bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Long-Term Effects of Unilateral Divorce Laws on the Noncognitive Skill of Conscientiousness
Authors: Hayduk Iryna , Kleinjans Kristin J. 
Published: September 2023

AbstractThis paper provides the first causal evidence of the effect of a change in divorce laws on noncognitive skills in adulthood. We exploit state-cohort variation in the adoption of unilateral divorce laws in the U.S. to assess whether children exposed to this law have different noncognitive skills in adulthood compared to those never exposed or exposed as adults. Using data from the National Survey of Midlife Development in the U.S. (MIDUS) and employing the staggered difference-in-differences identification strategy developed by Callaway and Sant’Anna, we show that divorce reform had a detrimental long-term effect on the conscientiousness of those who were exposed as children whether their parents divorced or not. Changes in parental inputs can explain most of the effect, which is greatest for men whose parents divorced.
Wall-less Flow Phantoms with 3D printed Soluble Filament for Ultrasonic Experiments
Authors: Huber Christian Marinus , Heim Christian , Ermert Helmut , Rupitsch Stefan J. , Ullmann Ingrid , Lyer Stefan 
Published: September 2023

Abstract
Tissue-mimicking materials (TMMs) typically used for ultrasound phantoms include gelatin, agarose and polyvinyl alcohol (PVA). These materials have shown sufficient similarity in ultrasound parameters compared to human tissue. Despite their extensive use for years to generate ultrasound phantoms, no simple and easily reproducible way to generate complex, wall-less ultrasound flow phantoms has been introduced. Commercially available ultrasound flow phantoms are limited to simple flow geometries that do not reflect the complex blood flow in humans. Flow phantoms with complex geometries presented in scientific publications either have walls between TMMs and the flow channel, are limited to one material, or are complicated to produce. In this contribution, we present a method using 3D printing and soluble filament that allows for the reliable and consistent production of complex flow geometries with the typical materials used for ultrasound phantoms and without any walls.
Development of a current controlled stimulation setup for investigating the effect of electrical currents on implant infections caused by biofilms
Authors: Fenko Maxim , Valtin Markus , Wiesener Constantin , Fanaei Pirlar Rima , Trampuz Andrej , Schauer Thomas 
Published: September 2023

Abstract
Introduction: Biofilms are bacterial communities that transform into a state enclosed in an extracellular polymeric substance, which makes them less susceptible to antibiotics. Those bacterial formations often develop on metal implants and cause chronic infections. Due to the severely reduced impact of antibiotics against biofilms, the effect of electric stimulation (on its own and in combination with antimicrobials) needs to be further observed, as available studies indicate a positive effect. Methods: Therefore, this work examined the development of a six-channel current-controlled stimulation setup, which enables further in vitro research on the effects of electric stimulation on biofilms. The setup controls the desired stimulation current through the load to counteract electrochemical processes, which constantly change its resistive and capacitive properties. Results: Each channel of the stimulator is able to operate within an amplitude range of 50 μA to 1 mA, a frequency range of 0 Hz to 1 kHz, and a pulse width range of 50 μs to 1 ms. The current control provides a sufficient rise time of 3.3 μs for three different stimulation modes: constant direct current (DC), pulsed DC, and biphasic-pulsed alternating current (AC). Furthermore, a graphical user interface enables the user to regulate and observe the stimulation on a computer to which the stimulator device is connected. Conclusion: The achieved variety of stimulation parameters in one device makes it possible to analyze the effect of different stimulation paradigms on biofilms and therefore enables more in vitro research which is inevitable to develop a sufficient treatment for patients with biofilm-infected implants.
Machine-based emotion-assessment in waiting rooms – a feasibility and acceptance study
Authors: Binz Eike , Seuß Dominik , Pahl Jaspar , Ko Yon-Dschun , Wittenberg Thomas 
Published: September 2023

