bluuebunny/crossref_metadata_2025_split huggingface dataset:
Fiber orientation on 3D structured collectors for electrospinning
Authors: Götz Andreas , Grabow Niels , Illner Sabine 
Published: September 2022

Abstract
Electrospinning, a standard technique for producing nonwoven fabrics, is widely used in the biomedical field. Due to local electric fields, fiber deposition is influenced by 3D surface structure of metallic collectors. For complex shaped structures, predicting the deposition and orientation of the fibers remains challenging. Investigations on well-defined structures permit a more detailed insight. In this study, metal blocks with round, slit and square holes of various sizes and depths were fabricated and covered with a thin layer of polymer fibers. The fiber arrangement of the layer was investigated by different microscopic methods. Analysis of the results showed less deposition on round holes and well-bridged aligned fibers on gaps which were less than four times wide as deep. Experimental results confirmed the theoretical predictions. Considering these findings, further work can be addressed to specific collector design to attain targeted fiber deposition.
Novel Procedure for Determining the Finger Force in Flexion Depending on the Finger Position
Authors: Carabello Alina , Henkner Rick , Drossel Welf-Guntram 
Published: September 2022

Abstract
Introduction: The design and development of a novel dynamic hand orthosis that allows individual and progressive adaptation to the patient’s condition require knowledge about the maximum voluntary contraction forces of the fingers. The following work presents a test setup and procedure for determination of isometric finger contraction forces during flexion depending on various finger positions and finger tasks. Methods: The test setup places hand and fingers in certain positions (position 0: grasping of a plate; position 1-3: circular grasping of cylinders of three different diameters). The force measurement is carried out using pressure sensing: A capacitive force sensor is placed on each fingertip. To perform various finger tasks, the subjects are asked for isometric contraction of defined finger groups and single fingers against the boundary system. For proof of the concept, measurements are performed with six subjects and compared with reference values from the literature. Results: The separated finger force of each finger can be captured during the finger tasks in varying positions. Furthermore, the total finger force of all active fingers can be determined. It is shown that the total finger force during contraction of all fingers (IMRLtask; 78.4N ± 25.8N, d2) is higher than during contraction of the index finger (25.8N ± 3.4N, d2). Furthermore, the single finger force of the index finger is higher than the index finger activity in IMRL-task. The results also show a tendency for the finger contraction force to increase with increasing flexion. Further investigations may require improvements to the method. In this proof of concept, this potential is revealed and discussed. However, the functionality of the test setup and procedure can be demonstrated.
Comparison of the Y-90 brachytherapy and Ir-192 brachytherapy of skin tumors: a simulation study
Authors: Pashazadeh Ali , Hoeschen Christoph 
Published: September 2022

Abstract
Skin brachytherapy is a successful practice for treating skin cancer patients. Although a gamma source like an Iridium-192 (Ir-192) is commonly used in the skin brachytherapy, the use of a beta source like Yttrium-90 (Y-90) has also been investigated for this purpose. In the current study, we simulated the dose distribution of a proposed Y-90 applicator and an Ir-192 Leipzig applicator in a skin phantom, and compared the performance of each of the methods in treating three tumors in three hypothetical scenarios. Results of this simulation study showed that because of the sharp dose falloff of beta radiation in tissue, the Y-90 applicator may lead to better protection of the bone and/or cartilage under thin tumors. However, it may lead to excessive skin surface dose if thick skin tumors are treated. To choose between the Ir-192 and Y- 90 applicators, one may need to consider the thickness of the tumor and the thickness of the adipose layer between the tumor and the bone.
Stochastic variational deep kernel learning based diabetic retinopathy severity grading
Authors: Siebert Marlin , Tesmer Nikolay , Rostalski Philipp 
Published: September 2022

