bluuebunny/crossref_metadata_2025 huggingface dataset:
A triangle-shape region of interest in cardiorespiratory estimation during sleep monitoring
Authors: Haghi Mostafa , Asadov Akhmadbek , Boiko Andrei , Seepold Ralf
Published: September 2023

Introduction: With advancements in sensor and communication technologies, sleep monitoring is moving out of specialized clinics and into everyday homes. Extracting sleep-related data using far less complicated tools and procedures is possible than polysomnography. Respiratory and cardiovascular data are extracted from the signals such as the electrocardiogram (ECG), photoplethysmogram (PPG), and ballistocardiogram (BCG) to identify the aberrant respiratory events of apnea/hypopnea as well as to estimate sleep parameters. However, due to the different sleeping positions, such systems lack accuracy and/or complicated sensor network topology. In this work, we proposed an optimal topology of forcesensitive resistor (FSR) sensors to simplify the system design by identifying the region of interest for estimating cardiorespiratory parameters with minimal error. The sensors are deployed under the mattress and located on the bed frame. Methods: We proposed a low-cost, unobtrusive, non-invasive, and reliable solution with robust signal processing algorithms for cardiorespiratory measurements and automatic signal validation based on signal quality. The solution is established based on a multi-physical layer (MPL) and sensor interfaces coping with the embedded system’s specifications, and signal processing is performed onboard with two independent and simultaneous pipelines for heart rate and respiratory rate using discrete wavelet transform (DWT) and bandpass filter, respectively. Results: We identified the three most contributing FSR sensors forming a triangle shape covering the left upper side of the subject (in the supine position) as the region of interest. We reduced the mean absolute error (MAE) to as low as 3.94 and 2.35 for heart rate and respiratory rate. Conclusions: The approach with the topology of triangle-shaped performs appropriately in estimating the cardiorespiratory parameters in all four regular sleeping positions, i.e. supine, prone, left lateral, and right lateral.
Removing noise and overlapping spikes from extracellular recordings using a regularized denoising autoencoder
Authors: Samann Fars , Meyer Luca , Schanze Thomas
Published: September 2023

One of the difficulties in sorting extracellularly recorded neuronal action potentials, known as spikes, is noise from several sources superimposed on the target spike. For example, the activity of other neurons is often overlaid on the recorded spikes, denoted as neuronal noise interference (NNI), and interference from neighbouring neurons firing at the same time is known as overlapping spikes. In addition, the noise induced by the recording device is known as Gaussian white noise (GWN). Thus, the appropriate signal denoising process of extracellular recordings is an extremely challenging task, where the classical filtering approach may fail specially in removing the overlapping spikes due to the similarity between the spike waveforms of the neighbouring neurons and the wanted single-unit waveform. This issue is tackled in this work by training regularized denoising autoencoder (RDAE) model with the average spike-waveforms of previously identified single-units, denoted as ground truth. To capture the distinct features of the wanted single-unit waveforms, and weight regularization were employed at the encoder/decoder of the RDAE, respectively. With a suitable regularization parameter, the -RDAE model outperforms the -RDAE model. In general, RDAE shows extraordinary performance not only in removing the NNI and GWN, but also the overlapping spikes.
Development of an Intelligent Walking Aid for Fall Detection
Authors: Schröter Eliane , Thanh Nghi Doan , Schneider Armin
Published: September 2023

The medical field has made significant improvements over the past decades, leading to an older and healthier population. One of the main core problems being tackled is the detection of falling events. The quick detection of a fall contributes greatly to the fast arrival of help and prevents further damages. Therefore an intelligent crutch was developed with the purpose of detecting a fall and the following call for help if needed, as well as conveying the location of the user to relatives and care givers. The crutch was evaluated in a series of experiments during which all falls were identified correctly and alerting was initiated.
The ARTEMIS project: Mixed-Signal IC for Edge-AI-based Classification of ECG Signals
Authors: Hoyer Ingo , Roßman Özgü , Utz Alexander , Straczek Lukas , Akboyraz Onur , Hessel Sebastian , Lüdecke André , Seidl Karsten
Published: September 2023

Atrial fibrillation (AF) is a common heart arrhythmia and is closely associated with causing strokes. Diagnosis is usually performed with Holter monitors over longer periods of time, causing discomfort to the patient. The proposed mixed-signal integrated circuit (IC) is designed for small patch electrocardiogram (ECG) devices and combines, an analog front-end (AFE) with tailored recording channel characteristics and 12-bit successive-approximation-register analog digital converter (SAR ADC) as well as an RISC-V based microcontroller (μC) for edge artificial intelligence (AI)-based AF-detection. The digital signal processing is supported with hardware accelerators. Including 160 kB of SRAM, the system on chip (SoC) requires 25.56 mm² in silicon area in a 180 nm technology. The recording channel shows promising simulation results with an input impedance of 230 MΩ, an input referred noise of below 1.6 μVrms and a CMMR of 95 dB. The digital part enables the integration of AI-based classification on the IC. Due to the flexibility of the software-based classification approach, this IC can also be used to detect other arrhythmias.
Audio-based tissue classification - preliminary investigation for a needle procedure
Authors: Serwatka Witold , Heryan Katarzyna , Sorysz Joanna , Illanes Alfredo , Boese Axel , Krombach Gabrielle A. , Friebe Michael
Published: September 2023