Abstract
Background: Due to an aging society and changing health behaviors, emergency room crowding has become a major problem in western health care systems. Empowering patients and health care workers to assess necessary and relevant information is critical to streamline clinical workflows. Health kiosks, designed for services like self-check-in or (ideally contactless) health self-assessment may be instrumental in solving this issue. Based on the collected data, automated workflows such as flagging critical patients, inducing specific diagnostics or early symptomatic treatment could be implemented. Objective: Using an AI-supported software, which visually analyzes and categorizes facial expressions, the emotional status of hemato-oncologic patients in a German oncology outpatient clinic was examined. Additionally a survey was conducted, evaluating the acceptance of such a self-assessment solution. Results: 98% of the participants were not stressed by the real-time emotion analysis. However, the current set of registered emotion categories was found to be only partially sufficient to adequately describe the emotional status of the patients. More importantly, 88% of the participants found such a system to be meaningful. Also, 84% of the participants agreed that such a self-analysis could be of potential assistance. No relevant generation- or gender-specific differences could be observed. Discussion: Automated analysis of patients’ emotional status can be a first step toward a more comprehensive assessment of the respective health status. Patients, in particular the elderly, approve to the vision and development of such a system. Next steps are a further improvement of the AI-based emotion recognition software with respect to more emotional states as well as the definition, inclusion and ideally contactless acquisition of physical biomarkers (as e.g. heart rate or respiratory rate) determining physical and mental well-being.
Calculation of standardized uptake values (SUVs) and time activity curves (TACs) of mice in FDG-PET
Authors: Jafarian-Dehkordi Forough , Tanha Kaveh , Hoeschen Christoph 
Published: September 2023

Abstract
Positron Emission Tomography (PET) with 18Ffluorodeoxyglucose (FDG) is an important imaging practice in cancer diagnosis, staging, treatment planning, and response assessment. Accurate interpretation of PET results requires an understanding of FDG uptake in organs. The time activity curve (TAC) and standardized uptake value (SUV) are the two common tools to measure tracer uptake in regions of interest. The TAC provides information on glucose consumption dynamics, while the SUV involves measuring the amount of tracer taken up by a specific region or volume of interest, and then adjusting the measurement by the amount of tracer injected and the subject’s characteristics. In the current study, we measured the TACs and SUVs of the heart, brain, kidney, and muscle in ten mice injected with FDG over a period of 120 minutes. According to the TACs obtained in this study, the brain has a slower uptake than the other organs with a tendency to keep the FDG for a longer period of time. Also, the SUV of the brain showed a rising trend until the middle of the experiment with a sharp falloff afterwards. The pattern of the curves of the other three organs was almost the same. The findings of this study were in agreement with a similar study on humans and are explained by the metabolic activity and physiology of the organs studied.
Temporal bone phantom for decoupled cochlear implant electrode insertion force measurement
Authors: Cramer Jakob , Dassow Niklas , Böttcher-Rebmann Georg , Budde Leon , Lenarz Thomas , Rau Thomas S. 
Published: September 2023

Abstract
In research on cochlear implants, preclinical testing of newly developed electrode arrays and surgical tools is an essential procedure, which requires the availability of a suitable testing environment. For this purpose, human temporal bone specimens are most realistic, but their availability is limited and additional parameters such as insertion forces are hardly measurable. Therefore, the aim of this study was to develop a temporal bone phantom with realistic anatomical structures for intracochlear force measurement. The temporal bone was segmented from CBCT data of a human cadaver head. The segmented model was 3D printed with an additional artificial skin layer to enable the simulated use of surgical instruments such as a self-retaining retractor. A mechanically decoupled artificial cochlear model was realistically positioned within the temporal bone and was furthermore attached to a force sensor. The usability of the phantom was evaluated by performing automated EA insertions using an automated hydraulic insertion device. The experiments showed that the insertion forces within the cochlea could be measured without interferences from surrounding structures. Moreover, the artificial skin provided a rigid interface for the insertion tool. The new phantom is a realistic testing and training platform for cochlear implant electrode insertions with the advantage of measureable insertion forces.
Analysis of EDA and Heart Rate Signals for Emotional Stimuli Responses
Authors: Arabian Herag , Schmid Ramona , Wagner-Hartl Verena , Moeller Knut 
Published: September 2023