Abstract
The retinal disease Diabetic retinopathy (DR) is one of the most probable causes of blindness. Automatic detection of DR is mostly done using convolutional neural networks (CNNs) on colour retinal images. This work in contrast uses stochastic variational deep kernel learning (SVDKL) for DR grading, combining a deep CNN with Gaussian processes (GPs) into a single end-to-end trainable model, which promises to provide predictions with a reliable uncertainty estimate exploiting approximate Bayesian inference. Evaluating the performance and uncertainty calibration of SVDKL on DR grading compared to a plain CNN, the EfficientNet-B0, preliminary results on a subset of the Kaggle DR dataset show a naturally enhanced uncertainty calibration for SVDKL over the plain CNN as well as a good diagnostic performance. Despite SVDKL achieving a slightly reduced accuracy, incorrect predictions were in closer proximity to the target stages, which is beneficial for clinical diagnosis due to minimizing the cost related to severe misclassifications.
Biomarkers based on continuous non-invasive blood pressure - review and outlook
Authors: Fortin Jürgen , Lerche Katja 
Published: September 2022

Abstract
Stationary devices for continuous blood pressure (BP) with user-friendly finger sensors are getting common in various clinical fields and new wearable BP-monitors are about to enter the medical world. Apart from the commonly used parameters systolic and diastolic BP, beat-to-beat as well as pulse shape information of these devices is not easily accessible. This article is a review of existing pattern recognition analysis and an outlook to new biomarkers giving a deep insight into the cardiovascular system and its control.
Investigation of Inertial Cavitation Induced by Modulated Focused Ultrasound Stimuli
Authors: George Benedikt , Savšek Ula , Fischer Dagmar , Ermert Helmut , Rupitsch Stefan J. 
Published: September 2022

Abstract
A passive cavitation detection setup is used to assess and verify the capability of frequency- and amplitudemodulated ultrasound burst signals on the generation of inertial cavitation. The measurements were conducted with a flowthrough tissue-mimicking phantom with a canal of dc = 1mm in diameter simulating a fine blood vessel. By means of a flow velocity of vf= 50mm/s circulating blood in the cardiovascular system is imitated. Applying signal frequencies from f = 550 − 950 kHz and peak rarefaction pressures in a range from ˆpPRFP ≈ 0.17 − 1.81 MPa, various ultrasound stimuli with alternating signal parameters were investigated in the absence and presence of a talcum-water mixture acting as cavitation nuclei. By evaluating the broadband emissions in the frequency domain and calculating the voltage spectral density Srm, the results can be compared. The findings of the present study demonstrate that each ultrasound stimulus triggers a similar strength of cavitation noise at the same Mechanical Index, representing a certain pressure. Over the whole frequency and Mechanical Index (pressure) range, each ultrasound signal type generates a voltage spectral density from Srm= 0.25 − 2.25 V/ √ Hz.
Evaluation of a nonlinear viscoelastic-plastic constitutive model in numerical simulation of thermoplastic polymers for stent application
Authors: Lebahn Kerstin , Kleine Thomas , Fiedler Nicklas , Arbeiter Daniela , Grabow Niels 
Published: September 2022

Abstract
To simulate the specific material properties of thermoplastic polymers a suitable constitutive model is essential. The parallel rheological framework (PRF) model was calibrated and evaluated in this study as potential constitutive model for polymer stent application. Tensile as well as recovery tests with different loading rates were performed using PLLA specimens. In order to calibrate the constitutive model, the conducted material tests were simulated accordingly. The parameters of the model were iteratively varied to obtain good accordance of the simulation with the material tests. In contrast to elastic plastic material models, viscoelastic material behavior can be represented with the nonlinear viscoelastic-plastic PRF model. The generated and possibly further refined model can be used for the simulation of polymer stents.
Determination of Infusion Filter Efficiency applying Dynamic Light Scattering
Authors: Kurzhals Anja , Wulf Katharina , Senz Volkmar , Eickner Thomas , Grabow Niels , Schmidt Wolfram 
Published: September 2022