Image-guided and minimally invasive procedures still require confirmation on having reached a target. Intraoperative imaging is not always sufficient or conclusive as it comes with artifacts that can come with a certain amount of ambiguity and inaccurate location information. As an alternative to imaging, we want to explore sounds produced by the biopsy needle tip while advancing and interacting with tissue. In this paper, we show that by analyzing vibroacoustic signals acquired at the proximal end of the needle we are able to differentiate the tissue type. In total, 419 audio samples of 5 tissues were acquired and converted to spectrograms used as input to a convolutional neural network. Using this experimental setup we were able to differentiate the tissue types with an F1 score of 71.64%. Based on these results we were able to demonstrate the feasibility of our approach, as well as the importance of further experiments to ensure that vibroacoustic sounds produced by the needle tip can be a new navigation method.
Optimization Techniques for Semi-Automated 3D Rigid Registration in Multimodal Image-Guided Deep Brain Stimulation
Authors: Al-Jaberi Fadil , Fachet Melanie , Moeskes Matthias , Skalej Martin , Hoeschen Christoph
Published: September 2023

Multimodal image registration is vital in Deep Brain Stimulation (DBS) surgery. DBS treats movement disorders by implanting a neurostimulator device in the brain to deliver electrical impulses. Image registration between computed tomography (CT) and cone beam computed tomography (CBCT) involves fusing images with a specific field of view (FOV) to visualize individual electrode contacts. This contains important information about the location of segmented contacts that can reduce the time required for electrode programming. We performed a semi-automated multimodal image registration with different FOV between CT and CBCT images due to the tiny structures of segmented electrode contacts that necessitate high accuracy in the registration. In this work, we present an optimization workflow for multi-modal image registration using a combination of different similarity metrics, interpolators, and optimizers. Optimization-based rigid image registration (RIR) is a common method for registering images. The selection of appropriate interpolators and similarity metrics is crucial for the success of this optimization-based image registration process.We rely on quantitative measures to compare their performance. Registration was performed on CT and CBCT images for DBS datasets with an image registration algorithm written in Python using the Insight Segmentation and Registration Toolkit (ITK). Several combinations of similarity metrics and interpolators were used, including mean square difference (MSD), mutual information (MI), correlation and nearest neighbors (NN), linear (LI), and B-Spline (SPI), respectively. The combination of a correlation as similarity metric, B-Spline interpolation, and GD optimizer performs the best in optimizing the 3D RIR algorithm, enhancing the visualization of segmented electrode contacts. Patients undergoing DBS therapy may ultimately benefit from this.
Using adaptive learning rate to generate adversarial images
Authors: Ding Ning , Möller Knut
Published: September 2023

Convolutional neural networks (CNNs) have proved their efficiency in performing image classification tasks, as they can automatically extract the image features and make the corresponding prediction. Meanwhile, the CNNs application is highly challenged by their vulnerability to adversarial samples. These samples are slightly different from the legitimate samples, but the CNN gives wrong classification. There are various ways to find the adversarial samples. The most common method is using backpropagation to generate gradients as the directed perturbation. Contrarily to set a constrained limitation, in this paper, we use iterative fast gradient sign method to generate adversarial images with the minimum perturbation. The CNNs were trained to perform surgical tool recognition as a configuration for the modern operation room. The coefficient or the learning rate which influenced the modification per iteration, was set to be adaptive instead of a fixed number. A few functions were utilized to perform the learning rate decay to compare the performance. Especially, we propose a new adaptive learning rate algorithm that consider the loss as a part of influence factor constitute the learning rate for the rest iterations. According to the experiments, our loss adaptive learning rate method was proved to be efficient to get the minimal perturbations for adversarial attack.
Fabrication and characterization of flexible microelectrode arrays for the long-term recording of mammalian brain slices
Authors: Frese Hanna , Kauth Andrea , Koch Henner , Ort Jonas , Ingebrandt Sven
Published: September 2023

A profound understanding of brain disorders is essential for developing more effective treatments, targeted therapies, and reliable diagnoses. Electrical in vitro long-term and repetitive measurements give insight to neural activity of more complex neural circuits and, thus, into malfunctioning caused by illness. Flexible microelectrode arrays (flexMEAs) enable the recording of neural activity with promising spatial and temporal resolution over longer durations. The present study aims to combine the long-term recording by flexMEAs with the cultivation of brain slices inside an incubator. An 8 μm-thin flexMEA with up to 256 electrodes with 30 μm in diameter and interelectrode spacing of 200 μm was fabricated. Characterization showed uniform and reproducible electronic characteristics. Coating with PEDOT:PSS by electropolymerization formed stable low-impedance spectra. The platform is ready for future experiments with brain slices inside an incubator.
Biphasic parameter determination of sheep articular cartilage from stress relaxation data using an optimized 3D FE-based method
Authors: Reuter Thomas , Horbert Victoria , Kinne Raimund , Hurschler Christof
Published: September 2023

