Lebenswissenschaften und Ethik
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Real-time computational speed and a high degree of precision are requirements for computer-assisted interventions. Applying a segmentation network to a medical video processing task can introduce significant inter-frame prediction noise. Existing approaches can reduce inconsistencies by including temporal information but often impose requirements on the architecture or dataset. This paper proposes a method to include temporal information in any segmentation model and, thus, a technique to improve video segmentation performance without alterations during training or additional labeling. With Motion-Corrected Moving Average, we refine the exponential moving average between the current and previous predictions. Using optical flow to estimate the movement between consecutive frames, we can shift the prior term in the moving-average calculation to align with the geometry of the current frame. The optical flow calculation does not require the output of the model and can therefore be performed in parallel, leading to no significant runtime penalty for our approach. We evaluate our approach on two publicly available segmentation datasets and two proprietary endoscopic datasets and show improvements over a baseline approach.
Heavy smoke development represents an important challenge for operating physicians during laparoscopic procedures and can potentially affect the success of an intervention due to reduced visibility and orientation. Reliable and accurate recognition of smoke is therefore a prerequisite for the use of downstream systems such as automated smoke evacuation systems. Current approaches distinguish between non-smoked and smoked frames but often ignore the temporal context inherent in endoscopic video data. In this work, we therefore present a method that utilizes the pixel-wise displacement from randomly sampled images to the preceding frames determined using the optical flow algorithm by providing the transformed magnitude of the displacement as an additional input to the network. Further, we incorporate the temporal context at evaluation time by applying an exponential moving average on the estimated class probabilities of the model output to obtain more stable and robust results over time. We evaluate our method on two convolutional-based and one state-of-the-art transformer architecture and show improvements in the classification results over a baseline approach, regardless of the network used.
Seit mehr als 25 Jahren ist der Workshop "Bildverarbeitung für die Medizin" als erfolgreiche Veranstaltung etabliert. Ziel ist auch 2024 wieder die Darstellung aktueller Forschungsergebnisse und die Vertiefung der Gespräche zwischen Wissenschaftlern, Industrie und Anwendern. Die Beiträge dieses Bandes - viele davon in englischer Sprache - umfassen alle Bereiche der medizinischen Bildverarbeitung, insbesondere die Bildgebung und -akquisition, Segmentierung und Analyse, Visualisierung und Animation, computerunterstützte Diagnose sowie bildgestützte Therapieplanung und Therapie. Hierbei kommen Methoden des maschinelles Lernens, der biomechanischen Modellierung sowie der Validierung und Qualitätssicherung zum Einsatz.
In the field of computer- and robot-assisted minimally invasive surgery, enormous progress has been made in recent years based on the recognition of surgical instruments in endoscopic images and videos. In particular, the determination of the position and type of instruments is of great interest. Current work involves both spatial and temporal information, with the idea that predicting the movement of surgical tools over time may improve the quality of the final segmentations. The provision of publicly available datasets has recently encouraged the development of new methods, mainly based on deep learning. In this review, we identify and characterize datasets used for method development and evaluation and quantify their frequency of use in the literature. We further present an overview of the current state of research regarding the segmentation and tracking of minimally invasive surgical instruments in endoscopic images and videos. The paper focuses on methods that work purely visually, without markers of any kind attached to the instruments, considering both single-frame semantic and instance segmentation approaches, as well as those that incorporate temporal information. The publications analyzed were identified through the platforms Google Scholar, Web of Science, and PubMed. The search terms used were “instrument segmentation”, “instrument tracking”, “surgical tool segmentation”, and “surgical tool tracking”, resulting in a total of 741 articles published between 01/2015 and 07/2023, of which 123 were included using systematic selection criteria. A discussion of the reviewed literature is provided, highlighting existing shortcomings and emphasizing the available potential for future developments.
The utilization of virtual reality (VR) technology has shown promise in various therapeutic applications, particularly in exposure therapy
for reducing fear of certain situations objects or activities, e.g. fear of height, or negative evaluation of others in social situations. VR has been shown to yield positive outcomes in follow-up studies, and provides a safe and ecological therapeutic environment for therapists and their patients. This paper presents a collaborative
effort to develop a VR speech therapy system which simulates a virtual audience for users to practice their public speaking skills. We describe a novel web-based graphica user interface that enables
therapists to manage the therapy session using a simple timeline. Lastly, we present the results from a qualitative study with therapists and teachers with functional dysphonia, which highlight the potential of such an application to support and augment the therapists’ work and the remaining challenges regarding the design of natural interactions, agent behaviours and scenario customisation for patients.
Wie können Pflege und Gesundheitswesen so digitalisiert werden, dass alle davon profitieren? Dieser Frage widmen sich Expert*innen aus den Bereichen Medizin, Pflege und Therapie. Sie präsentieren Ergebnisse, die im Kontext der Veranstaltung »Digitalisierung im Gesundheitswesen – Gehen Sie mit uns in eine neue Zukunft« an der Ostbayerischen Technischen Hochschule Regensburg im Juli 2022 entstanden. Im Mittelpunkt steht dabei die Projektreihe »DeinHaus 4.0«. Daneben bespielen die Beiträge aber auch die Themen digitale Versorgungsanwendungen in Pflege und Gesundheit, Akzeptanz und Nutzung von sozio-assistiven Technologien in der Pflege sowie digitalisierte Logopädie bzw. Physiotherapie.
BACKGROUND: Thrombosis remains a critical complication during venovenous extracorporeal membrane oxygenation (VV ECMO). The involvement of neutrophil extracellular traps (NETs) in thrombogenesis has to be discussed. The aim was to verify NETs in the form of cell-free DNA (cfDNA) in the plasma of patients during ECMO.
METHODS: A fluorescent DNA-binding dye (QuantifFluor®, Promega) was used to detect cell-free DNA in plasma samples. cfDNA concentrations from volunteers (n = 21) and patients (n = 9) were compared and correlated with clinical/technical data before/during support, ECMO end and time of a system exchange.
