bluuebunny/crossref_metadata_2025_split huggingface dataset:
Flow analysis of steady state forward flow of aortic valve prostheses using Particle Image Velocimetry
Authors: Lenz Christopher , Borowski Finja , Oldenburg Jan , Brandt-Wunderlich Christoph , Stiehm Michael , Schmitz Klaus-Peter 
Published: December 2024

Abstract
Transcatheter aortic valve replacement (TAVR) has become the standard therapy for aortic valve stenosis in patients with high surgical risk. Understanding the flow dynamics in TAVR is crucial for its evaluation and optimization. Experimental flow measurement by means of Particle Image Velocimetry (PIV) is increasingly applied alongside numerical analyses. This study introduces a novel test rig concept enabling the determination of velocity fields using Stereo-PIV under steady forward flow conditions through a TAVR during the peak systole matching the ISO 5840-1:2021 requirements. The experimental setup utilized an impeller pump to generate steady forward flow through a silicone aortic root model with implanted TAVR. A Stereo-PIV setup captured velocity fields in both ventricular inflow and aortic outflow regions. Test conditions were based on physiological flow rates determined from pulsatile measurements. Illumination of the added particles in the test fluid was achieved using an Nd:YAG laser. Fluorescent polystyrol particles (size: 50 μm) were used for flow visualization. Results showed characteristic flow patterns: a central jet flow entering the TAVR in the ventricular flow field. A jet flow directed towards the sinus side and a recirculation zone forming on the opposite side of the sinus could be detected in the aortic outflow. The width of the recirculation zone increases with distance from the TAVR. Maximum flow velocities were detected at 0.78 m/s for the ventricular flow field and 0.94 m/s for the aortic flow field. This study provides a comprehensive approach for fluid dynamic analysis of TAVR under steady flow conditions, offering insights into flow mechanics performance crucial for device optimization. Further investigations could enhance the PIV - measurement procedure by increasing both the spatial resolution and the tracer particle density within the acquired images for a comprehensive characterization of TAVR flow dynamics.
Towards Less Invasive Instruments For Cardiac Tissue Stabilizing
Authors: Moenkemoeller Raphael , Budde Leon , Martin Ulrich , Martens Andreas , Seel Thomas 
Published: December 2024

Abstract
Beating-heart surgery is performed to reduce patient trauma but depending on the intervention requires tissue stabilization through vacuum cardiac tissue stabilizers. Current designs either require open chest sternotomy or display significant residual motion when used minimally invasively. Active motion compensation methods require complex control algorithms and expensive technology. We propose a novel design for a vacuum tissue stabilizer with expandable suction feet enabling a larger stabilized area and increased motion reduction. Its performance is evaluated on a heart phantom using a silicon membrane mimicking cardiac tissue properties. The motion of the membrane is captured using a stereo camera and marker points on the membrane surface. The results are then compared to a state-of-the-art tissue stabilizer. The extended stabilizer is found to stabilize a 110% larger area while achieving similar motion amplitude reduction. Thus, extendable stabilizers seem to be a promising solution for improved cardiac tissue stabilization in less invasive beating-heart surgery.
Regulatory Requirements for Medical Devices Based on Artificial Intelligence
Authors: Nguyen My Duyen , Sohrabi Keywan , Gross Volker , Scholtes Michael , Seifert Oskar 
Published: December 2024

Abstract
Artificial intelligence is rapidly transforming healthcare, yet in Europe, there are no standardized regulations for AI-based medical devices under the Medical Device Regulation. This gap necessitates independent compliance verification by medical device manufacturers. This paper addresses the regulatory void by proposing a comprehensive training concept. It equips manufacturers with essential artificial intelligence knowledge and outlines current market introduction challenges. Through pre-tests and training sessions, the concept's efficacy will be assessed and refined. In summary, this work offers insights into AI's transformative potential and regulatory hurdles in healthcare, paving the way for improved standards and practices.
ML-Based XPS Quantification Supported by Synthetic Dataset Generation
Authors: Orth André , Höfer Hawo , Nefedov Alexei , Jalali Mehrdad , Wöll Christof , Reischl Markus 
Published: December 2024

Abstract
With growing interest in laboratory automation and high-throughput systems, the amount of generated experimental data is rapidly increasing while analysis methods still require many manual work hours from experts. This is prevalent in X-ray photoelectron spectroscopy (XPS), where quantification is a complex, time-consuming, and error-prone task. We therefore propose a neural network-based workflow to make this process more approachable. As training data availability ranges from insufficient to non-existent, our workflow creates a synthetic dataset containing XPS signals and corresponding area percentages based on binding energies supplied by the user. As a result, no previous measurements are needed. After training on the synthetic data, the neural network can predict area percentages of the known binding energies with high confidence. This workflow can therefore be adapted for XPS quantification tasks to filter significant data and supervise processes. Moreover, this enables non-experts to analyze spectra and can help experts to reduce focus on important spectra.
Development of a microfluidic setup for the isolation and imaging of immune cells
Authors: Petzold Mandy , Behrens Stephan , Polk Christoph , Sonntag Frank , Schmieder Florian 
Published: December 2024