Mechanical characteristics of hard and soft tissues are central features for the quantitative description of tissue properties. In this study, we present a biphasic 3D-FE-based method to determine the biomechanical properties of sheep stifle joint articular cartilage (load-bearing area of the medial and lateral femur condyle, n = 28) from stress relaxation indentation tests (s = 0.1 mm, t = 180 s). The FE-model computation was optimized by exploiting the axial symmetry and mesh resolution. Parameters were determined using the Levenberg-Marquardt-algorithm. Results showed significant differences between the biomechanical parameters of the lateral (n = 11) and medial (n = 17) femur condyle of the sheep stifle joint articular cartilage. R² of the fit results varied between 0.91 and 0.99. Overall values for the Young’s modulus were 1.316 ± 0.778 MPa, for the Poisson’s ratio 0.106 ± 0.088 and for the permeability 0.008 ± 0.004 mm4/Ns. Future work will focus on studying the influence of experimental settings on the results obtained with the biphasic 3D-FE-model.
Fast 4D FEM Model for EIT Source Separation Benchmarking
Authors: Silva Diogo Filipe , Leonhardt Steffen
Published: September 2023

The accurate separation of cardiac and ventilatory contributions to electrical impedance tomography signals is crucial for complete and non-invasive cardiorespiratory monitoring. However, no consensus on a suitable source separation algorithm was achieved despite several proposals due to lacking systematic evaluation. To address this, we propose a benchmarking 4D finite element method generative model for mixed, cardiac, and ventilatory signals. Our model implements dynamic modelling of the heart, lungs, and pulmonary arteries using realistic volume and flow curve templates, along with cardiac and respiratory frequency coupling.We also employed variable alveolar and blood conductivities. The model was able to obtain long recordings faster than comparably complex models while maintaining significant physiological effects and signal properties such as non-stationarity, spatial delays, time and frequency profiles. The realistic physiological model can be used to taxonomize and evaluate source separation algorithms, as well as aid in the development and training of new ones.
Diagnostic medical device for stabilized image perception of retinal blood vessels using entoptic phenomena
Authors: Tautorat Carsten , Lebahn Kerstin , Schmidt Wolfram , Guthoff Rudolf , Stahnke Thomas , Lips Thomas , Grabow Niels
Published: September 2023

A diagnostic medical tool is described that allows patients to non-invasively observe, describe and document their own retinal blood vessels using the Purkinje vascular entoptic test. We envision key applications of the Purkinje retinal image stabilization (PRIS) device in monitoring of diabetic retinopathy and cost-effective assessment of retinal visual acuity. To optimize our PRIS principle, we have developed a benchtop device with interchangeable light modules. The modular design is intended to provide maximum flexibility for determining appropriate PRIS stimulation parameters. In self-tests, we found that a pleasant entoptic perception occurs when a green luminous light spot (approx. 525 nm) moves on a circular path. The challenging part is to derive an objective diagnosis from a subjective phenomenon.
Accuracy of CT numbers and electron density calibration for mixtures of materials with low and high-atomic number
Authors: Ese Zehra , Erni Daniel , Zylka Waldemar
Published: September 2023

In computed tomography (CT) materials with high-atomic number Z cause image artefacts, thus, errors in CT numbers given in Hounsfield Units (HU). Also, the conventional HU scale (CHU) implemented in CT scanners is truncated, i.e., it does not cover high-Z materials. These restrictions lead to incorrect mapping of CT numbers to electron density, which are used in radiotherapy (RT) treatment planning systems (TPS). Even analytical conversion methods are only permissible for tissue-equivalent materials. In terms of HU-to-density conversion in RT TPS, we investigated the CT numbers of material mixtures up to Z<29 at the CHU and an extended-HU (EHU) scale, respectively, and quantify the systematic errors of image artefacts. In [1] the feasibility of a stoichiometric analytical calibration method were analyzed for metals and adapted for higher accurcy, for energies of 80 kV and 120 kV. In this work, we add results for 100 kV and 140 kV to cover the wide diagnostic range. The CT numbers are effected by physical and machine-based properties and depend strongly on the energy, e.g., for Cu a HU difference of 6 171HU at 80 kV and 140 kV occured. The analytical calibration parameters change with energy by a factor between 2 and 10 depending on the physical process. Although for high- Z materials our calibration procedure remains in conflict with rigorous physics [2], it offers an improved and a practical way to predict electron densities from CT numbers.
Development of quality criteria for long-term testing of intracortical electrodes
Authors: Sielaff Charlotte , Urban Ute , Froriep Ulrich P.
Published: September 2023

