bluuebunny/crossref_metadata_2025_split huggingface dataset:
Artificial Prostate Tissue for the Simulation of the Transurethral Resection of the Prostate
Authors: Ramien Max F. , Biakop Nana Pascalessa C. , Radtke Noah S. , Knopf Christoph , Klein Stephan , Damiani Christian 
Published: September 2022

Abstract
In-vitro models for the transurethral resection of the prostate (TUR-P) require prostate phantoms whose mechanical, thermal, and electrical properties resemble those of real tissue. This work focused on the latter. Phantoms were fabricated with gelatin, pectin, and corn syrup solved in an aqueous solution of 0.4 wt% NaCl. The effect of each component on the phantom’s electrical properties could be described in a multiple nonlinear regression model. As a result, the electrical current drawn during resection of the phantom could be predicted with an uncertainty of ± 10 %. This allows for purposive adjustment of the phantom’s composition to regulate the Joule heating during resection. Particularly the pectin concentration appeared to greatly affect the electrical properties as well as the mechanical stability of the artificial tissue at higher temperatures caused by Joule heating of the system. Until now, this research focused on the development of tissue that resembles porcine musculus longissimus dorsi (MLD). In the future, the phantom’s composition will be adjusted based on data obtained through in-vivo resection of the prostate.
Bad channel detection in EEG recordings
Authors: Komosar Milana , Fiedler Patrique , Haueisen Jens 
Published: September 2022

Abstract
Electroencephalography (EEG) is widely used in clinical applications and basic research. Dry EEG opened the application area to new fields like self-application during gaming and neurofeedback. While recording, the signals are always affected by artefacts. Manual detection of bad channels is the gold standard in both gel-based and dry EEG but is timeconsuming. We propose a simple and robust method for automatic bad channel detection in EEG. Our method is based on the iterative calculation of standard deviations for each channel. Statistical measures of these standard deviations serve as indications for bad channel detection. We compare the new method to the results obtained from the manually identified bad channels for EEG recordings. We analysed EEG signals during resting state with eyes closed and datasets with head movement. The results showed an accuracy of 99.69 % for both gel-based and dry EEG for resting state EEG. The accuracy of our new method is 99.38 % for datasets with the head movement for both setups. There was no significant difference between the manual gold standard of bad channel identification and our iterative standard deviation method. Therefore, the proposed iterative standard deviation method can be used for bad channel detection in resting state and movement EEG recordings.
Effect of enzymatic degradation and hydrolysis on 3D-printed resin-based composite material for temporary dental crowns and bridges
Authors: Berghaus Eva , Roling Xeniya , Warkentin Mareike , Petersen Svea 
Published: September 2022

Abstract
Additive manufacturing of dental prostheses e.g. composite materials is continuously finding broader application in dentistry. In the oral environment, the materials are subjected to hydrolysis and accelerated hydrolysis by enzymes. With the aim to compare an additively manufactured composite with a self-curing and a CAD/CAM material of the same composition, the effect of enzymatic degradation and hydrolysis on the materials were investigated. The experimental composites consisted of 50 wt. % inorganic filler and 50 wt. % monomer matrix (BisEMA, BisGMA, TEGDMA). The influence of hydrolysis via a buffer system was investigated in comparison to enzymatic degradation by cholesterol esterase over 22 days. The detection of degradation products over time was conducted by HPLC-DAD analysis. Degradation products and monomers calibrated were: TEGDMA, BisGMA, BisEMA, MA, TEGMA, Bis-HPPP and E-bis-PA. Surface roughness of polished specimens was measured by AFM and hardness with a Vickers micro hardness tester. After enzymatic degradation and hydrolysis, BisGMA was detected in the eluates of the additively manufactured composites. The samples fabricated by CAD/CAM behaved similar during hydrolysis, where additionally MA was detected. After enzymatic degradation, furthermore TEGMA and BisEMA were detected qualitatively. In contrast, the self-curing material exhibited traces of MA, TEGMA, TEGDMA and BisGMA after enzymatic degradation. Hereof detectable upon hydrolysis were MA, TEGMA and BisGMA. Surface roughness was comparable for all manufactured samples whereas the hardness was lowest of the 3D printed material. Enzymatic degradation and hydrolysis had no effect on surface roughness and hardness. The additively manufactured composite appeared to have low susceptibility to enzymatic degradation and hydrolysis. Due to the manufacturing process, the polishability and initial hardness are poorer.
Towards the Development of a Medical App for the Detection of Diabetic Foot Syndrome and Pressure Ulcers
Authors: Zaufenberger Linda , Stampfer Philip , Mader Julia K. , Gold Herbert , Donsa Klaus 
Published: September 2022