Abstract
Specialised cells in the blood play an important role in controlling the immune responses. Thus, these cells are also valuable for the diagnosis and treatment of diseases such as autoimmune disorders and cancer. Trace-less Affinity Cell Selection technology, also known as Fab-TACS technology, is a new approach to precisely isolate specific immune cells from blood samples. Currently, this technology is used in a device called Fabian, which resembles a cassette with a capacity of 60 ml. For diagnostic purposes, however, the sample size must be reduced to a few microliters. For this purpose, a stand-alone setup for the control of a microfluidic cartridge was developed and a sequence for the implementation of the TACS technology was designed and characterized. As the combination of cell purification and subsequent cell counting in a single device can streamline workflows and save time, we also integrated an analysis platform for cell counting into the setup. Instead of using two separate devices, isolated samples can be counted immediately after isolation without interruption. This platform was initially tested with latex particles that were passed through the detection channel of the microfluidic cartridge. The functionality of the developed program sequence and cell counting was successfully demonstrated in verification experiments.
Advancements in Dexterity Measurements: the digital Nine Hole Peg Test
Authors: Prochaska Eveline , Sedlmayr Martin 
Published: December 2024

Abstract
Digitalization in the healthcare sector is progressing rapidly. In the field of measuring instruments for manual dexterity, digitization has resulted in major limitations in terms of ease of use and usability. This study describes the development of a digital nine-hole peg test (dNHPT) for assessing hand dexterity that can be used just as easily and quickly as the original nine-hole peg test. The extensive validation of the dNHPT includes the investigation of validity, reliability, clinical utility and user-friendliness This paper presents the validation of clinical utility and usability of the dNHPT. The comparison of the original ninehole peg test (NHPT) und the dNHPT show similar clinical utility of the dNHPT and the NHPT (74% of all ratings of dNHPT are equal to or better than the NHPT) and good to excellent System Usability Scale Scores of 78 (CI95: 75;81) and 85 (CI95: 80;89). Several recommendations for further development of the dNHPT were identified. The newly developed dNHPT presented here supports the quality of data collection through automated time measurement and result presentation and shows good usability and clinical utility ratings.
Concept of a human-centered electronic health record
Authors: Remlinger Wolfram , Langer Ferdinand , Welte Korinna , Maier Thomas 
Published: December 2024

Abstract
This paper examines the challenges and opportunities associated with the implementation of Electronic Health Records (EHR) globally and, specifically, in Germany. While EHRs promise improved accessibility and comprehensibility of patient data, healthcare professionals face challenges such as resource constraints and the need for user-friendly interfaces. The focus is on the design of the graphical user interface (GUI) to enhance accessibility for both healthcare professionals and patients. The proposal suggests a three-dimensional digital human model as the basis for organizing and retrieving medical documents, integrating visual and chronological aspects. Additionally, the paper explores the potential benefits of incorporating personal health data, addressing data access separation concerns for users and promoting interdisciplinary information exchange. The concept-study concludes by highlighting the importance of a well-designed user interface in facilitating effective utilization of EHR systems.
Hybrid Method for Image Reconstruction in Magnetic Induction Tomography: Combination of the Neural Network with an Iterative Analytical Algorithm
Authors: Schledewitz Tatiana , Hofmann Anna , Fehr Moritz , Sauer Andreas , Rueter Dirk 
Published: December 2024

Abstract
Magnetic induction tomography (MIT) is an imaging technique that uses eddy current effects to map the conductivity of low conductive objects. It has a wide range of applications and offers numerous possibilities in various fields. One task of this imaging method involves resolving the ill-posed and nonlinear inverse problem. However, existing methods have weaknesses. A hybrid approach that integrates theoretical algorithms with machine-learning methods is proposed as an innovative solution to the challenges of MIT reconstruction.
Design and Development of a Gaming Controller for Ankle Therapy
Authors: Shah Nazar Peiman , El Sheikh Mohammad , Pott Peter P. 
Published: December 2024

Abstract
Although rehabilitation therapies are still the most common treatment for those who have had a stroke, they can be dull and repetitive. Despite recent advances in the field of rehabilitation robots, they can be complicated to operate and needs plenty room space in most cases. This paper presents a gaming controller (GC) that acts as an interface system between the rehabilitation system and the patient. It is connected to a mechanically actuated ankle rehabilitation mechanism, which incorporates the usage of the patients’ upper limbs to work on the range of motion of the ankle. The mechanical mechanism (MM) provides the base for gamified ankle rehabilitation; adopting the mechanism with the developed GC that comprises commercial games. The GC translates the rotary actions of MM to corresponding controller in the games.
Towards Liver Segmentation in Laparoscopic Images by Training U-Net With Synthetic Data
Authors: Sleeman Joshua , Krames Lorena , Nahm Werner 
Published: December 2024