The use of intracortical electrodes is a crucial method for medical diagnosis and treatment and holds the promise for overcoming various neural diseases and challenges in prosthetics. The general physical and electrical function as well as the biocompatibility is extensively tested before intracortical electrodes are implanted, however, their longterm performance, is still limited through tissue scarring and electrode deterioration, and consequently a decrease in signal quality. To overcome this challenge, aspects such as materials selection, coatings, flexibility, and surgical procedures are often addressed. To date, however, a systematic, industry - compatible process for long-term functional testing of new intracortical electrodes technologies is still lacking. Implementation of a general overall in vivo testing procedure with defined quality criteria would enable reliable in vivo longterm (> 30 days) device assessment. This could reduce the risk in developing probes for long-term performance and pave the way for clinical application in patients suffering from neural diseases.
Analyzing the Effect of Age and Gender on the Blink Reflex using MediaPipe
Authors: Güney Gökhan , Jansen Talisa S. , Braczynski Anne K. , Rohr Maurice , Dill Sebastian , Hoog Antink Christoph
Published: September 2023

The glabellar tapping reflex (GTR) is a sign related to brain conditions and can be analyzed by clinicians for diagnostic purposes. To facilitate the quantitative analysis of this reflex, we developed a video-based tool using the MediaPipe framework. We tested our approach on healthy subjects to assess the effect of age and gender on reaction time and blinking duration. The reaction time results show that the young group has a mean value (±standard deviation) of 0.091 (±0.066) seconds and the old group has 0.085 (±0.052) seconds, while female and male subjects have 0.097 (±0.053) seconds and 0.080 (±0.064) seconds respectively. For blinking duration, males have a mean value of 0.216 (±0.077) seconds and, females have 0.189 (±0.115) seconds, while old and young groups have 0.132 (±0.039) seconds 0.267(±0.084) seconds respectively.
Defibrillation data analysis of prehospital resuscitation scenarios having different impulse durations
Authors: Fischer Jonas , Wenzel Folker
Published: September 2023

The objective of this study was to assess and present technical data related to resuscitation procedures that involved the application of a defibrillator for at least one shock. Specifically, we sought to distinguish two rectangular defibrillation impulses in terms of varying phase durations. The evaluation encompassed a range of defibrillation metrics and metadata, including transthoracic impedance, mean and peak currents. The results of this analysis provide valuable insights into the technical aspects of defibrillation.
Computer-assisted generation of anaesthesia report during laparoscopic procedures
Authors: Aldeen Jalal Nour , Abdulbaki Alshirbaji Tamer , Laufer Bernhard , Docherty Paul D. , Neumuth Thomas , Moeller Knut
Published: September 2023

Automatic generation of intraoperative anaesthesia reports has great potential to generate accurate and efficient documentation and allow the anaesthesiologists to utilise their time more optimally. However, automatically generating anaesthesia reports represents a demanding component in future operating rooms. In this paper, a computer-assisted system for anaesthesiology report generation during laparoscopic surgery is proposed. Data from anaesthesia machine, patient monitor, and surgical devices were acquired and analysed. The system proved to generate more accurate, efficient, and better readable reports compared to hand-written ones by clinicians. Moreover, additional information that describes surgical activities and events was also added by analysing surgical data.
Symmetry of Respiration Induced Upper Body Movements
Authors: Laufer Bernhard , Krueger-Ziolek Sabine , Aldeen Jalal Nour , Docherty Paul D. , Hoeflinger Fabian , Reindl Leonhard , Moeller Knut
Published: September 2023

Many researchers have postulated inexpensive methods to estimate respiratory volumes from respirationinduced movements of the upper body. In the development of smart garments, the number and the optimal positioning of sensors is an essential aspect. Therefore, the symmetry of upper body respiratory movements was investigated based on the measurement data from a motion capture system. In the ventral central area, coefficients of determination above 0.99 regarding movements of symmetrical markers mean that sensors can be placed without restrictions in the position mirrored on the sagittal plane. This simplifies sensor positioning in a smart shirt and expands the range of applications of smart shirts for respiratory monitoring.
Randomized Registry Trials - efficient post market clinical follow-up for medical devices
Authors: Leuchter Matthias , Siewert Stefan , Stiehm Michael , Klar Ernst , Schmitz Klaus-Peter , Kaule Sebastian
Published: September 2023

In spite of advances in the treatment of cardiovascular disease, there remains a significant need for high-quality evidence for clinical practice given the rising prevalence worldwide. Next to the increase of regulatory requirements, poor screening-to-recruitment ratios, lack of patient engagement, and long timelines for contracting and institutional review board (IRB) approval are leading to an outflow of clinical trials in the EU. Patient registries are a potentially rich source of data, especially for evaluating the course of rare diseases and effects of new treatments. In the sectors of regulatory assessments of medical devices they are greatly underused. Health-related real world data provide crucial support for regulatory decision-making, particularly post-market assessments of medical products. Registries can be used not only for observational studies, but also for conducting experimental study designs. Register-based randomized controlled trials (RRCTs) are of particular importance here, as they can combine the advantages of randomized controlled trials (RCT) and registers. The government's financial support and reduction of regulatory barriers to the implementation of registry-based RCT are incentive factors for manufacturers to let their medical devices (re-)certified. Furthermore, it is essential that the necessary criteria are already taken into account when setting up the German implant registry in order to enable future RRCTs. The presented work is an additional pleading for the use and extension of registries for all medical products.
Post-modification of PLLA nanofiber nonwovens by various solvent treatments
Authors: Götz Andreas , Reske Thomas , Oschatz Stefan , Grabow Niels , Illner Sabine
Published: September 2023