Abstract
Diabetic foot syndrome is a secondary disease of diabetes mellitus that can lead to amputations, reduced mobility and in some cases death if treatment is delayed. The objective of this study was to collect user requirements for the development of a shoe insole accompanied by a medical app for continuous pressure measurement, applying a usercentered Design Thinking process.We conducted a systematic competitor analysis and performed 10 context-interviews with both health care professionals (HCPs) (n=7) and patients with diabetic foot syndrome (n=3) at the University Hospital Graz (Austria) and a medical supply store. Structured interview guides, tailored to user groups were used for qualitative interviews. Questionnaires on the acceptance of eHealth applications were designed for quantitative analysis (n=14 patients, n=7 HCPs). The competitor analysis identified 12 inshoe- systems for plantar pressure measurement for patients with diabetes. No current system on the EU market meets the requirements of the users interviewed in this study. Quantitative evaluation showed that 95% of future users would support the use of a medical app to monitor plantar pressure in patients with diabetic foot syndrome. Based on the identified user requirements, a first user interface was developed iteratively. Usability tests of the prototype resulted in a score of 71.7 on the System Usability Scale. The medical app will be further developed and tested in clinical studies.
Robust Colon Tissue Cartography with Semi-Supervision
Authors: Dexl Jakob , Benz Michaela , Kuritcyn Petr , Wittenberg Thomas , Bruns Volker , Geppert Carol , Hartmann Arndt , Bischl Bernd , Goschenhofer Jann 
Published: September 2022

Abstract
We explore the task of tissue classification for colon cancer histology in a low label regime comparing a semi-supervised and a supervised learning strategy in a series of experiments. Further, we investigate the model robustness w.r.t. distribution shifts in the unlabeled data and domain shifts across different scanners to prove their practicality in a histology context. By utilizing unlabeled data in addition to nl = 1000 labeled tiles per class, we yield a substantial increase in accuracy from 89.9% to 91.4%.
Characterization of Ball-milled Poly(Nisopropylacrylamide) Nanogels
Authors: Huling Jennifer , Wulf Katharina , Illner Sabine , Grabow Niels , Teske Michael 
Published: September 2022

Abstract
Poly(N-isopropylacrylamide) (PNIPAM) hydrogels are one of the most commonly studied thermoresponsive biomaterials. In this work, we investigate ball milling as a method of producing PNIPAM nanogels and confirm that the temperature responsiveness is maintained in the nanogel form based on hydrodynamic diameter and zeta potential measurements.
Mathematical lung model for local gas exchange based on EIT-measurements
Authors: Hennigs Carlotta , Becher Tobias , Rostalski Philipp 
Published: September 2022

Abstract
In this work we present a four compartment mathematical lung model to simulate the local gas exchange based on electrical impedance tomography (EIT) input. The model simulates the local and global time course of the volume, the gas fraction in the alveoli and the arterial partial pressure. The regional information about the distribution of ventilation and perfusion is extracted from EIT data. In order to illustrate the suitability of this approach, an ARDS patient is simulated. The results show good agreement with measurement data for large portions of the time and seem promising e.g. for the use in medical decision support systems.
A concept to combine a gamma probe with ultrasound imaging for improved localization of sentinel lymph nodes: a feasibility study of the concept
Authors: Pashazadeh Ali , Hoeschen Christoph , Grosser Oliver S. , Kreissl Michael C. , Kupitz Dennis , Boese Axel , Illanes Alfredo , Friebe Michael 
Published: September 2022