Abstract
The lack of labeled, intraoperative patient data in medical scenarios poses a relevant challenge for machine learning applications. Given the apparent power of machine learning, this study examines how synthetically-generated data can help to reduce the amount of clinical data needed for robust liver surface segmentation in laparoscopic images. Here, we report the results of three experiments, using 525 annotated clinical images from 5 patients alongside 20,000 synthetic photo-realistic images from 10 patient models. The effectiveness of the use of synthetic data is compared to the use of data augmentation, a traditional performance-enhancing technique. For training, a supervised approach employing the U-Net architecture was chosen. The results of these experiments show a progressive increase in accuracy. Our base experiment on clinical data yielded an F1 score of 0.72. Applying data augmentation to this model increased the F1 score to 0.76. Our model pre-trained on synthetic data and fine-tuned with augmented data achieved an F1 score of 0.80, a 4% increase. Additionally, a model evaluation involving k-fold cross validation highlighted the dependency of the result on the test set. These results demonstrate that leveraging synthetic data has the ability of limiting the need for more patient data to increase the segmentation performance.
Synthetic Data in Supervised Monocular Depth Estimation of Laparoscopic Liver Images
Authors: Walkner Heiko , Krames Lorena , Nahm Werner 
Published: December 2024

Abstract
Monocular depth estimation is an important topic in minimally invasive surgery, providing valuable information for downstream application, like navigation systems. Deep learning for this task requires high amount of training data for an accurate and robust model. Especially in the medical field acquiring ground truth depth information is rarely possible due to patient security and technical limitations. This problem is being tackled by many approaches including the use of synthetic data. This leads to the question, how well does the synthetic data allow the prediction of depth information on clinical data. To evaluate this, the synthetic data is used to train and optimize a U-Net, including hyperparameter tuning and augmentation. The trained model is then used to predict the depth on clinical image and analyzed in quality, consistency over the same scene, time and color. The results demonstrate that synthetic data sets can be used for training, with an accuracy of over 77% and a RMSE below 10mm on the synthetic data set, do well on resembling clinical data, but also have limitations due to the complexity of clinical environments. Synthetic data sets are a promising approach allowing monocular depth estimation in fields with otherwise lacking data.
Classification of Respiratory Diseases
Authors: Wichum Felix , Wiede Christian , Seidl Karsten 
Published: December 2024

Abstract
Contactless measurement methods offer a novel approach to assessing respiratory parameters. This study investigates the feasibility of classifying chronic obstructive pulmonary disease, asthma, and healthy individuals using depth-based plethysmography (DPG). The approach involves calculating Pearson's correlation coefficient for all pixel-wise signals against each other, with the cumulative result visualized in patient-specific masks. A convolutional neural network is used for the classification process. For evaluation, on a recorded data set (N=53), a classification accuracy of 57.7% and Cohen’s Kappa of 0.28 were reached. These findings provide indications that DPG might effectively classify respiratory conditions by analyzing respiratory motion dynamics.
Automatic Tissue Differentiation in Parotidectomy using Hyperspectral Imaging
Authors: Wisotzky Eric L. , Schill Alexander , Hilsmann Anna , Eisert Peter , Knoke Michael 
Published: December 2024

Abstract
In head and neck surgery, continuous intraoperative tissue differentiation is of great importance to avoid injury to sensitive structures such as nerves and vessels. Hyperspectral imaging (HSI) with neural network analysis could support the surgeon in tissue differentiation. A 3D Convolutional Neural Network with hyperspectral data in the range of 400 − 1000 nm is used in this work. The acquisition system consisted of two multispectral snapshot cameras creating a stereo-HSI-system. For the analysis, 27 images with annotations of glandular tissue, nerve, muscle, skin and vein in 18 patients undergoing parotidectomy are included. Three patients are removed for evaluation following the leave-one-subjectout principle. The remaining images are used for training, with the data randomly divided into a training group and a validation group. In the validation, an overall accuracy of 98.7% is achieved, indicating robust training. In the evaluation on the excluded patients, an overall accuracy of 83.4% has been achieved showing good detection and identification abilities. The results clearly show that it is possible to achieve robust intraoperative tissue differentiation using hyperspectral imaging. Especially the high sensitivity in parotid or nerve tissue is of clinical importance. It is interesting to note that vein was often confused with muscle. This requires further analysis and shows that a very good and comprehensive data basis is essential. This is a major challenge, especially in surgery.
Development and characterization of acetylated and acetylated surface-modified tapioca starches as a carrier material for linalool
Authors: Shaari Syuzeliana , Samsudin Hayati , Yin Koh Wee , Utra Uthumporn 
Published: December 2024