RESULTS: Before ECMO, patients with a median (IQR) age of 59 (51/63) years, SOFA score of 11 (10/15), and ECMO run time of 9.0 (7.0/19.5) days presented significantly higher levels of cfDNA compared to volunteers (6.4 (5.8/7.9) ng/μL vs. 5.9 (5.4/6.3) ng/μL; p = 0.044). Within 2 days after ECMO start, cfDNA, inflammatory, and hemolysis parameters remained unchanged, while platelets decreased (p = 0.005). After ECMO removal at the end of therapy, cfDNA, inflammation, and coagulation data (except antithrombin III) remained unchanged. The renewal of a system resulted in known alterations in fibrinogen, d-dimers, and platelets, while cfDNA remained unchanged.
CONCLUSION: Detection of cfDNA in plasma of ECMO patients was not an indicator of acute and circuit-induced thrombogenesis.
Menschen mit Aphasie erleben erhebliche Einbußen in sozialer Teilhabe und Lebensqualität. Peer-to-Peer-Unterstützung durch Selbsthilfeangebote oder Peer-Befriending-Maßnahmen kann sich positiv auf Partizipation und psychisches Wohlbefinden auswirken. Mit dem Projekt shalk konnte gezeigt werden, dass von Betroffenen geleitete Selbsthilfegruppen maßgeblich zu Selbstwerterleben und verbesserter Lebensqualität der Leitungspersonen und der Gruppenteilnehmenden beitragen können. Um einen Austausch zwischen Betroffenen auch jenseits des Gruppensettings zu ermöglichen, können digitale Medien genutzt werden. Im Projekt PeerPAL wird ein für Menschen mit Aphasie angepasstes digitales soziales Netzwerk (Smartphone-App) zur virtuellen Vernetzung und persönlichen Begegnung entwickelt und evaluiert. Erste Daten weisen darauf hin, dass die Betroffenen die App nutzen können und mit Design und Funktionen zufrieden sind. Auswirkungen auf die Lebensqualität werden aktuell untersucht. Sprachtherapeut:innen nehmen in der Peer-to-Peer-Unterstützung insofern eine zentrale Rolle ein, als dass sie Betroffene in entsprechende Angebote einführen und sie mittels abgestufter Begleitung an die eigenständige Nutzung heranführen.
Aufgrund der steigenden Lebenserwartung und dem damit einhergehenden demographischen Wandel wird der Bedarf an Rehabilitations-Behandlungen in absehbarer Zukunft stark ansteigen. Ein Beispiel für diesen Trend ist die physiotherapeutische Behandlung nach Erhalt einer Knie-Totalendoprothese (Knie-TEP). So gehen Modellrechnungen basierend auf dem Bevölkerungswachstum und der bisherigen Prävalenz von Knie-TEPs davon aus, dass die Anzahl an durchgeführten Eingriffen in einkommensstarken Ländern wie Deutschland weiter zunehmen wird. Weiterhin stoßen traditionelle Rehabilitationsverfahren, gerade in strukturschwachen Regionen, schon heute an ihre Grenzen. Deutlich zu sehen war das während den Hochphasen der aktuellen Covid-19-Pandemie, als der Kontakt zwischen Therapeut*in und Patient*in flächendeckend eingeschränkt war. Eine erhöhte Nachfrage nach neuartigen Reha-Angeboten ist die logische Konsequenz. Innovative Konzepte sind daher dringend notwendig, um die daraus resultierenden technischen, sozialen und ökonomischen Herausforderungen zu bewältigen.
Der Beitrag untersucht den Diskurs um Freiheit während der Coronakrise, indem er mediale Aushandlungen von Freiheit, Selbstbestimmung und Corona in der deutschen Tageszeitungsberichterstattung im Frühling des Jahres 2020 beleuchtet. Methodisch orientiert er sich dabei an der Deutungsmusteranalyse und nutzt inhaltsanalytische Codierungsverfahren. Freiheit erweist sich bereits in einer frühen Phase der Pandemie als stark thematisiert und diskursiv umkämpft. Grundrechte und mehrere Bereiche des persönlichen Lebens werden referenziert und Auswirkungen der Coronakrise auf jene unterschiedlich beschrieben. Die analysierten Deutungsmuster beziehen sich u. a. auf die allgemeine Handlungsfreiheit im Zuge einer pandemiebedingten Zäsur, auf Beschränkungen des Reisens, auf die Möglichkeit körperlicher Unversehrtheit, auf die Bedingungen von Versammlungs- sowie Meinungs- und Pressefreiheit. Der Beitrag rekonstruiert über die mediale Auseinandersetzung Folgen und Nebenfolgen, die mit der Krise und den Strategien der Pandemiebekämpfung einhergingen – sie tragen angesichts eines neuen Potenzials von Überwachung und staatlicher Kontrolle auch das Risiko in sich, Freiheitsrechte in (digitalen) Gesellschaften prekär werden zu lassen. Insgesamt zeigt sich der Diskurs als differenzierte sowie detaillierte Auseinandersetzung mit Freiheit in einer außergewöhnlichen Situation.
Die Behandlung von Menschen mit demenziellen Erkrankungen erfordert wirksame Maßnahmen zur nachhaltigen Unterstützung der Betroffenen und ihrer Angehörigen. In Pflege und Therapie gewinnt die biografieorientierte Arbeit an Bedeutung, um die Lebensqualität und die durch dieErkrankung beeinträchtigten kognitiven Fähigkeiten so lange wie möglich zuerhalten und möglichst zu verbessern. In dem systematischen Review wurdenaktuelle Evidenzen aus zehn randomisiert-kontrollierten Studien untersucht, die sich mit der Frage nach der Effektivität biografieorientierter Maßnahmen beschäftigen, und praxisorientierte Schlussfolgerungen für logopädische Therapien
abgeleitet.