Abstract
Emotion intelligence is a popular research topic that stems to bridge the gap between human-machine interaction. The use of such systems varies, however, the goal remains the same, to develop a robust and accurate model for detecting and identifying emotions. Physiological signals, such as electrodermal activity (EDA) and heart rate (HR), when coupled with facial expression analyses can bolster the efficacy of the recognition system. To this end, the aim of this study is to analyse EDA and HR signals to identify reactions to emotional stimuli. The data used in this work was collected from different subjects taking part in a separate study on emotion induction methods. The obtained physiological signals were processed to identify the stimulus trigger instances and detect trends between the different administered emotional stimuli. The results showed that the EDA signal was able to pinpoint the emotional trigger with a root mean squared error (RMSE) of 0.9871. The HR signal showed inconsistencies, however, a clear trend was observed between the emotion reaction and relaxation phase. This preliminary study assessment highlights the possibility of implementing data fusion in emotion recognition systems.
Lymph node metastases detection in Whole Slide Images using prototypical patterns and transformer-guided multiple instance learning
Authors: Heinlein Lukas , Benz Michaela , Kuritcyn Petr , Bruns Volker , Hartmann Arndt , Keil Felix , Geppert Carol , Evert Katja , Wittenberg Thomas 
Published: September 2023

Abstract
Background: The examination of lymph nodes (LNs) regarding metastases is vital for the staging of cancer patients, which is necessary for diagnosis and adequate treatment selection. Advancements in digital pathology, utilizing Whole-Slide Images (WSIs) and convolutional neural networks (CNNs), pose new opportunities to automate this procedure, thus reducing pathologists’ workload while simultaneously increasing the accuracy in metastases detection. Objective: To address the task of LN-metastases detection, the use of weakly supervised transformers are applied for the analysis of WSIs. Methods & Materials: As WSIs are too large to be processed as a whole, they are divided into non-overlapping patches, which are converted to feature vectors using a CNN network, pre-trained on HE-stained colon cancer resections. A subset of these patches serves as input for a transformer to predict if a LN contains a metastasis. Hence, selecting a representative subset is an important part of the pipeline. Hereby, a prototype based clustering is employed and different sampling strategies are tested. Finally, the chosen feature vectors are fed into a transformer-based multiple instance learning (MIL) architecture, classifying the LNs into healthy/negative (that is, containing no metastases), or metastatic/positive (that is, containing metastases). The proposed model is trained only on the Camelyon16 training data (LNs from breast cancer patients), and evaluated on the Camelyon16 test set. Results: The trained model achieves accuracies of up to 92.3% on the test data (from breast LNs). While the model struggles with smaller metastases, high specificities of up to 96.9% can be accomplished. Additionally, the model is evaluated on LNs from a different primary tumor (colon), where accuracies between 62.3% and 95.9% could be obtained. Conclusion: The investigated transformer-model performs very good on LN data from the public LN breast data, but the domain transfer to LNs from the colon needs more research.
Robotic Scrub Nurse: Surgical Instrument Handling with a Granular Jamming Gripper
Authors: Schäfer Max B. , Friedrich Jolanda H. , Hotz Jonas , Worbs Lukas , Weiland Sophie , Pott Peter P. 
Published: September 2023

Abstract
The global shortage of healthcare staff has led to high workloads and subsequent risks to patient well-being. One of the professions affected is that of the scrub nurse. Robotic scrub nurse systems have the potential to reduce workload and to assist in handling surgical instruments. Existing approaches mostly use two-finger grippers or electromagnetic grippers. However, it is assumed that a granular jamming gripper is more suitable for handling various surgical instruments, regardless of material and shape. A gripping unit based on a granular jamming gripper and attached to a robotic arm is presented. For evaluation, six surgical instruments were repeatedly gripped and transported. The granular jamming gripper was found to be suitable for picking up and transferring most instruments, however, handling very flat instruments turned out to be challenging.
Third-order SVD based denoising of multi-channel ECG
Authors: Schanze Thomas 
Published: September 2023