Abstract
Linalool compound is easily degraded when exposed to light and oxygen during processing or storage. Therefore, the objective of this work was to develop surface-modified tapioca starch as a carrier for linalool. Surface-modified starch was prepared by various treatments, i.e., enzyme treatment (using STARGEN 002 enzyme), alcohol–enzyme treatment, and acetylation; acetylation was carried out with 20% (w/w, starch dry basis) acetic anhydride. Acetyl percentage (Ac%) and degree of substitution (DS) of the acetylated samples significantly increased with different treatments due to the introduction of acetyl groups in the starch molecules. After acetylation, the percentage crystallinity as well as WR1047/1022 values decreased. The acetylated surface-modified starches showed a reduction in terms of thermal properties and gelatinization enthalpy values. Compared with native starch samples, the granules of acetylated surface-modified starch sample exhibited a rough surface and clumped structures. The encapsulation efficiency of linalool significantly increased from 3.63% to 66.76%. The encapsulated linalool with enzyme-acetylated starch showed significantly low values of linalool reduction (11.00%) over 28 days of storage due to its surface erosion and addition of acetyl groups on granular starch, as evidenced by scanning electron microscopy and Ac% as well as DS results. Among them, the enzyme-acetylated starch had the lowest values of linalool reduction, which could effectively encapsulate linalool and control the linalool reduction value.
El eclipse del profesionalismo en la era de la Rendición de Cuentas – Modelando Docentes en el Contexto del Nuevo Management Público
Authors: Sisto Vicente , Fardella Carla 
Published: December 2024

El objetivo del artículo es analizar las últimas reformas educativas emprendidas en Chile como una expresión concreta de cómo se puede reestructurar el campo de la educación pública a partir de la mirada del Nuevo Management Público (NMP,) el que instala al mercado y a la empresa como modelos, conminando a los profesores a asumir nuevas identidades laborales afines. Para ello se abordan los grandes principios orientadores de la política educativa chilena, profundizando en la política de evaluación e incentivos al desempeño docente. Esto permite dar cuenta de cómo el NMP interpela las identidades laborales de los docentes, ofreciéndoles nuevos referentes identitarios basados en la noción de que el trabajo docente es constitutivamente individual, codificable en estándares, y que el profesor debe orientarse a mejoras en resultados, impulsado por estímulos económicos individuales, buscando un desarrollo profesional emprendedor. Lo que desafía las identidades construidas históricamente.
Ciberchamanismo: el chamanismo coreano y las nuevas tecnologías
Authors: Vivas Bellido Estrella , Doménech del Río Antonio José 
Published: December 2024

Gracias a las plataformas que ofrecen las nuevas tecnologías ha surgido una nueva forma de religiosidad virtual denominada ciberchamanismo y con ella las llamadas neo-chamanas o chamanas virtuales. En este artículo se observa la adaptación que ha experimentado el chamanismo coreano tradicional y las chamanas mediante una investigación basada en la observación participante en dos instituciones de la capital de Corea del Sur. Del mismo modo, se analizará el debate académico existente entre tradición y modernidad dentro del chamanismo coreano actual.
Effect of different inocula of Meloidogyne incognita and Pseudomonas syringae pv. aptata on three cultivars of beetroot (Beta vulgaris L.)
Authors: Aqib  , Khan Manzoor R. , Siddiqui Zaki A. 
Published: December 2024

AbstractPathogenicity test was conducted of three beetroot (Beta vulgaris) cultivars using five different inocula of Meloidogyne incognita and Pseudomonas syringae pv. aptata. Plant growth parameters, chlorophyll and carotenoid contents were reduced significantly over control when 2000 or more second stage juveniles (J2) of M. incognita or 10.0 mL or more P. syringae pv. aptata per kg soil were inoculated in cultivar Rosy Red 555 and Ruby Queen while in Red Express 505, 4000 or more J2 of M. incognita or 20.0 mL or more P. syringae pv. aptata caused significant reductions. A significant increase in proline in Rosy Red 555 and Ruby Queen was observed when 2000 or more J2 of M. incognita or 10.0 mL or more P. syringae pv. aptata were inoculated while in Red Express 505, 4000 or more J2 of M. incognita or 20 mL or more P. syringae pv. aptata caused significant increase. Galling and nematode population was increased with the increase in inoculum level of M. incognita. Bacterial leaf spot indices caused by P. syringae pv. aptata was also increased with the increase in inoculum level of bacterial pathogen. Leaf spot indices were recorded 1–5 scale in different cultivars.