BACKGROUND:
Tracheobronchial mucus plays a crucial role in pulmonary function by providing protection against inhaled pathogens. Due to its composition of water, mucins, and other biomolecules, it has a complex viscoelastic rheological behavior. This interplay of both viscous and elastic properties has not been fully described yet. In this study, we characterize the rheology of human mucus using oscillatory and transient tests. Based on the transient tests, we describe the material behavior of mucus under stress and strain loading by mathematical models.
METHODS:
Mucus samples were collected from clinically used endotracheal tubes. For rheological characterization, oscillatory amplitude-sweep and frequency-sweep tests, and transient creep-recovery and stress-relaxation tests were performed. The results of the transient test were approximated using the Burgers model, the Weibull distribution, and the six-element Maxwell model. The three-dimensional microstructure of the tracheobronchial mucus was visualized using scanning electron microscope imaging.
RESULTS:
Amplitude-sweep tests showed storage moduli ranging from 0.1 Pa to 10000 Pa and a median critical strain of 4 %. In frequency-sweep tests, storage and loss moduli increased with frequency, with the median of the storage modulus ranging from 10 Pa to 30 Pa, and the median of the loss modulus from 5 Pa to 14 Pa. The Burgers model approximates the viscoelastic behavior of tracheobronchial mucus during a constant load of stress appropriately (R2 of 0.99), and the Weibull distribution is suitable to predict the recovery of the sample after the removal of this stress (R2 of 0.99). The approximation of the stress-relaxation test data by a six-element Maxwell model shows a larger fit error (R2 of 0.91).
CONCLUSIONS:
This study provides a detailed description of all process steps of characterizing the rheology of tracheobronchial mucus, including sample collection, microstructure visualization, and rheological investigation. Based on this characterization, we provide mathematical models of the rheological behavior of tracheobronchial mucus. These can now be used to simulate mucus flow in the respiratory system through numerical approaches.
DeinHaus 4.0 Oberbayern
(2023)
Onlinetherapie für Menschen mit Aphasie - Tipps und Hinweise zur Anwendung im therapeutischen Alltag
(2023)
9x Ofra: Technikunterstütztes Wohnen als Beitrag zur Verbesserung der kommunalen Daseinsvorsorge
(2023)
Für Fußballer:innen stellen muskuläre Verletzungen der unteren Extremitäten ein großes Problem dar. Ein Beispiel hierfür liefert die Nationalmannschaftsstürmerin Alexandra Popp, die aufgrund muskulärer Probleme das EM-Finale 2022 in Wembley kurzfristig verpasste. Oftmals stehen gerade hohe Anspannungssituationen in zeitlichem Zusammenhang mit Verletzungen, der Einfluss der psychischen Beanspruchung auf die biomechanischen Belastungen wird jedoch meist nur wenig beachtet.
To avoid dislocation of the shoulder joint after reverse total shoulder arthroplasty, it is important to achieve sufficient shoulder stability when placing the implant components during surgery. One parameter for assessing shoulder stability can be shoulder stiffness. The aim of this research was to develop a temporary reverse shoulder implant prototype that would allow intraoperative measurement of shoulder stiffness while varying the position of the implant components. Joint angle and torque measurement techniques were developed to determine shoulder stiffness. Hall sensors were used to measure the joint angles by converting the magnetic flux densities into angles. The accuracy of the joint angle measurements was tested using a test bench. Torques were determined by using thin-film pressure sensors. Various mechanical mechanisms for variable positioning of the implant components were integrated into the prototype. The results of the joint angle measurements showed measurement errors of less than 5° in a deflection range of ±15° adduction/abduction combined with ±45° flexion/extension. The proposed design provides a first approach for intra-operative assessment of shoulder stiffness. The findings can be used as a technological basis for further developments.
Background:
People with aphasia (PWA) often suffer from reduced participation and quality of life. Nevertheless, there are currently only a few specific interventions that respond to this problem. Participation and quality of life could be increased by interacting with peers who have similar experiences. Digital social networks could stimulate an autonomous interaction. However, digital social networks need to be adapted to the specific needs of PWA. Therefore, a participatory, agile process involving the target group should be chosen to develop such a olution, i.e., an app. The research project consists of a total of three phases. In the first phase—app development—the app was developed and programmed including the target group. In the second phase—app testing—the usability and user-friendliness of the app were evaluated with four PWA. In the third phase—feasibility and preliminary effcacy—that will be described in the article, the impact of the app on PWA will be evaluated.
Aims:
The overarching aim of our study is to provide preliminary effcacy of the intervention. Digital social interaction with other PWA can lead to increased social integration. In addition to digital interaction, personal encounters between PWA should be encouraged. As a result, we expect an improvement in quality of life of PWA. Additionally, we focus on identification of the most appropriate measurements to discover changes associated with the intervention.
Methods:
The evaluation, which is described in this paper, takes place in a pre-test - post-test design with a total of n = 48 PWA. Participants will be recruited in regional clusters to facilitate face-to-face meetings. Half of the participants will be assigned to the delayed intervention group and the other half to the immediate intervention group. Participants in the delayed intervention group will go through a 3-month waiting period before using the app, while the participants of the immediate intervention group will start using the app for 3 months right away. Inclusion criteria are the presence of chronic aphasia (at least 6 months) and possession of a smartphone with internet access. Questionnaires on quality of life (SAQOL-39, GHQ-12), depression (GDS, DISCs), communicative participation (CPIB), and social support (F-SozU) will be conducted at inclusion (t0), after 3 months of app use (t1), and after another 3 months for follow-up (t2). Participants in the delayed intervention group will be assessed twice before the intervention, before the 3-month waiting period (t0a) and after the waiting period (t0b). In addition to the quantitative measures, interviews will take place with 6 to 8 selected participants after 3 months of app use. Responses will be analysed using Thematic Analysis.
Discussion:
The app will be the first social network tool that is systematically developed with PWA. Initial indications from the first phases are that the app can be used by PWA, so that the evaluation of this app version can take place in the third phase. Results of this study can provide an initial indication of whether social network support is a suitable intervention. Findings will help provide information on the feasibility of digital connectivity for PWA. Preliminary findings on its impact on the participation and quality of life of PWA could be made available.