Degradable polymeric Biomaterials, such as polylactic acid, are widely used in medical field as bioresorbable implant coatings. More recently, stent covers, implantable tubes or wound dressings made from electrospun nanofibers have been developed. In order to tailor the material for specific applications, dedicated fine-tuning procedures are required. To modify fiber interconnection or porosity, solvents or their dilutions are applicable. In this work, the effect of differently diluted protic and aprotic-polar solvents on electrospun poly-L-lactic acid (PLLA) nonwovens was investigated via immersion or vapor exposure. Fiber adhesion was observed with protic solvents. Fiber destruction and increased polymer crystallinity was observed with aprotic polar solvents. The immersion duration revealed to be far more irrelevant than solvent dilution. None of the tested solvents affected molecular weight. This opens up significant research potential to replace halogenated solvents as a cornerstone of eco-friendly “green” electrospinning processes. The results of this study are suggesting that biomaterials with desired properties can be produced in a targeted manner.
Non-invasive glucose sensing via the fingernail bed using THz radiation
Authors: Jalali Mandana , Prokscha Andreas , Yan Yijun , Kubiczek Tobias , Taro Svejda Jan , Preu Sascha , Balzer Jan , Kaiser Thomas , Erni Daniel
Published: September 2023

The possibility of establishing a novel technique for reliably accessing glycemic information in a non-invasive, easy to implement method at THz frequencies via the fingernail bed is investigated. The nail bed’s major content is blood at its various glucose levels and is also partially protected from environmental conditions by the nail plate, making it a desirable platform for non-invasive glucose sensing. The study is based on a 2D computational electromagnetics (EM) model of the layered fingernail in COMSOL Multiphysics, where the required dielectric function (i.e. permittivity and conductivity) of the fingernail plate is measured using THz-Time-Domain- Spectroscopy (THz-TDS), and the glucose-dependend dielectric functions of the fingernail bed are taken from available experimental data in the literature. From this data a material model using a multipole Cole-Cole model is established for both, the nail plate and the nail bed, where the glucose content in the latter is varied from 3.0-19.0 mmol/l. A numerical analysis of the THz reflectometry at the fingernail in the frequency range of 0.1-2.0 THz revealed that the reflectance spectra are sensitive to the glucose content in the nail bed proving that accurate glucose sensing via the fingernail bed may become feasible around 0.2-0.4 THz.
Telemonitoring – Regulations and Opportunities
Authors: Ikar Faduma , Bui Jessica , Musse Nafisa , Hahn Kai , Brück Rainer , Keil Alexander
Published: September 2023

Regulation compliance, privacy, and security issues all need to be taken into consideration while using remote patient monitoring. This article on current developments in remote healthcare and monitoring sheds light on contactless techniques and draws attention to the difficulties still facing the deployment of remote patient monitoring. It is summarized how remote patient monitoring is already being used, paying special attention to Germany and its regulatory framework.
Smartphone-Based Videonystagmography Using Artificial Intelligence
Authors: Reinhardt Sophia , Schmidt Joshua , Schneider Jonas , Schulte Elena , Schüle Christiane , Leuschel Michael , Schipper Jörg
Published: September 2023

Dizziness is a common symptom in medicine. The anamnesis and detection of a nystagmus is essential to distinguish a vertigo's pathogenesis. The diagnosis is complex, expensive, and not always available across the board. We present a novel location- and time-independent mobile application for videonystagmography (VNG) to support vertigo patients and medical staff. No additional hardware is necessary. The app uses artificial intelligence for eye tracking and to detect a horizontal nystagmus. A feasibility study of the mobile VNG with 13 healthy volunteers was performed. Each participant underwent a caloric vestibular testing to provoke the presence of a vestibular nystagmus. It could be shown that a smartphone-based VNG is possible.
Multifrequency bioimpedance device based on the Analog Discovery 2: performance and characterization
Authors: Battistel Alberto , Craamer Lizarraga Hegoa , Termenon Maite , Möller Knut
Published: September 2023