Abstract
Filter membranes (pore size 0.2 μm) are frequently used to perform a sterile filtration. To characterize the pore size of filter membranes, a method to determine the particle retention of nanoparticles was defined. The Dynamic Light Scattering method was first analysed and calibrated to correlate the particle concentration with absolute intensity of scattered light. Afterwards, standard particle suspensions with particle sizes of 0.150 μm and 0.239 μm were lead through an infusion filter and the particle retention of the filter was evaluated by the determination of the original and the filtrated particle concentration. The test filter retained 91% of the smaller and 98% of the larger particles. Evaluating the particle retention with Dynamic Light Scattering could be a time saving method with lesser effort compared to existing methods.
A Comparative Overview of Digital Health Applications between Belgium and Germany
Authors: Schudt Florian , Rohloff-Meinke Christine , Koehler Niklas , Sohrabi Keywan , Gross Volker , Scholtes Michael 
Published: September 2022

Abstract
The increasing use of mobile digital technologies in our daily life is also affecting the healthcare sector. Digital health applications have the potential to improve disease monitoring and management, avoid hospitalizations, and more. A main task for the health care system is to create a framework that facilitates the identification of safe and effective apps for health care professionals and patients to generate the most health benefit as well as guide payer coverage decisions. Currently some European countries have already developed a national framework for market access approval, nevertheless only Belgium and Germany have additionally created a reimbursement system. The aim of this investigation is to compare the two different approaches of Belgium and Germany, based on the framework itself, the access requirements, and the experience so far. The overall approach seems to be similar, but scrutinizing the details, there are several significant differences. The evaluation procedure in Belgium consists of a 3-level validation pyramid while in Germany there is a normal procedure as well as a DiGA-Fast- Track. Only applications in stage 3 are reimbursable in Belgium whereas in Germany applications from provisional and permanent listing are reimbursable. The comparative overview shows that the two forerunners, Belgium, and Germany, use similar but not identical approaches, with partly relevant differences such as the evaluation procedure reimbursement and the formulation of the target group. The current situation (status 11.04.22) shows that the focus of the app indications differs between both countries. Furthermore, not a single app is listed in Belgium and Germany at the same time. From our perspective it should be a goal for manufacturers of CE marked medical devices to achieve reimbursement in as many member states as possible.
Image-based deep learning of finite element simulations for fast surrogate biomechanical organ deformations
Authors: Ziche Lorenz , Lenzig Bjarne , Bieck Richard , Neumuth Thomas , Schoenfelder Stephan 
Published: September 2022

Abstract
Introduction: We apply deep learning to emulate Finite Element simulations by exploiting neural networks as universal function approximators. A novel processing pipeline automates the incorporation of three-dimensional biomechanical simulation data into an image-based deep learning task where spatially resolved deformations are predicted using pixelwise regression. We show that a model trained in this way can quickly reproduce simulation results with reasonable preliminary performance. Methods: We trained a customized U-Net (32 input features) to predict displacement models based on the input force vectors. The pipeline was applied to a basic indentation process for an idealized reference geometry (puck) and a 3D liver model from the LiTS dataset. The generated 3D simulation data is normalized and projected onto 2D images using the midpoint circle algorithm such that x, y, and z coordinates are mapped to pixel values of RGB image channels. This way, 1500 simulations of the reference geometry and 500 simulations of the liver deformation with force-controlled, randomly initialized load distributions were performed. Simulations took about 2-5 minutes of computation time with FE models of 6000 and 10000 elements. The neural network was trained on the simulation database using pixelwise regression. Results: We successfully trained a model on the pixelwise regression of simulation results. The model optimization reached an f1- score of 0.69 and 0.61 for the puck and the liver. The displacement images were reprojected and compared to the 3D ground truth. The trained models reached an average surface distance of 3.1 ± 1.2 and 4.2 ± 2.2, as well as 4.3 ± 0.4 and 6.4 ± 0.2 for the Hausdorff distances. Conclusion: The model performance is still impractical for clinical decisions. Still, with mean computation times of under 250 ms, the approach's potential with a more extensive database and a more sophisticated deep learning architecture, e.g., GNN, is recognizable.
Reduced Steering Performance in Magnetic Drug Targeting Induced by Shielding Arising from Accumulation of Particles
Authors: Thalmayer Angelika S. , Zeising Samuel , Fischer Georg 
Published: September 2022