Seit mehr als 25 Jahren ist der Workshop "Bildverarbeitung für die Medizin" als erfolgreiche Veranstaltung etabliert. Ziel ist auch 2023 wieder die Darstellung aktueller Forschungsergebnisse und die Vertiefung der Gespräche zwischen Wissenschaftlern, Industrie und Anwendern. Die Beiträge dieses Bandes - viele davon in englischer Sprache - umfassen alle Bereiche der medizinischen Bildverarbeitung, insbesondere die Bildgebung und -akquisition, Segmentierung und Analyse, Visualisierung und Animation, computerunterstützte Diagnose sowie bildgestützte Therapieplanung und Therapie. Hierbei kommen Methoden des maschinelles Lernens, der biomechanischen Modellierung sowie der Validierung und Qualitätssicherung zum Einsatz.
Older adults in long-term care homes are at high risk of experiencing
reduced quality of life (QoL) and depression. Technology-assisted biography work can have a positive impact on QoL and mood, but there is little research on its use with this target group. The purpose of this paper is to examine the effect of tablet-based biography work conducted by volunteers on the QoL of residents and volunteers. A pretest-posttest control group design with an intervention period of 3 months and a 3-month follow-up was used. Results show a significant increase in participation for volunteers and residents after
the intervention, which is stable for residents until follow-up. Volunteers also show significant improvement in mental QoL immediately after the intervention. There were no significant effects for life satisfaction, self-esteem, or depression. No significant changes were found for the control group. Digitally conducted tablet-based biography work appears to have effects on QoL-associated outcomes.
Menschen mit einer erworbenen Sprachstörung, Aphasie, erleben aufgrund ihrer Kommunikationseinschränkungen und der damit einhergehenden reduzierten Teilhabe eine verminderte Lebensqualität. Aphasie-Selbsthilfegruppen können Inklusion und Selbstwerterleben der Betroffenen besonders dann fördern, wenn sie von betroffenen Personen selbst geleitet werden. In dem hier vorgestellten Forschungsprojekt Selbsthilfegruppenarbeit bei Aphasie zur Steigerung der Lebensqualität und Kompetenz (shalk) wurde untersucht, wie von Aphasie betroffene Menschen auf die Übernahme einer Gruppenleitung vorbereitet und bei der Umsetzung begleitet werden können, um die Lebensqualität der betroffenen Leitungspersonen und auch der Gruppenteilnehmenden zu steigern.
Aims
VA is an endoscopic finding of celiac disease (CD), which can easily be missed if pretest probability is low. In this study, we aimed to develop an artificial intelligence (AI) algorithm for the detection of villous atrophy on endoscopic images.
Methods
858 images from 182 patients with VA and 846 images from 323 patients with normal duodenal mucosa were used for training and internal validation of an AI algorithm (ResNet18). A separate dataset was used for external validation, as well as determination of detection performance of experts, trainees and trainees with AI support. According to the AI consultation distribution, images were stratified into “easy” and “difficult”.
Results
Internal validation showed 82%, 85% and 84% for sensitivity, specificity and accuracy. External validation showed 90%, 76% and 84%. The algorithm was significantly more sensitive and accurate than trainees, trainees with AI support and experts in endoscopy. AI support in trainees was associated with significantly improved performance. While all endoscopists showed significantly lower detection for “difficult” images, AI performance remained stable.
Conclusions
The algorithm outperformed trainees and experts in sensitivity and accuracy for VA detection. The significant improvement with AI support suggests a potential clinical benefit. Stable performance of the algorithm in “easy” and “difficult” test images may indicate an advantage in macroscopically challenging cases.
Aims
Evaluation of the add-on effect an artificial intelligence (AI) based clinical decision support system has on the performance of endoscopists with different degrees of expertise in the field of Barrett's esophagus (BE) and Barrett's esophagus-related neoplasia (BERN).
Methods
The support system is based on a multi-task deep learning model trained to solve a segmentation and several classification tasks. The training approach represents an extension of the ECMT semi-supervised learning algorithm. The complete system evaluates a decision tree between estimated motion, classification, segmentation, and temporal constraints, to decide when and how the prediction is highlighted to the observer. In our current study, ninety-six video cases of patients with BE and BERN were prospectively collected and assessed by Barrett's specialists and non-specialists. All video cases were evaluated twice – with and without AI assistance. The order of appearance, either with or without AI support, was assigned randomly. Participants were asked to detect and characterize regions of dysplasia or early neoplasia within the video sequences.
Results
Standalone sensitivity, specificity, and accuracy of the AI system were 92.16%, 68.89%, and 81.25%, respectively. Mean sensitivity, specificity, and accuracy of expert endoscopists without AI support were 83,33%, 58,20%, and 71,48 %, respectively. Gastroenterologists without Barrett's expertise but with AI support had a comparable performance with a mean sensitivity, specificity, and accuracy of 76,63%, 65,35%, and 71,36%, respectively.
Conclusions
Non-Barrett's experts with AI support had a similar performance as experts in a video-based study.
Aims
AI has proven great potential in assisting endoscopists in diagnostics, however its role in therapeutic endoscopy remains unclear. Endoscopic submucosal dissection (ESD) is a technically demanding intervention with a slow learning curve and relevant risks like bleeding and perforation. Therefore, we aimed to develop an algorithm for the real-time detection and delineation of relevant structures during third-space endoscopy.
Methods
5470 still images from 59 full length videos (47 ESD, 12 POEM) were annotated. 179681 additional unlabeled images were added to the training dataset. Consequently, a DeepLabv3+ neural network architecture was trained with the ECMT semi-supervised algorithm (under review elsewhere). Evaluation of vessel detection was performed on a dataset of 101 standardized video clips from 15 separate third-space endoscopy videos with 200 predefined blood vessels.