Bioimpedance spectroscopy can be used to investigate the composition and monitor the human body, organs, tissues, or cell cultures by measuring the voltage developed by the injection of small alternating currents at different frequencies. These currents are injected sequentially through a frequency sweep and a with a Howland current source or one of its modifications. However, the frequency sweep is not time efficient and introduces problems with data coherence in the case of bioinstability. On the other hand, the Howland current source requires high precision matching between its components. In this contribution we developed a custom-made device for bioimpedance measurements based on a multisine current waveform and on a negative-feedback topology for the current source. Measurements on passive elements showed that the device had less than 1Ω and 0.05∘ uncertainty in the frequency range between 500 Hz and 200 kHz for impedance between 1 kΩ and 10 kΩ. The measurements were affected by an inductive artifact connected with the limited common-mode rejection at high frequencies. Nevertheless, we could characterize the artifacts through a fitting procedure to recover the expected value of the targeted impedance.
Ag sputter coating of PLLA nonwovens
Authors: Teske Michael , Illner Sabine , Tautorat Carsten , Lebahn Kerstin , Schmidt Wolfram , Senz Volkmar , Grabow Niels
Published: September 2023

Physical vapor deposition (PVD) sputtering is a promising technique for the generation of electrically conductive coatings on complex structured biomaterials, such as nonwovens, that can be used in implants. The ultra-thin coatings can fulfil a variety of purposes, such as drug depot or biosensor. The aim of our studies was to determine the feasibility of generating thin silver (Ag) coatings on complex structured poly-L-lactide (PLLA) nonwovens by sputtering and their morphological characterization. PVD sputter process was performed using a customized plasma chamber and a complex rotation of the tender electrospun PLLA nonwovens. Morphological analysis revealed a promising homogenous Ag coating, representing the structure of the nonwovens.
Enhancement and labelling of OCT images
Authors: Karthik Karri , Mahadevappa Manjunatha
Published: September 2023

Optical Coherence Tomography (OCT) is a noninvasive technique for visualizing retinal cross-sections, assisting in diagnosing and monitoring retinal diseases. This study presents an effective OCT image noise reduction and segmentation method. Due to the high resolution of our primary image database, a visual comparison of filter performance was challenging. To address this, we utilized a high-noise image dataset from Duke University, enabling a better evaluation of noise reduction filters. Our method was compared against eight widely-used image filters, including Gaussian, Low pass,Wavelet domain filtering, Lee filter, Anisotropic diffusion, Bilateral filter, Total variational filter, and BM3D filter. Both visual and quantitative analyses were conducted using no-reference performance parameters, namely Gradient Magnitude Similarity Deviation (GMSD), Standard Deviation of Wavelet Coefficients (SDWC), Focus Measure with Tengrade Variance (FMTV), Perception-based Image Quality Evaluator (PIQE), and Visual Information Fidelity (VIF). The results demonstrated the superiority of our proposed filter in terms of noise reduction performance while maintaining the sharpness of retinal layers. Quantitative analysis revealed notable performance gains, including improvements of 63.27% to 83.24% with GMSD, consistent edge strength enhancement of 9.4% to 9.97% using SDWC, gains in image quality between 51.99% and 54.64% with FMTV, performance improvements ranging from 7.25% to 23.97% in terms of PIQE, and a substantial increase in performance varying from 16.31% to 27.69% when assessing the impact on retinal layer quality using VIF. Using the Kruskal-Wallis test, our proposed noise reduction method shows statistical significance for all quantitative parameters. Additionally, our clustering algorithm effectively separated the foreground, including retinal layers and vitreous detachment, from the background and identified an area representing the region between retinal layers where fluid accumulates. We’ve successfully achieved OCT image enhancement, along with distinct foreground and background segmentation.
Investigation of manufacturing deviations of CPC scaffolds for improving the design process
Authors: Seidler Alexander , Pendzik Martin , Hilbig Arthur , Sembdner Philipp , Holtzhausen Stefan , Paetzold-Byhain Kristin
Published: September 2023

In current implantology, calcium phosphate cement (CPC) is increasingly used. A special focus is given to CPC scaffolds, as they are suitable for cell settlement and growth due to their positive osteoconductive properties. The design of the scaffolds is of decisive importance for this. The value ranges of the geometric parameters of these scaffolds (e.g. path distance, path diameter), which are positive for both cell settlement and cell growth, are very small. Manufacturing deviations therefore have a significant impact on cell settlement and growth. The pasty manufacturing consistency can cause sagging at the path interstices of a layer immediately below, resulting in significant manufacturing deviations. A larger path distance and thus a larger path interstitial space promotes cell settlement, but at the same time increases the risk of CPC path shape inconsistency. This in turn can have a negative effect on cell settlement. Therefore, the aim of this paper is to investigate the discrepancies between the nominal and actual state at a path distance favourable to cell settlement on the basis of manufactured CPC scaffolds. In this context, geometric and manufacturing parameters of the shape deviation are to be identified and constructive design adaptations are to be derived on the basis of these. In addition, the effects of the shape and position deviations on the flow behaviour will be investigated.
Method for fabrication of laser-induced periodic surface structures on vascular stents
Authors: Grossmann Swen , Bohne Eric , Stiehm Michael , Schmitz Klaus-Peter , Siewert Stefan
Published: September 2023