Abstract
This paper presents the proof-of-concept study of an adaptor allowing the combination of a gamma probe with ultrasound (US) imaging, intending to improve the detectability of sentinel lymph nodes (SLNs). The performance of the adaptor in US imaging, in terms of depth of penetration and distance accuracy, and gamma scanning, in terms of sensitivity and spatial resolution, was investigated. We observed that the quality of the US imaging through the adaptor was promising and close to that of normal US imaging. However, the performance of the gamma probe through the adaptor was fairly poor, necessitating the improvement in the design of the adaptor for better gamma scanning. This study shall provide a basis for the development of a handheld gamma-US scanner for interventional procedures and small field-of-view (FOV) imaging in the future.
Augmented Reality Application for Simulation of Mamma Palpation
Authors: Steiger Annika , Burbach Simon , Eiler Tanja Joan , Schmuecker Vanessa , Gießer Christian 
Published: September 2022

Abstract
The teaching of medicine is in a state of flux. New technologies enable students to learn faster and more efficiently than ever before. Especially the possibilities that augmented reality brings with it can be applied very well in this field. The possibility to project additional information directly into the user’s field of vision allows him to perceive additional knowledge without being distracted from the actual work. In this paper, a possibility of training Mamma Palpation is presented, which teaches students the course of action and the detection of possible tumors through virtual points. Through guideline-based interviews, added value for teaching could be highlighted.
Methods of Contactless Blood Pressure Measurement
Authors: Bajraktari Flakë , Liu Jan , Pott Peter P. 
Published: September 2022

Abstract
Contactless blood pressure monitoring displays a chance for detecting hypertension and increasing the awareness of cardiovascular diseases. Camera-based methods show high potential for replacing inconvenient cuff-based technology. Blood pressure estimation through pulse transit time (PTT) and other parameters obtained from photoplethysmography (PPG) signal present a promising alternative to conventional blood pressure measurement. In this paper, recent approaches for contactless blood pressure monitoring were reviewed. All of the articles implemented systems based on PPG, whereas most of them used PTT as measuring parameter. The results are reviewed and summarized while pinpointing key challenges and discussing expectations for future research.
Remote knee endoprosthesis monitoring – alignment requirements and prototyping of the external readout unit
Authors: Rieck Paul , Schaufler Anna , Fritzsche Holger , Bertrand Jessica , Lohmann Christoph , Boese Axel 
Published: September 2022

Abstract
Knee arthroplasty follow-up involves only a limited number of X-ray-based implant condition assessments at extended intervals. Polyethylene (PE) wear of knee endoprosthesis often progresses unnoticed until symptomatic adverse effects become apparent or implant failure occurs. A PE wear measuring concept, consisting of an implanted passive sensor and an extracorporeal readout unit, was previously presented by our group. This system is intended to enable patients to regularly check their knee implants’ condition at home. In this context, an appropriate product design for the readout unit is essential to enable the user to record correct measurements. An experimental investigation of the optimized positioning of the system's readout unit was conducted and the measurement data were analyzed. The findings were then implemented in a prototype for readout unit attachment. A sleeve with Velcro and a sewn-in readout unit has been fabricated. A cutout for the patella ensures intuitive correct alignment of the measurement system according to the experimental results. A hardware housing incorporates the electronics.
Recommendations for the Development of CFD Model to Assess the Thrombogenic Potential of Stents
Authors: Stiehm Michael , Borowski Finja , Oldenburg Jan , Öner Alper , Schmitz Klaus-Peter 
Published: September 2022

Abstract
Computational fluid dynamics (CFD) has enormous potential for the development of novel cardiovascular implants. In particular, the evaluation of the thrombogenic potential of implants is gaining importance. For this purpose, the numerical model must represent the essential properties of the real system, i.e., an implant in the human blood circulation, in a suitable form. Due to its complexity, the model building process is crucial for the successful application of the model. Currently, there is no structured guideline for the application of numerical flow simulation to which model developers can refer. Based on NASA and ASTM, we present key aspects of the model building process using CFD for stent flow as an example.
Towards Identification of Biometric Properties in Blood Flow Sounds Using Neural Networks and Saliency Maps
Authors: Henze Jasmin , Fuentealba Patricio , Salvi Rutuja , Sahare Natasha , Bisgin Pinar , Burmann Anja , Illanes Alfredo , Friebe Michael 
Published: September 2022