Results
Internal validation yielded an overall mean Dice score of 85% (68% for blood vessels, 86% for submucosal layer, 88% for muscle layer). On the video test data, the overall vessel detection rate (VDR) was 94% (96% for ESD, 74% for POEM). The median overall vessel detection time (VDT) was 0.32 sec (0.3 sec for ESD, 0.62 sec for POEM).
Conclusions
Evaluation of the developed algorithm on a video test dataset showed high VDR and quick VDT, especially for ESD. Further research will focus on a possible clinical benefit of the AI application for VDR and VDT during third-space endoscopy.
In the field of computer- and robot-assisted minimally invasive surgery, enormous progress has been made in recent years based on the recognition of surgical instruments in endoscopic images. Especially the determination of the position and type of the instruments is of great interest here. Current work involves both spatial and temporal information with the idea, that the prediction of movement of surgical tools over time may improve the quality of final segmentations. The provision of publicly available datasets has recently encouraged the development of new methods, mainly based on deep learning. In this review, we identify datasets used for method development and evaluation, as well as quantify their frequency of use in the literature. We further present an overview of the current state of research regarding the segmentation and tracking of minimally invasive surgical instruments in endoscopic images. The paper focuses on methods that work purely visually without attached markers of any kind on the instruments, taking into account both single-frame segmentation approaches as well as those involving temporal information. A discussion of the reviewed literature is provided, highlighting existing shortcomings and emphasizing available potential for future developments. The publications considered were identified through the platforms Google Scholar, Web of Science, and PubMed. The search terms used were "instrument segmentation", "instrument tracking", "surgical tool segmentation", and "surgical tool tracking" and result in 408 articles published between 2015 and 2022 from which 109 were included using systematic selection criteria.
Surgical Smoke is generated during the cauterization of tissue with high-frequency (HF) devices and consists of 95% water vapor and 5% cellular debris. When the coagulation tweezers, which are supplied with HF voltage by the HF device, touch tissue, the electric circuit is closed, and smoke is generated by the heat. In-vivo investigations are performed during tracheotomies where surgical smoke is produced during coagulation of tissue. Furthermore, in-vitro parametric studies to investigate the particle number and size distribution and the spatial distribution of surgical smoke with laser light sheet technique are conducted. With higher power of the HF device, the particles generated are larger in size and the total number of particles generated is also higher. Adding artificial saliva to the tissue shows even higher particle counts. The study by laser light sheet also confirms this. The resulting characteristic size distribution, which may include viruses and bacterial components, confirms considering the risk arising from surgical smoke. Furthermore, the experiments will provide the database for further numerical investigations.
High Spatial Resolution Tomo-PIV of the Trachea Focussing on the Physiological Breathing Cycle
(2023)
Investigations of complex patient-specific flow in the nasopharynx requires high resolution numerical calculations validated by reliable experiments. When building the validation base and the benchmark of computational fluid dynamics, an experimental setup of the nasal airways was developed. The applied optical measurement technique of tomo-PIV supplies information on the governing flow field in three dimensions.
This paper presents tomo-PIV measurements of the highly complex patient-specific geometry of the human trachea. A computertomographic scan of a person’s head builds the basis of the experimental silicone model of the nasal airways. An optimised approach for precise refractive index matching avoids optical distortions even in highly complex non-free-of-sight 3D geometries. A linear-motor-driven pump generates breathing scenarios, based on measured breathing cycles. Adjusting of the CCD cameras‘ double-frame-rate PIV-Δt enables the detailed analysis of flow structures during different cycle phases. Merging regions of interest enables high spatial resolution acquisition of the flow field.
Für Mutter und Kind konnte das Risiko der Geburt durch die Weiterentwicklung der Medizin drastisch reduziert werden. Doch wie ist es um das Wohl derer bestellt, die die Gebärende unterstützen? Eine Studie der Ostbayerischen Technischen Hochschule Regensburg hat sich mit den muskuloskelettalen Beschwerden von Geburtshelfer:innen auseinandergesetzt
Gesundheit und Soziales
(2023)
Osteoporosis is a common disease of old age. However, in many cases, it can be very well prevented and counteracted with physical activity, especially high-impact exercises. Wearables have the potential to provide data that can help with continuous monitoring of patients during therapy phases or preventive exercise programs in everyday life. This study aimed to determine the accuracy and reliability of measured acceleration data at different body positions compared to accelerations at the pelvis during different jumping exercises. Accelerations at the hips have been investigated in previous studies with regard to osteoporosis prevention. Data were collected using an IMU-based motion capture system (Xsens) consisting of 17 sensors. Forty-nine subjects were included in this study. The analysis shows the correlation between impacts and the corresponding drop height, which are dependent on the respective exercise. Very high correlations (0.83–0.94) were found between accelerations at the pelvis and the other measured segments at the upper body. The foot sensors provided very weak correlations (0.20–0.27). Accelerations measured at the pelvis during jumping exercises can be tracked very well on the upper body and upper extremities, including locations where smart devices are typically worn, which gives possibilities for remote and continuous monitoring of programs.
Das Virus SARS-CoV-2 und die dadurch ausgelöste Coronapandemie haben die Gesellschaft in einen Krisenmodus versetzt: Die Coronapandemie hat tiefgreifenden Einfluss auf den Alltag von Subjekten in allen Lebenslagen genommen, gesellschaftliche Bedingungen verändert und institutionelle Veränderungen angestoßen. Ob das Tragen eines Mund-Nasen-Schutzes, die virtuelle Kommunikation als neuer Standard in Arbeitsumgebungen oder Regelungen zu Impfungen und öffentlichem Gesundheitsschutz – gesellschaftliche Praktiken und Diskurse haben sich verändert sowie Wissensregime etabliert, die einer genaueren multidisziplinären Analyse würdig sind. Der Band versammelt Beiträge zu Bereichen, die von einem krisenbedingten Wandel betroffen sind: Alter, Bildung, Emotion, Freiheit, Geschlecht, Gesundheit, Digitalisierung, Körper, Medizin und Versorgung sowie Sorgebeziehungen.