Abstract
Magnetic Drug Targeting is a new approach in cancer therapy, where magnetic nanoparticles are used as carriers for cancer drugs. Commonly, external magnets are employed for steering the particles inside the blood vessels towards a desired direction. However, an unwanted side effect of the steering is the accumulation of the particles underneath the steering magnet. Many researchers address the number of accumulated particles, but, to the best of the authors’ knowledge, the retroaction of the accumulation profile on the generated magnetic field and, therefore, on the magnetic steering force, has not been investigated so far. Thus, in the proposed study, the influence of the accumulation profile on the magnetic force was numerically investigated. Therefore, a 2D model of a blood vessel with particles assumed as an accumulation profile and a nearby magnet was examined. Moreover, the length, thickness, and effective susceptibility of the approximated accumulation profile and the magnet size were varied. The results reveal that the field distribution is significantly affected, especially for high effective susceptibilities. The initially applied profile amplified the magnetic force; however, when the profile accumulated, it reduced the force up to 50 %. Overall, the results reveal that the retroaction of the particle distribution on the magnetic field must be considered in a simulation model.
On Gait Stability: Correlations between Lyapunov Exponent and Stride Time Variability
Authors: Chandrasekaran Sanjay , Ngo Chuong , Lueken Markus , Bollheimer Cornelius , Wolf Alon , Leonhardt Steffen 
Published: September 2022

Abstract
Lyapunov exponent is a promising parameter to ascertain the stability of the human gait. In this work, we use a time-series model based on a second-order delay-system with inertial measurement units placed on the foot and wrist. Stability is analyzed in a localized sense, with the Lyapunov exponent computed in the temporal region between two heel-strike points, which are determined using a peak-detection algorithm. We have attempted to show correlations between variations in the stride time and stability of the gait under normal and abnormal conditions. In the latter case, we attach a weight on foot to emulate weakness. On comparison between both cases, we observe a statistical significance of p=0.0039 using Wilcoxon’s rank-sum test. Moreover, on observing the correlations between Lyapunov Exponent and Stride Time Variability, we notice a left-shift in the abnormal case, indicating a lower threshold for instability, with the Stride Time Variability being 0.07 as compared to 0.11 in the normal case.The results indicate that by exploiting the correlation between stride time variability and Lyapunov exponents, one can establish a threshold for gait stability.
Thyroid Nodule Region Estimation using Auto-Regressive Modelling and Machine Learning
Authors: Gomes Ataide Elmer Jeto , Jabaraj Mathews S. , Illanes Alfredo , Schenke Simone , Boese Axel , Kreissl Michael C. , Friebe Michael 
Published: September 2022

Abstract
Ultrasound (US) imaging is used for the diagnosis and also evaluation of thyroid nodules. A Thyroid Imaging Reporting and Data System (TIRADS) is used for the risk stratification of thyroid nodules through US images. The composition of thyroid nodules plays an important role in the risk-stratification process. The percentages of cystic and solid components in a thyroid nodule are one of the features that are can be indicative of the risk of malignancy. In this work, we attempt to classify and estimate solid and cystic regions within nodules. 20x20 texture patches were extracted from solid and cystic regions and converted into signals. These signals are decomposed into low, mid, and high-frequency bands using Continuous Wavelet Transform (CWT). A total of 36 features were extracted from the decomposed signals using Auto- Regressive Modeling. The features were fed into three different Machine Learning (ML) algorithms (Artificial Neural Networks, K-Nearest Neighbors, and Random Forest Classifier) to provide us with a classification of solid versus cystic regions in thyroid nodule US images. The Random Forest Classifier obtained an Accuracy, Sensitivity, and Specificity of 90.41%, 99% and 91% respectively which was the highest among the three chosen ML algorithms. Additionally, the output from the classification phase was also be used to determine the percentage of cystic and solid regions with a given thyroid nodule US image.
Pupillometry examinations of the human eye with the eye diagnostic device PEP-2000 – First results
Authors: Schieding Nikola , Reuter Thomas , Grundmann Andreas , Walther Sebastian , Klee Sascha 
Published: September 2022