Abstract
In previous work, we demonstrated the potential of blood flow sounds for biometric authentication acquired by a custom-built auscultation device. For this purpose, we calculated the frequency spectrum for each cardiac cycle represented within the measurements based on continuous wavelet transform. The resulting spectral images were used to train a convolutional neural network based on measurements from seven users. In this work, we investigate which areas of those images are relevant for the network to correctly identify a user. Since they describe the frequencies’ energy within a cardiac cycle, this information can be used to gain knowledge on biometric properties within the signal. Therefore, we calculate the saliency maps for each input image and investigate their mean for each user, opening perspectives for further investigation of the spectral information that was found to be potentially relevant.
Analysing attention convolutional neural network for surgical tool localisation: a feasibility study
Authors: Jalal Nour Aldeen , Arabian Herag , Abdulbaki Alshirbaji Tamer , Docherty Paul D. , Neumuth Thomas , Moeller Knut 
Published: September 2022

Abstract
Image-based surgical tool localisation and detection play an important role in developing intelligent systems for operating theatres of the future. In literature, proposed approaches require large amounts of data that are fully annotated with tool positions in the image. In this paper, a deep learning framework, trained on binary tool presence, was evaluated for surgical tool localisation in laparoscopic images. Gradient class activation maps (Grad-CAMs) were extracted from an attention-CNN model. The Grad-CAMs were then processed to generate bounding boxes over the surgical tools. Experimental results showed better performance of the attention-CNN compared to the base CNN model with mean tool localisation precision of 72.4% and 28.3%, respectively. These results show the potential of using attention modules to improve tool localisation in laparoscopic images.
Design of an experimental platform of gait analysis with ActiSense and StereoPi
Authors: Leer Alexandra , Garcia Santa Cruz Beatriz , Hertel Frank , Koch Klaus Peter , Bremm Rene Peter 
Published: September 2022

Abstract
Gait analysis is a systematic study of human movement. Combining wearable foot pressure sensors and machine learning (ML) solutions for a high-fidelity body pose tracking from RGB video frames could reveal more insights into gait abnormalities. However, accurate detection of heel strike (HS) and toe-off (TO) events is crucial to compute interpretable gait parameters. In this work, we present an experimental platform to study the timing of gait events using a new wearable foot pressure sensor (ActiSense System, IEE S.A., Luxembourg), and Google’s open-source ML solution MediaPipe Pose. For this purpose, two StereoPi systems were built to capture stereoscopic videos and images in real time. MediaPipe Pose was applied to the synchronized StereoPi cameras, and two algorithms (ALs) were developed to detect HS and TO events for gait and analysis. Preliminary results from a healthy subject walking on a treadmill show a mean relative deviation across all time spans of less than 4% for the ActiSense device and less than 16% for AL2 (33% for AL1) employing MediaPipe Pose on StereoPi videos. Finally, this work offers a platform for the development of sensor- and video-based ALs to automatically identify the timing of gait events in healthy individuals and those with gait disorders.
XPS-Analysis of solvent residues in different polymeric biomaterials
Authors: Eickner Thomas , Lange Helge , Illner Sabine , Wulf Katharina , Senz Volkmar , Grabow Niels 
Published: September 2022

Abstract
For the use of polymers as biomaterials in the production of implants, several processing methods are needed. Besides injection molding or Fused Deposition Modeling as temperature based processes, also solvent based processes were performed such as solvent-casting and electrospinning. As fabrication of medical devices makes use of potentially hazardous or toxic solvents, they have to be extracted from the final medical device. For analyses of residual solvents different methods can be used e.g. gas chromatography (GC), coulometric elemental analysis or thermogravimetrical analyses (TGA). Here we present first results for the use of X-ray photoelectron spectroscopy (XPS) to yield elemental as well as spatial information on residual solvents in polymeric materials. Therefore we analysed PLLA as biodegradable electrospun polymer, TPC-ET as biostable polymer and a PLLA-iron composite as example of a polymer metal composite via XPS and etching processes. It was possible to determine residual halogen containing solvents in all tested polymers processed from polymeric solution. As discrimination between polymer and solvent was made possible by specific elements, XPS measurements may be a suitable tool to identify even small amounts of solvent. Thus XPS analyses will help to optimize polymer processing in order to remove residual solvents.
Saliency-assisted multi-label classification for explainable deep learning applications in endoscopic ENT navigation
Authors: Bieck Richard , Heuermann Katharina , Sorge Martin , Neumuth Thomas , Pirlich Markus 
Published: September 2022