The special wing geometry of dragonflies consisting of veins and a membrane forming a corrugated profile leads to special aerodynamic characteristics. To capture the governing flow regimes of a dragonfly wing in detail, a realistic wing model has to be investigated. Therefore, this study aimed to analyze the aerodynamic characteristics of a 3D dragonfly wing reconstructed from a high-resolution micro-CT scan. Afterwards, a spatially high discretized mesh was generated using the mesh generator CENTAUR™ 14.5.0.2 (CentaurSoft, Austin, TX, US) to finally conduct Computational Fluid Dynamics (CFD) investigations in Fluent® 2020 R2 (ANSYS, Inc., Canonsburg, PA, US). Due to the small dimensions of the wing membrane, only the vein structure of a Camacinia Gigantea was captured at a micro-CT voxel size of 7 microns. The membrane was adapted and connected to the vein structure using a Boolean union operation. Occurring nconsistencies after combining the veins and the membrane were corrected using an adapted pymesh script [1]. As an initial study, only one quarter of the wing (outer wing section) was investigated to reduce the required computational effort. The resulting hybrid mesh consisting of 10 pseudo-structured prism layers along the wing surface and tetrahedra in the farfield area has 43 mio. nodes. The flow around the wing was considered to be incompressible and laminar using transient calculations. When the flow passes the vein structures, steady vortices occur in the corrugation valleys leading to recirculation zones. Therefore, the dragonfly wing resembles the profile of an airfoil. This leads to comparable lift coefficients of dragonfly wings and airfoil profiles at significantly reduced structural weight. The reconstructed geometry also included naturally occurring triangular prismlike serrated structures at the leading edge of the wing, which have comparable effects to micro vortex generators and might stabilize the recirculation zones. Further work aims to investigate the aerodynamic properties of a complete dragonfly wing during wing flapping.
High Spatial Resolution Tomo-PIV of the Nasopharynx Focussing on the Physiological Breathing Cycle
(2022)
Investigations of complex patient-specific flow in the nasopharynx requires high resolution numerical calculations validated by reliable experiments. When building the validation base and the benchmark
of computational fluid dynamics, an experimental setup of the nasal airways was developed. The applied optical measurement technique of tomo-PIV supplies information on the governing flow field in three dimensions. This paper presents tomo-PIV measurements of the highly complex patient-specific geometry of the human trachea. A computertomographic scan of a person’s head builds the basis of the experimental silicone model of the nasal airways. An optimised approach for precise refractive index matching avoids optical distortions even in highly complex non-free-of-sight 3D geometries. A linear-motor-driven pump generates breathing scenarios, based on measured breathing cycles. Adjusting of the CCD cameras‘ double-frame-rate PIV-Δt enables the detailed analysis of flow structures during different cycle phases. Merging regions of interest enables high spatial resolution acquisition of the flow field.
Surgical smoke has been a little discussed topic in the context of the current pandemic. Surgical smoke is generated during the cauterization of tissue with heat-generating devices and consists of 95% water
vapor and 5% cellular debris in the form of particulate matter. In-vivo investigations are performed during tracheotomies where surgical smoke is produced during tissue electrocautery. Furthermore, in-vitro parametric studies to investigate the particle number and size distribution and the spatial distribution of surgical smoke with laser light sheet technique are conducted. The higher the power of the high-frequency-device the larger the particles in size and the higher the resulting particle counts. The images taken show the densest smoke at 40W with artificial saliva. The resulting characteristic size distribution, which may include viruses and bacterial components, confirms that the risk arising from surgical smoke should be considered. Furthermore, the experiments will provide the database for further numerical investigations.
Semantic segmentation is an essential task in medical imaging research. Many powerful deep-learning-based approaches can be employed for this problem, but they are dependent on the availability of an expansive labeled dataset. In this work, we augment such supervised segmentation models to be suitable for learning from unlabeled data. Our semi-supervised approach, termed Error-Correcting Mean-Teacher, uses an exponential moving average model like the original Mean Teacher but introduces our new paradigm of error correction. The original segmentation network is augmented to handle this secondary correction task. Both tasks build upon the core feature extraction layers of the model. For the correction task, features detected in the input image are fused with features detected in the predicted segmentation and further processed with task-specific decoder layers. The combination of image and segmentation features allows the model to correct present mistakes in the given input pair. The correction task is trained jointly on the labeled data. On unlabeled data, the exponential moving average of the original network corrects the student’s prediction. The combined outputs of the students’ prediction with the teachers’ correction form the basis for the semi-supervised update. We evaluate our method with the 2017 and 2018 Robotic Scene Segmentation data, the ISIC 2017 and the BraTS 2020 Challenges, a proprietary Endoscopic Submucosal Dissection dataset, Cityscapes, and Pascal VOC 2012. Additionally, we analyze the impact of the individual components and examine the behavior when the amount of labeled data varies, with experiments performed on two distinct segmentation architectures. Our method shows improvements in terms of the mean Intersection over Union over the supervised baseline and competing methods. Code is available at https://github.com/CloneRob/ECMT.
Background and aims
Celiac disease with its endoscopic manifestation of villous atrophy is underdiagnosed worldwide. The application of artificial intelligence (AI) for the macroscopic detection of villous atrophy at routine esophagogastroduodenoscopy may improve diagnostic performance.
Methods
A dataset of 858 endoscopic images of 182 patients with villous atrophy and 846 images from 323 patients with normal duodenal mucosa was collected and used to train a ResNet 18 deep learning model to detect villous atrophy. An external data set was used to test the algorithm, in addition to six fellows and four board certified gastroenterologists. Fellows could consult the AI algorithm’s result during the test. From their consultation distribution, a stratification of test images into “easy” and “difficult” was performed and used for classified performance measurement.
