Biomedizinische Technik. Biomedical engineering最新文献

Objectives: The objective of this study was to assess the current usage, challenges, and specific needs regarding biomechanical data, with a focus on gait analysis, across a diverse field such as sports performance, clinical rehabilitation, and occupational health.

Methods: We conducted a survey among more than 300 institutions in the DACH region (Germany, Austria, Switzerland). Of 49 individuals who began the survey, 24 submitted complete responses.

Results: Findings indicate that participants recognize the value of biomechanical data, yet adoption of publicly available datasets is hindered by inconsistent formats, limited metadata, and strict privacy regulations. Respondents emphasized standardized documentation and clearer guidelines to facilitate sharing and collaboration. Many institutions are overwhelmed by legal compliance, with data proception laws posing significant hurdles to effective usage.

Conclusions: The results highlight the need to (1) establish standardized file formats, (2) improve metadata quality, (3) develop transparent, consistent data-sharing protocols, and (4) clarify legal frameworks for compliance.

Objectives: Cardiopulmonary bypass (CPB) and extracorporeal membrane oxygenation (ECMO) or life support (ECLS) circuits are built from polymers and might release polymeric micro- and nanoparticles (MNP) into the circulation. MNPs seem to provoke inflammation, oxidative stress, and apoptosis, which are also side effects of extracorporeal circulation. Thus, we investigated the MNP release from CPB and ECMO/ECLS circuits.

Methods: A CPB and ECMO/ECLS circuit was filled with saline solution, and circulation was initiated for 5 h and 7 d, respectively. Samples were taken from both circuits and filtered through a silicon membrane. MNPs were detected and quantified using optical microscopy and micro Raman spectroscopy.

Results: During circulation, polyvinyl chloride (PVC) and polymethyl methacrylate (PMMA) were detected in the CPB perfusate. After 5 h of circulation, polyethylene terephthalate (PET) was detected. In the ECMO/ECLS circuit, time-dependent accumulation of polymeric fragments was detected. Finally, particles of polyethylene (PE), polystyrene (PS), PET, and PVC were identified. The particle-size distribution extended from initially <2 µm to finally >10 µm with increasing circulation time.

Conclusions: CPB and ECMO/ECLS circuits release MNPs. The number of MNPs increases over the period of use. A larger number of circuits and of health effects of identified MNPs, should be investigated.

Objectives: This paper presents an experimental numerical method for modeling and analyzing stochastic systems. For this purpose, various machine prediction models are trained using the Monte Carlo simulation method. This method is presented using experimental data of a kidney transplantation with an immunosuppressive protocol based on tacrolimus.

Methods: A multivariate regression model was constructed by previous authors based on a clinical study in which key independent physiological parameters such as serum creatinine and estimated glomerular filtration rate (eGFR) six months after transplantation, as well as the pharmacokinetics of tacrolimus, including the dose-adjusted trough concentration of tacrolimus (C0/D) and intrastation variability (IPV), and eGFR between 13 and 36 were the dependent variable. Using the Monte Carlo simulation method, this model is further applied to obtain the essential data for the optimization of the prediction models. To determine the optimal prediction model, the DecisionTreeClassifier, Random Forest Classifier, and XGBClassifier were trained and compared.

Results: The results indicate that XGBoost is the most accurate, reliable and generalizable model among the classifiers tested, while Monte Carlo simulation represents a significant methodological advance in the field of kidney transplantation.

Conclusions: Advanced numerical methods for kidney transplant patients' therapy are step forward in optimization of current immunosuppressive protocols.

Objectives: Early and accurate detection of breast cancer is critical for improving patient survival outcomes. This study proposes a robust deep learning-based framework for breast cancer detection using digital breast tomosynthesis (DBT) images, leveraging both single-slice and multi-slice inputs.

Methods: The proposed work includes image normalization, resizing and Laplacian Pyramid Enhancement (LPE). Various features were extracted and fused in different combinations. To retain the most discriminative features Exhaustive Feature Selection (EFS) is used. A hybrid model integrated using ResNet V2, MobileNet V3 and Inception V3+ for classification. Finally ensemble learning with XGBoost was applied and Hyperparameters were optimized using a grid search strategy.

Results: The hybrid model with multi-slice DBT inputs achieved better improvements with accuracy, sensitivity, specificity and area under the curve (AUC). While applying LPE, feature fusion and EFS substantially enhanced the hybrid model's diagnostic performance.

Conclusions: The findings demonstrate the strong potential of advanced methods in enhancing performance. Future research work will focus on integrating this pipeline with clinical decision support systems, multi-center datasets and extending to other breast imaging modalities.

Objectives: Romania is aligning its healthcare AI ecosystem with the European Union's AI Act (2024/1689), through its own National AI Strategy. Despite a growing volume of research in medical AI, significant gaps remain in translating this output into patents and domestically developed high-risk medical devices. This study assesses Romania's research productivity, patenting activity, and commercial solutions in medical AI.

Methods: We conducted an analysis combining bibliometric data, patent information, and medical device databases, and analyzed 619 Romanian-authored articles on medical AI, 272 patent records, and identified domestic AI-powered software providers.

Results: Romania's publication output in medical AI has surged post-2018, primarily driven by academic institutions in Bucharest and Cluj. Patents are predominantly filed by multinationals, indicating a potential disconnect between research and industrial output. Six AI medical software solutions were identified, most under the low-risk classifications.

Conclusions: While Romania is expanding its medical AI research, it faces barriers in converting academic output into innovation. The dominance of foreign corporations in patent filings, reliance on international funding for high-impact research, and the scarcity of domestically developed high-risk medical AI solutions highlight important gaps. Addressing these disparities is essential for national alignment in the AI medical innovation space.