Abstract
Denoising of time series is still a challenge, especially when the spectral components of wanted signal and noise overlap. However, denoising is an inverse problem and its solution is of often ambiguous. Standard methods often use predefined functions, e.g., sine waves, to decompose a time series into wanted and unwanted parts. More recent methods calculate the basis for the representation of a time series by the time series itself. Such a method is singular spectrum analysis (SSA). SSA uses Hankel matrix embedding of a time series and singular value decomposition to determine wanted and unwanted components, e.g., noise. The wanted components are then used to compute the wanted part of the time series. Here we present a method that provides an extension of SSA for analyzing and - in particular - denoising multi-channel time series, i.e., multi-channel SSA-based denoising of multi-channel time series (MSSAD). The performance of the new method, which is based on Hankel embedding and tensor decomposition, is demonstrated on a 12-lead ECG.
A Lightweight Framework for Semantic Segmentation of Biomedical Images
Authors: Rieken Münke Friedrich , Rettenberger Luca , Popova Anna , Reischl Markus 
Published: September 2023

Abstract
We introduce a lightweight framework for semantic segmentation that utilizes structured classifiers as an alternative to deep learning methods. Biomedical data is known for being scarce and difficult to label. However, this framework provides a lightweight, easy-to-apply, and fast-to-train approach that can be adapted to changes in image material though efficient retraining. Moreover, the framework is able to adapt to various input sizes making it robust against changes in resolution and is not tied to specialized hardware, which allows efficient application on standard laptops or desktops without GPUs. We benchmark two distinct models, a single structured classifier and an ensemble of structured classifiers, against a U-Net, evaluating overall performance and training speed. The framework is versatile and can be applied to multi-class semantic segmentation. Our study shows that the proposed framework can effectively compete with established deep learning methods on diverse datasets in terms of performance while reducing training time immensely.
Decoupling the effects of varying amplitude and frequency in the electrical stimulation of optic nerve fibers
Authors: Romeni Simone , Marino Gabriele , Pierantoni Luca , Micera Silvestro 
Published: September 2023

Abstract
Introduction. Computational models of optic nerve stimulation can allow to estimate the neural response of optic nerve fibers electrical stimulation and thus can be exploited to tune stimulation parameters to obtain a specific target perception. In principle, such tuning should be performed in an automatic way, so that the chosen stimulation parameters minimize some given cost function related to the quality of resulting the visual perception, but the use of such automatic methods is still under study and no satisfactory solution is available yet. In the absence of automatic methods, stimulation parameters are customarily set via manual tuning, which can be extremely time-consuming if performed in a non-principled way. Methods. We build biophysically-accurate hybrid models of monopolar and bipolar electrical stimulation of optic nerve fibers to study how the fibers firing rates depend upon the stimulation parameters. Results. In the case of monopolar sinusoidal stimulation of optic nerve fibers, we show that the amplitude of stimulation controls the size of the recruited cluster of fibers, and that the frequency controls their firing rate, independently. Instead, for bipolar stimulation, we show that when cross-talk is non negligible it is very difficult to obtain rules of thumb linking the firing rate of target fibers to stimulation parameters. Conclusion. We show that, if the stimulation amplitude is kept such that neighboring stimulating sites do not produce cross-talk, it is possible to reconstruct visual scenes “pixel-by-pixel” without needing any optimization process. If on the contrary current steering is required and cross-talk is non negligible, then it is very difficult to obtain rules of thumb and the development and use of automatic optimization techniques should be preferable.
From Multispectral-Stereo to Intraoperative Hyperspectral Imaging: a Feasibility Study
Authors: Wisotzky Eric L. , Triller Jost , Kossack Benjamin , Globke Brigitta , Arens Philipp , Hilsmann Anna , Eisert Peter 
Published: September 2023