Results
External validation of the AI algorithm yielded values of 90 %, 76 %, and 84 % for sensitivity, specificity, and accuracy, respectively. Fellows scored values of 63 %, 72 % and 67 %, while the corresponding values in experts were 72 %, 69 % and 71 %, respectively. AI consultation significantly improved all trainee performance statistics. While fellows and experts showed significantly lower performance for “difficult” images, the performance of the AI algorithm was stable.
Conclusion
In this study, an AI algorithm outperformed endoscopy fellows and experts in the detection of villous atrophy on endoscopic still images. AI decision support significantly improved the performance of non-expert endoscopists. The stable performance on “difficult” images suggests a further positive add-on effect in challenging cases.
Mit dem demografischen Wandel wird der Anteil der institutionalisierten Menschen im hohen Alter in den kommenden Jahrzehnten stark zunehmen. Sie sind oftmals betroffen von altersbedingten Einschränkungen in der Kommunikation, dem Erleben von Einsamkeit, Einbußen in der Lebensqualität und tragen ein erhöhtes Risiko, an einer Demenz zu erkranken. Mit Blick auf die mögliche Entwicklung logopädisch relevanter Beeinträchtigungen erscheint es sinnvoll, entsprechende Präventionsmaßnahmen zu erarbeiten. Die Rekrutierung und Einbindung der Zielgruppe gestaltet sich durch ihre institutionelle Einbettung und besonderen Bedarfe komplex. Barrieren und potenzielle Förderfaktoren für die Forschung mit Bewohner*innen von Senioreneinrichtungen werden anhand der aktuellen Literatur und der Erfahrungen aus dem Forschungsprojekt BaSeTaLK vorgestellt. Es werden erste Ideen generiert, wie sich die genannten Herausforderungen durch die Einbindung von Logopäd*innen an der Schnittstelle von Forschung und Praxis überwinden lassen und welche besondere Rolle auch Studierende der Logopädie in der geriatrischen Forschung einnehmen können. Außerdem wird der Mehrwert für die klinisch-praktische Versorgung diskutiert.
Exoskeletons were invented over 100 years ago but have only become popular in the last two decades, especially in the working industry as they can decrease work-related loads significantly. The most often used exoskeletons are for the lower back and shoulder since these are commonly affected body regions. All devices have in common that their purpose is to reduce internal loads of vulnerable body regions. Nevertheless, there is still little understanding on how biomechanical loading in the human body changes when exoskeletons are used. Therefore, further analyses are needed. A promising candidate for these are musculoskeletal models, which are based on an inverse dynamics approach and can calculate external parameters such as ground reaction forces or other interaction forces as well as internal parameters such as joint reaction forces or muscle activities. The various examples in the literature show that these models are increasingly used for assessing the biomechanical effects of exoskeletons on the human body. Furthermore, musculoskeletal models can calculate biomechanical loadings of humans with and without exoskeletons for all kinds of applications and allow an evaluation of their purpose.
Practical Relevance: This article highlights the possibilities of musculoskeletal models for assessing the design and efficiency of occupational exoskeletons. Several practical use cases are described along with distinct descriptions of common implications of musculoskeletal and exoskeleton modeling.
Wie Untersuchungen zeigen, beeinträchtigen nicht medizinisch indizierte Verlegungen die Lebens- und Versorgungsqualität Sterbender stark. Das Projekt Avenue-Pal hat das Ziel, Leitlinien zu entwickeln, die diese unnötigen Verlegungen reduzieren sollen. Die hier vorgestellte Begleitstudie untersucht aus ethischer Sicht die im Sterbe- und Verlegungsprozess stattfinden Abläufe sowie deren potenziellen Gestaltungsmöglichkeiten. Mittels einer Literaturrecherche, der Teilnahme an einem Fokusgruppengespräch und einer modifizierten MEESTAR-Befragung werden die Bedingungen für eine optimale Betreuung
Sterbender sowie für ein würdevolles Sterben identifiziert und die aktuellen Abläufe im Rahmen des Verlegungsmanagements vor und während der Implementierung der Leitlinien unter Berücksichtigung der ethischen Dimensionen Selbstverständnis, Selbstbestimmung, Fürsorge, Gerechtigkeit, Privatheit und Teilhabe analysiert. Die Ergebnisse der Untersuchung zeigen, dass Verlegungen v.a. aus Unsicherheiten, mangelndem palliativmedizinischem Wissen oder Personalmangel resultieren und dass die Etablierung der Leitlinien und eines palliativen Konsildienstes besonders die fachliche Kompetenz des Personal stärkt und dadurch unnötige Verlegungen reduziert werden konnten.
Jedes Jahr erleiden 270.000 Menschen in Deutschland einen Schlaganfall. In vielen Fällen können die betroffenen Personen wieder nach Hause zurückkehren und ihr Leben fortführen, benötigen dabei aber ambulante Pflege- und Therapiemaßnahmen. Gerade in ländlichen Regionen bringt dies erhebliche Herausforderungen mit sich, denen sich neue Technologien und die Digitalisierung entgegenstellen. Die Beiträger*innen des Bandes diskutieren erste Ergebnisse des Projekts »DeinHaus 4.0 Oberpfalz« aus interdisziplinärer Sicht, bei dem die Möglichkeit des Einsatzes von Telepräsenzrobotern zur Unterstützung ambulanter Pflege- und Therapiemaßnahmen untersucht wird.
Elbow stability is derived from a combination of muscular, ligamentous, and bony structures. After an elbow trauma the stability of the joint is an important decision criterion for the subsequent treatment. The decision regarding non-operative/operative care depends mostly on subjective assessments of medical experts. Therefore, the aim of this study is to use musculoskeletal simulations as an objective assessment tool to investigate the extent to which failure of different stabilizers affects the elbow stability and how these observations correspond to the assessment from clinical practice. A musculoskeletal elbow simulation model was developed for this aim. To investigate the stability of the elbow, varus/valgus moments were applied under 0°, 45°and 90° flexion while the respective cubital angle was analyzed. This was performed for nine different injury scenarios, which were also evaluated for stability by clinical experts. With the results, it can be determined by which injury pattern and under which flexion angle the elbow stability is impaired regarding varus/valgus moments. The scenario with a complete failure of the medial and lateral ligaments and a fracture of the radial head was identified as having the greatest instability. The study presented a numerical determination of elbow stability against varus/valgus moments regarding clinical injury patterns, as well as a comparison of the numerical outcome with experience gained in clinical practice. The numerical predictions agree well with the assessments of the clinical specialists. Thus, the results from musculoskeletal simulation can make an important contribution to a more objective assessment of the elbow stability.
