1515/bmt-2025-0344","url":null,"abstract":"Objectives: To predict abnormal pulmonary artery hemodynamics caused by ventricular septal defect (VSD) using Physics-Informed Neural Networks (PINN) and address the challenges of high computational cost in traditional Computational Fluid Dynamics (CFD) and difficulty in obtaining measurement data.Methods: The PINN model was trained using boundary conditions and scattered clinical CFD data, with dynamic weighting factors incorporated to enhance training efficiency and optimize predictions. Model outputs for blood flow velocity and pressure were subsequently evaluated against CFD simulation results.Results: The PINN accurately reproduced velocity and pressure fields across pulmonary artery models using only boundary conditions and sparse internal measurements. For velocity prediction, the average RMSE, MAE, and MRE for components u, v, and w ranged from 0.274 to 0.832 %, 0.448-1.096 %, and 0.833-1.341 %, respectively. For pressure prediction, the average RMSE, MAE, and MRE ranged from 2.953 to 5.145 %, 3.264-5.679 %, and 0.376-0.565 %, respectively. These findings demonstrate that the framework generalizes well and provides reliable hemodynamic estimation with limited input data.Conclusions: The PINN model compensates for incomplete measurement data through physical constraints, enabling rapid and accurate prediction of pulmonary artery hemodynamics and offering a promising non-invasive alternative for pulmonary artery pressure measurement.","PeriodicalId":93905,"journal":{"name":"Biomedizinische Technik. Biomedical engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148165748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0