Abstract
Spectral imaging allows to analyze optical tissue properties that are invisible to the naked eye. Different spectral setups have been introduced in the last years to allow intraoperative image-guidance. All systems suffer from different disadvantages, such as lack of real-time capability, limited spectral coverage or low resolution. We present a novel approach using two multispectral snapshot cameras covering different spectral ranges as a stereo-system. The proposed method allows for continuous hyperspectral imaging of the surgical scene, while also enabling 3D scene reconstruction. The study demonstrates the feasibility of this approach and its potential applications in real-time monitoring and tissue identification of surgical interventions. The paper also discusses the technical challenges and future directions for improving the imaging system.
Surgical Phase Recognition for different hospitals
Authors: Wisotzky Eric L. , Renz-Kiefel Lasse , Beckmann Sophie , Lünse Sebastian , Mantke René , Hilsmann Anna , Eisert Peter 
Published: September 2023

Abstract
Surgical phase recognition is an important aspect of surgical workflow analysis, as it allows an automatic analysis of the performance and efficiency of surgical procedures. A big challenge for training a neural network for surgical phase recognition is the availability of training data and the large (visual) variability in procedures of different surgeons. Hence, a network must be able to generalize to new data. In this paper, we present an adaptation of a Temporal Convolutional Network for surgical phase recognition in order to ensure the generalization of the network to new scenes with different conditions on the example of cholecystectomy. We used publicly available datasets of 104 surgeries from four different centers for training. The results showed that the network was able to generalize to new scenes and we obtained recognition results with accuracy up to 82% on our own six captured surgeries, performed in a different hospital. This performance is similar for test data from the hospitals of the training data, suggesting that the network can well generalize to new surgical rooms and surgeons. The findings have important implications for the development of automated surgical decision support systems that can be applied in a variety of real-world surgical settings.
A fully automated normothermic machine perfusion system for kidney grafts supporting physiological motivated flow profiles
Authors: Wiartalla Marc , Berg Frederik , Kühn Jahn , Buglowski Mateusz , Bleilevens Christian , Kowalewski Stefan , Stollenwerk André 
Published: September 2023

Abstract
Research showed that the normothermic machine perfusion of kidneys can enable prolonged storage and improve conditions compared to traditional cold storage. For research in this area, there is a demand for a long-term in vitro perfusion setup. In this work, we present a fully automated normothermic machine perfusion (NMP) system as an experimental research platform. The perfusion system is intended as a tool for researching the effects of different perfusion strategies on the kidney. To enable the automation, the NMP system consists of a blood pressure control, a circulation volume level control and a pH-regulation component. The setup is realized as a medical cyber-physical system consisting of networked embedded microcontroller nodes.
Validation of Skin Perfusion Monitoring by Imaging PPG versus Laser Speckle Imaging
Authors: Fleischhauer Vincent , Adrians Jasmin , Zaunseder Sebastian 
Published: September 2023

Abstract
Assessment of skin perfusion can reveal signs of deterioration and help to prevent critical states. Commonly applied clinical tests to capture skin perfusion are subjective and often hard to quantify. Laser speckle contrast analysis (LASCA) is a technique that can capture skin perfusion at high spatial and temporal resolution. LASCA requires, however, a complex setting and is not suited for monitoring under clinical conditions. Imaging photoplethysmography (iPPG) might be an easy to use alternative. However, direct comparisons of LASCA and iPPG, and thus proves of iPPG’s capabilities to capture skin microperfusion, are rare. In this work, we compare the longitudinal development of the amplitude of green channel and near-infrared iPPG and LASCA after application of a hyperemic test. Our results show statistically significant increases in amplitude over time in all modalities. The maximum increase in median normalized amplitude is 1.281 for the green channel, 0.594 for near-infrared and 1.111 for LASCA. Median Spearman’s rank correlation coefficient of the amplitude for green channel and LASCA is r= 0.89 and r= 0.71 for near-infrared and LASCA.