Abstract
Introduction: In endoscopic procedures of the nasal sinuses, a critical issue for classification tasks is the ambiguous anatomical representations due to their complex composition. We investigated the potential of multi-label image-based classifications of sinus landmark combinations together with explainability methods for machine learning in an assistance function at the application level. By combining image classification and pixel attribution in a navigation function, we provide the surgeon with label predictions and additional localization cues of important pixels relevant to the model output with regard to the input image. Methods: We used 3500 label annotated video sequences from 30 recorded sinus surgeries to fine-tune a pretrained ResNet50 as the feature extractor and a classification head using binary cross-entropy on one-hot encoded target vectors of landmark classes with a RAdam optimizer over 28-32 epochs. Image augmentation and a focal loss function were added to counter over-fitting. An explainability function used the trained model to produce pixel attribution maps for predicted classes regarding individual input images. These gradient maps were summed over all classes, and pixel values above 0.20 were clustered using weighted k-means based on the gradient value of each pixel coordinate. The resulting cluster centroids were then overlapped into the endoscopic image with the predicted landmark classes. Three surgeons investigated three different overlay scenarios in a validation study. Results: The top-1 predictions reached a mean f1-score of 0.47, with the highest values of 0.71 and the lowest with 0.28. Despite over-fitting mitigation, prediction results largely depended on over- and underrepresented classes. Conclusion: The provided explainability function at the application level showed the strong potential of delivering visual cues from prediction results to surgeons at runtime to support human-machine interaction.
Bioengineering of Tubes, Rings and Panels for Guided Bone and Vascular Regeneration
Authors: Sowislok Andrea , Jennissen Herbert P. 
Published: September 2022

Abstract
The development of three dimensional geometries by electrospinning of nonwoven, bioresorbable PDLLA nanoand microfibers is a prerequisite for multimodal growth factor delivery by biohybrids. Electrospinning of 3D tubes is a formidable task since the deposited nanofibers on the rotating mandrel adhere so strongly that newly formed tubes cannot be removed. A specific electrospinning instrument setup with a "removable mandrel sleeve" technique facilitated the reproducible synthesis of tubes for guided bone and future vascular applications. It is shown, that this technique not only allows the preparation of tubes but also of self-supported 3D rings and sandwich panels which can be functionally doped by growth factors such as rhBMP-2 to biohybrids.
Modeling and Error Analysis in Camera-Based Jump Height Measurement
Authors: Webering Fritz , Emamzadehei Leili , Blume Holger 
Published: September 2022

Abstract
Introduction: In this work, we use simulated data to quantify the different failure mechanisms of a previously presented low-cost jump height measurement system, based on widely available consumer smartphone technology. Methods: In order to assess the importance of the different preconditions of the jump height measurement algorithm, we generate a synthetic dataset of 2000 random jump parabolas for 2000 randomly generated persons without real-world artifacts. We then selectively add different perturbations to the parabolas and reconstruct the jump height using the evaluated algorithm. The degree to which the manipulations influence the reconstructed jump height gives us insights into how critical each precondition is for the method’s accuracy. Results: For a subject-to-camera distance of 2.5 meters, we found the most important influences to be tracking inaccuracies and distance changes (non-vertical jumps). These are also the most difficult factors to control. Camera angle and lens distortion are easier to handle in practice and have a very low impact on the reconstructed jump height. The intraclass correlation value ICC(3,1) between true jump height and the reconstruction from distorted data ranges between 0.999 for mild and 0.988 for more severe distortions. Conclusion: Our results support the design of future studies and tools for accurate and affordable jump height measurement, which can be used in individual fitness, sports medicine, and rehabilitation applications.