Laser-induced periodic surface structures (LIPSS) provide the possibility to functionalize the surface of various materials, which can be used to control the wettability of metal surfaces or influence cell adhesion. LIPSS thus offer great potential for application on implant surfaces if their fabrication process is compatible with standard implant fabrication techniques. Here we demonstrate an easy-to-use two-step fabrication method for stents with different types of LIPSS. The separation between the fabrication of the stent structure and the surface structuring process allows the implementation of any stent structure finishing technique without affecting the LIPSS. The suggested fabrication method may be a step further in implementing laser machining-based surface functionalization in commercial implants.
Design of a Flexible mechatronic system for mechanical impedance measurement of the wrist as one step towards robotic rehabilitation
Authors: Tortorici Ornella , Abbasimoshaei Alireza , Schilling Arndt F. , Kern Thorsten A.
Published: September 2023

Home rehabilitation is a great complement to inpatient rehabilitation, because it allows for more regular training, especially in rural areas, where the access to physiotherapists is limited. However, unsupervised training carries the risk of deviation from the prescribed protocol. An obvious solution is to introduce a technical means for rehabilitation and continuous monitoring of performance. This paper presents a robotic system for rehabilitation of three degrees of freedom of the wrist independently, implemented with only one actuator, optimizing the design and size of the system. The device provides measurements of joint torque-position dependence, allowing objective assessment of mechanical impedance and range of motion to evaluate the progress of rehabilitation. Derived from research on haptic devices, a measurement method was implemented to measure hand impedance in the high frequency range between 100 and 200 Hz using a combination of exciters and accelerometers in the handles. This allows continuous monitoring of gripping forces without the need for complex contact-based force sensors. In collaboration between the authors’ technical and medical universities, a set of two-level passive and active safety measures was implemented to ensure the safety of the system’s users. Therefore, the presented system shows the potential to evaluate the mechanical impedance of the wrist at both low and high frequencies and to monitor the progress during rehabilitation. In the next step, a baseline of wrist impedance will be measured in a target group of healthy volunteers in order to prepare for studies in motion-restricted subjects.
AUC margin loss for limited, imbalanced and noisy medical image diagnosis – a case study on CheXpert5000
Authors: Ihler Sontje , Kuhnke Felix
Published: September 2023

The AUC margin loss is a valuable loss function for medical image classification as it addresses the problems of imbalanced and noisy labels. It is used by the current winner of the CheXpert competition. The CheXpert dataset is a large dataset (200k+ images), however datasets in the range of 1k-10k medical datasets are much more common. This raises the question if optimizing AUC margin loss also is effective in scenarios with limited data.We compare AUC margin loss optimization to binary cross-entropy on limited, imbalanced and noisy CheXpert5000, a subset of CheXpert dataset. We show that AUC margin loss is beneficial for limited data and considerably improves accuracy in the presence of label noise. It also improves out-of-box calibration.
3D-printed scaffolds with perfusable channels for low-cost large construct 3D cell culture
Authors: Kreuels Klaus , Schemmer Carina , Flesch Maximilian Frederick , Gillner Arnold
Published: September 2023

This study presents a novel approach for threedimensional (3D) cell culture using a two-component system consisting of a 3D-printed scaffold structure combined with a perfusion channel. A polymeric scaffold structure with an overall size of 9 mm x 9 mm x 1 mm composed of a cubic lattice with a web thickness of 200 μm and pore size of 600 μm was 3D-printed using a hot UV-stereolithography (SLA) system. The perfusion channel with an inner diameter of 800 μm and channel wall pores of 300 μm for cell culture medium supply was 3D-printed with the same system and material. Scaffolds were investigated with respect to the printing accuracy by digital microscopy. Cytotoxicity of the materials was assessed using MTT-assay and Live/Dead staining. Scaffold were subsequently seeded with 3T3- fibroblasts within a fibrin-based hydrogel and then conditioned either statically or under passive perfusion using a hydrostatic pressure driven flow system. The results show that the scaffold structure and perfusion channel can be produced with high accuracy and stability allowing a supply of nutrient and oxygen via perfusion channel to the cells within the scaffold. This approach has potential for nutrient supply within larger constructs for tissue engineering and regenerative medicine applications.
Comparing Pertubagens from Differential Gene Expression Data Analysis of ASD using Random Forest and Statistical Test
Authors: Sudharshan Ranga , Sinha Kshitij , Pragya  , Manohari Balachander Gowri , Agastinose Ronickom Jac Fredo
Published: September 2023