Objectives: Flight cognitive ability is the core element of guaranteeing flight safety. This study attempts to explore the basic cognitive operation ability of pilots under overload environment, and provides reference for the research on the index screening and monitoring technology methods cognitive operation ability under centrifuge overload condition.

Methods: A specific cognitive operation ability task is designed, physiological signals such as electroencephalography (EEG) when performing cognitive ability tasks under human centrifuge overload condition are obtained, and the characteristic parameters changes are studied.

Results: When performing cognitive tasks, both the beta waves and the Theta/Beta Ratio (TBR) parameters in EEG show certain specific changes after the task starts and in the later stage the task.

Conclusions: Beta wave energy ratio and TBR parameter can better reflect the changes of pilot's cognition under overload environment, and the joint research of multiple frequency bands and multiple parameters helps to find the parameters and indicators with the potential to become the physiological monitoring index of pilot's cognitive operation capacity under overload condition more quickly and better. This study lays a foundation and provides a reference for the follow-up selection of physiological monitoring index of a pilot's cognitive operation ability and the research of monitoring technology on cognitive operation ability under overload condition.

Objectives: This study aimed to enhance the comprehension of volumetric bone mineral density (vBMD) changes following Total Hip Arthroplasty (THA) by establishing a protocol to (i) precisely locate alterations in the proximal femur in three dimensions and (ii) evaluate these changes over an extended period.

Methods: Twelve individuals who underwent unilateral THA, using either cemented or uncemented prostheses, were recruited. CT-scans of the proximal femur were acquired at three distinct time points: 24 h, 1 and 6 years post-surgery. Utilizing the acquired data, 3D models of the proximal femur were generated, and a novel algorithm was developed to categorize them into Gruen zones. Comparative analysis of density values among the three sets of scans allowed the calculation of bone density gains/losses for the entire proximal femur and specific regions.

Results: A lower trabecular bone quantity was observed in the cemented group compared to the uncemented cohort, with discernible differences in vBMD evolution observed in the overall femur and certain Gruen zones. Noteworthy inter-patient variability was evident, ranging from physiological bone remodeling to unexpected increases/decreases in vBMD (e.g.,+340 % after one year).

Conclusions: This analysis proves to be a valuable tool to understand the long-term vBMD evolution in THA patients.

Objectives: This study aimed to determine the impact of physiological loading and boundary conditions on the biomechanical performance of a plating system for femoral shaft osteosynthesis via axial implant system testing (IST). Specifically, the effects of rotational load boundary conditions and realistic gait-based load patterns were evaluated to understand their influence on the biomechanical response and failure modes of the implant system.

Methods: Two test configurations - Fix-Free, featuring a rotational joint, and Fix-Fix, with fixed support at both ends - were subjected to static and cyclic loading. Cyclic testing incorporated sinusoidal and gait-based load patterns, reflecting the physiological axial joint load during walking. In total, 30 test samples (n=30), employed by a bone surrogate and the plate-screw system in bridge-plating state, were tested via axial IST.

Results: The Fix-Free configuration exhibited significantly lower axial stiffness and load capacity reductions of 60.8 % compared to Fix-Fix in static testing. Under cyclic gait-based loading, both setups experienced progressive screw-plate interface failures, with earlier degradation observed in Fix-Free.

Conclusions: Findings indicate a strong impact of physiological load patterns and boundary conditions. The results support the need for standards and guidelines for biomechanical testing of osteosynthetic plating systems via IST with universal physiological boundaries.

Objectives: Pilots are susceptible to fatigue during flight operations, posing significant risks to flight safety. However, single-feature-based detection methods often lack accuracy and robustness.

Methods: This study proposes a fatigue classification approach that integrates EEG features and motion behavior features to enhance fatigue recognition and improve aviation safety. The method extracts energy ratios of EEG frequency bands (α, β, θ, δ), incorporates forearm sample entropy and Euler angle standard deviation, and applies Pearson correlation analysis to select key features. Finally, a Support Vector Machine (SVM) classifier is employed to achieve precise fatigue classification.

Results: Experimental findings indicate that the proposed method achieves a test accuracy of 93.67 %, outperforming existing fatigue detection techniques while operating with a reduced computational cost.

Conclusions: This study addresses a gap in current research by integrating physiological and behavioral data for fatigue classification, demonstrating that the fusion of multi-source information significantly enhances detection accuracy and stability compared to single-feature methods. The findings contribute to improved pilot performance and enhanced flight safety by increasing the reliability of fatigue monitoring systems.

Objectives: Identification of bony landmarks in medical images is of high importance for 3D planning in orthopaedic surgery. Automated landmark identification has the potential to optimize clinical routines and allows for the scientific analysis of large databases. To the authors' knowledge, no direct comparison of different methods for automated landmark detection on the same dataset has been published to date.

Methods: We compared 3 methods for automated femoral landmark identification: an artificial neural network, a statistical shape model and a geometric approach. All methods were compared against manual measurements of two raters on the task of identifying 6 femoral landmarks on CT data or derived surface models of 202 femora.

Results: The accuracy of the methods was in the range of the manual measurements and comparable to those reported in previous studies. The geometric approach showed a significantly higher average deviation compared to the manually selected reference landmarks, while there was no statistically significant difference for the neural network and the SSM.

Conclusions: All fully automated methods show potential for use, depending on the use case. Characteristics of the different methods, such as the input data required (raw CT/segmented bone surface models, amount of training data required) and/or the methods robustness, can be used for method selection in the individual application.