Abstract
Pupillometry forms the diagnostic basis for numerous pathologies of the eye. For this reason, fast and accurate diagnostics in the field of ophthalmology are essential. Two examination techniques, full-field ERG and pupillometry were combined in a diagnostic device developed by ICM e.V. to reduce the examination process for both examiners and patients. In this paper, the device is examined for the quality of the pupillometry measurements. A study with 12 healthy subjects (3 f, 9 m, 36.33 ± 11.94 years) was conducted to evaluate the device. The results showed that the minimal pupil diameter is 40 % higher than the literature values. The main reason for the differences is the low light intensity of 15 cd/m2. However, the maximum pupil diameter is within the range of the researched values. The results of the pupillary reaction measurements show that the values obtained (amplitude, contraction time and peak time) are within the range of literature values. The latency time of 690 ms is 40 % too high. The reason for this could be the moderate pupil detection rate of 50-70 %. Nevertheless, plausible and comparable analysis values could be obtained with the eye diagnostic device PEP-2000. Further work will look at improving pupil detection rates.
Stretchable electrodes for biopotential measurements in wearable patches
Authors: Eyvazi Hesar Milad , Khan Dibyendu , Ingebrandt Sven 
Published: September 2022

Abstract
Soft, stretchable, and flexible electrodes offer a unique and nonintrusive option to monitor essential biopotential signals from the skin, which can be related to the functioning and status of different organs including the heart. Such stretchable electrodes, by forming an intimate contact to the human skin, not only increase the signal-to-noise ratio of the recorded biopotentials and reduce eventual signal artifacts, but are also more pleasant to wear. We developed a stretchable electrode patch for electrocardiogram (ECG) monitoring utilizing the medical-grade, transparent, and stretchable material Styrene-ethylene-butylene-styrene (SEBS). Our fabrication process is compatible with standard micro- and nano clean room processes and could be up-scaled accordingly.
Importance of interdisciplinary training for successful development of innovative biomedical implants
Authors: Doll Katharina , Doll Patrick W. , Doll Christian , Doll Theodor 
Published: September 2022

Abstract
The development of novel and innovative biomaterials and biomedical devices like medical implants is a sophisticated task that requires expertise ranging from physicians over engineers and natural scientists to regulatory officers for clinical translation to be most efficient. This short communication should highlight the specific requirements and perspectives of the different disciplines and conclude on the importance of interdisciplinary training for early-stage researchers. For this purpose, experts for clinical practice, material development, biological testing and medical device approval with profound experience in interdisciplinary research summarized and discussed current literature and knowledge. Multiple interactions right from the start of the design phase until the medical device approval could clearly be identified. As not all relevant study programs already include interdisciplinary training, implementing structured interdisciplinary doctoral programs is advised to accelerate innovative biomedical implant development.
Thrilling AI – A novel, signal-based digital biomarker for diagnosing canine heart diseases
Authors: Bisgin Pinar , Strube Tom , Henze Jasmin , Ljungvall Ingrid , Häggström Jens , Wess Gerhard , Stadler Julia , Schummer Christoph , Meister Sven , Howar Falk Maria 
Published: September 2022

Abstract
Auscultation methods enable non-invasive diagnosis of diseases, e.g. of the heart, based on heartbeat sounds. Regular, early examinations using machine learning techniques could help to detect diseases at an early stage to prevent serious health conditions and then provide optimal therapy through continuous monitoring. There is already a lot of work on human data using AI algorithms to detect patterns in signals or images. However, there is hardly no work on detecting heart murmurs with digital such as Myxomatous Mitral Valve Disease. In this paper, we present a canine auscultation project that aims to provide a tool to establish a baseline of classification parameters from audio signals that could be used to monitor canine health status by analyzing deviations from this baseline. In the future, data analysis could also lead to prediction and early detection of other diseases.