Differentially Expressed Genes (DEGs) are treated as candidate biomarkers, and a small set of DEGs might be identified as biomarkers using either biological knowledge or data-driven approaches like machine learning and statistical analysis. In this study, we used a combination of the machine learning algorithm and statistical tests to identify the top 300 genes that are differentially expressed in ASD compared to Typically Developed (TD). Initially, we extracted microarray gene expression data of 15 ASD and 15 TD from NCBI GEO database and used a standard pipeline to preprocess the data. Further, Random Forest (RF) was used to discriminate genes between ASD and TD. We, then analyzed the upregulated and downregulated genes using the logFC value to gain insights into their potential roles in the development of ASD. We further used drug-gene interaction analysis from ConnectivityMap to identify drugs that can inhibit the expression of these genes. Our results show that the proposed RF model yields average 5-fold cross-validation accuracy, sensitivity, and specificity of 96.67%. Further, we obtained precision and F-measure scores of 97.5% and 96.57%, respectively. Our analysis identified several novel genes that are dysregulated in ASD, including genes (such as proliferation-inducing protein 38 and germinal centre expressed transcript 32) involved in synaptic transmission, neural development, and immune function. We also identified several drugs (such as ATPase_Inhibitor, kinase inhibitors, and histone deacetylase inhibitors) that can potentially be used to treat ASD. Our findings provide new insights into the molecular mechanisms of ASD and suggest potential targets for drug development. These findings may lead to new therapeutic approaches for the treatment of ASD.
Deep Learning Based Quality Prediction of Retinal Fundus Images
Authors: Bolla Mounika , Biswas Sria , Palanisamy Rohini
Published: September 2023

The accuracy of diagnosing and monitoring eye diseases using fundus imaging is strongly dependent on the quality of the images. Poor image quality can result in delays or inaccuracies in diagnosis, thus risking patient health. Image quality assessment (IQA) is crucial for regulating retinal imaging quality and assisting in successful diagnoses. In this paper, a learning-based model for evaluating the quality of fundus images is proposed. The EyeQ dataset is selected due to its ease of availability and large collection of images. The images in the dataset are classified into three labels based on their perceptual quality. Label 0 denotes good, 1 denotes fair and 2 denotes poor quality. The ResNet50 deep learning model is fine-tuned with different hyperparameters to develop two models, which are labelled as network-1 and network-2. Network-1 uses the Stochastic Gradient Descent (SGD) optimizer and network-2 utilizes the Adam optimizer. Both networks utilise the Categorical Cross Entropy (CCE) loss function, are trained for 40 epochs and has a learning rate of 1e-3. The performance of the trained models is compared using the accuracy validation curve and the loss validation curve. Network-1 demonstrates an accuracy value of 85.8% with a loss of 0.37 and network-2 achieves an accuracy of 86.2% with a loss of 0.34. The results indicate that network-2 slightly outperforms the performance of the other in terms of accuracy, and should be preferred. Moreover, it is evident that the ResNet50 model is highly adept in appropriate feature extraction and quality evaluation of retinal fundus imaging.
Diagnostics of Multi Drug Resistant Tuberculosis in Chest Radiographs using Local Textures & Extreme Gradient Boosting
Authors: Govindarajan Satyavratan , Manuskandan S. R. , Swaminathan Ramakrishnan
Published: September 2023

This study attempts to detect and differentiate Multi Drug Resistant (MDR) - Tuberculosis (TB) and Drug Sensitive (DS)-TB Chest Radiographs (CXR) using local texture descriptors and Ensemble Learning method. Studies report that CXR images contain likelihood information of the drug resistance which can be utilized computationally. Initially, CXR images are subjected to lung fields segmentation using Reaction Diffusion Level Set method. Further, Local Directional Texture Pattern (LDTP) features are extracted from the segmented lungs to characterize the localized textural variations. Extreme Gradient Boosting (XGBoost) classifier is employed to differentiate DS-TB and MDR-TB images. The obtained results demonstrate the ability of extracted LDTP features to characterize nonspecific textural inhomogeneities in images by operating on its principal directions. XGBoost algorithm provides maximum accuracy of 93% and true positive rate of 94.6% in detecting MDR-TB. As the proposed study differentiates the MDR-TB condition using CXR images, its computerized diagnostics could be used in the early screening and followup of TB ridden patients for public health infection control in any setting.
Double-layer optimal energy management of smart grid incorporating P2P energy trading with smart traction system
Authors: El-Zonkoly Amany
Published: September 2023

Moving towards clean and sustainable transportation system, electrification of railway systems along with the use of electric vehicles (EVs) are of great interest. For economic operation of such systems, the peer-to-peer (P2P) energy trading policy became more applicable. Therefore, this paper proposes a double-layer individual-based optimization algorithm for P2P based optimal energy management (EM) of a smart distribution network incorporating flexible smart railway substation taking into consideration multiple energy sources of the traction system and the wayside distribution network with the integration of both EVs and energy hubs (EHs). For efficient operation of the railway substation, captured regenerative braking energy (RBE) are considered. In addition, photovoltaic (PV) units are used on station’s and platforms’ roof-tops for clean energy generation along with a multistory parking garage of electric vehicles (EVs). The wayside distribution network of district distribution area with flexible loads also contains several energy sources including distributed generation and multiple EHs. Optimal energy management is carried out including optimal demand side management of flexible loads and EHs. The optimization study takes into consideration several operational uncertainties arising from several components of the system. The simulation results show the feasibility of applying the proposed EM algorithm with an improvement of the energy economics of the system. The results show a reduction of 6.5 % in energy cost of the distribution network loads, 48 % in net energy cost of EHs, 70.8 % in net energy cost of EVs, and 59.1 % in the cost of energy bought by the traction substation.