Clinical setting
Third space procedures such as endoscopic submucosal dissection (ESD) and peroral endoscopic myotomy (POEM) are complex minimally invasive techniques with an elevated risk for operator-dependent adverse events such as bleeding and perforation. This risk arises from accidental dissection into the muscle layer or through submucosal blood vessels as the submucosal cutting plane within the expanding resection site is not always apparent. Deep learning algorithms have shown considerable potential for the detection and characterization of gastrointestinal lesions. So-called AI – clinical decision support solutions (AI-CDSS) are commercially available for polyp detection during colonoscopy. Until now, these computer programs have concentrated on diagnostics whereas an AI-CDSS for interventional endoscopy has not yet been introduced. We aimed to develop an AI-CDSS („Smart ESD“) for real-time intra-procedural detection and delineation of blood vessels, tissue structures and endoscopic instruments during third-space endoscopic procedures.
Characteristics of Smart ESD
An AI-CDSS was invented that delineates blood vessels, tissue structures and endoscopic instruments during third-space endoscopy in real-time. The output can be displayed by an overlay over the endoscopic image with different modes of visualization, such as a color-coded semitransparent area overlay, or border tracing (demonstration video). Hereby the optimal layer for dissection can be visualized, which is close above or directly at the muscle layer, depending on the applied technique (ESD or POEM). Furthermore, relevant blood vessels (thickness> 1mm) are delineated. Spatial proximity between the electrosurgical knife and a blood vessel triggers a warning signal. By this guidance system, inadvertent dissection through blood vessels could be averted.
Technical specifications
A DeepLabv3+ neural network architecture with KSAC and a 101-layer ResNeSt backbone was used for the development of Smart ESD. It was trained and validated with 2565 annotated still images from 27 full length third-space endoscopic videos. The annotation classes were blood vessel, submucosal layer, muscle layer, electrosurgical knife and endoscopic instrument shaft. A test on a separate data set yielded an intersection over union (IoU) of 68%, a Dice Score of 80% and a pixel accuracy of 87%, demonstrating a high overlap between expert and AI segmentation. Further experiments on standardized video clips showed a mean vessel detection rate (VDR) of 85% with values of 92%, 70% and 95% for POEM, rectal ESD and esophageal ESD respectively. False positive measurements occurred 0.75 times per minute. 7 out of 9 vessels which caused intraprocedural bleeding were caught by the algorithm, as well as both vessels which required hemostasis via hemostatic forceps.
Future perspectives
Smart ESD performed well for vessel and tissue detection and delineation on still images, as well as on video clips. During a live demonstration in the endoscopy suite, clinical applicability of the innovation was examined. The lag time for processing of the live endoscopic image was too short to be visually detectable for the interventionist. Even though the algorithm could not be applied during actual dissection by the interventionist, Smart ESD appeared readily deployable during visual assessment by ESD experts. Therefore, we plan to conduct a clinical trial in order to obtain CE-certification of the algorithm. This new technology may improve procedural safety and speed, as well as training of modern minimally invasive endoscopic resection techniques.
In den Austausch kommen
(2022)
Mit der BaSeTaLK-App wird eine Tablet-gestützte Biographiearbeit für institutionalisierte ältere Menschen zur Steigerung der Lebensqualität ermöglicht. Um Gelingensbedingungen für die Erprobung und die mögliche Implementierung der Maßnahme zu bestimmen, wurde die Perspektive von Mitarbeitenden einer Pflegeeinrichtung eingeholt. Insbesondere eine umfassende Informationsvermittlung und eine flexible, enge Zusammenarbeit mit den Forschenden wurden hervorgehoben.
Autologous lipotransfer is a promising method for tissue regeneration, because white adipose tissue contains a heterogeneous cell population, including mesenchymal stem cells, endothelial cells, immune cells, and adipocytes. In order to improve the outcome, adipose tissue can be processed before application. In this study, we investigated changes caused by mechanical processing.
Lipoaspirates were processed using sedimentation, first-time centrifugation, shear-force homogenization, and second-time centrifugation. The average adipocyte size, stromal vascular cell count, and adipocyte depot size were examined histologically at every processing step. In addition, the adipose derived stem cells (ADSCs) were isolated and differentiated osteogenically and adipogenically. While homogenization causes a disruption of adipocyte depots, the shape of the remaining adipocytes is not changed. On average, these adipocytes are smaller than the depot adipocytes, they are surrounded by the ECM, and therefore mechanically more stable. The volume loss of adipocyte depots leads to a significant enrichment of stromal vascular cells such as ADSCs. However, the mechanical processing does not change the potential of the ADSCs to differentiate adipogenically or steogenically. It thus appears that mechanically processed lipoaspirates are promising for the reparation of even mechanically stressed tissue as that found in nasolabial folds. The changes resulting from the processing correspond more to a filtration of mechanically less stable components than to a manipulation of the tissue.
Der Bericht fasst Ergebnisse der Befragung bayerischen Rehabilitationseinrichtungen im Projekt Reha/TI-Konsil zusammen.
Der Digitalisierungsgrad ist – gemessen am EMRAM-Modell - in der überwiegenden Zahl der Rehabilitationseinrichtungen in Bayern, die an der Befragung teilgenommen haben, relativ gering ausgeprägt. Mehrheitlich, aber längst nicht durchgängig, liegt eine Digitalisierungsstrategie vor. Auch wenn diese vorhanden ist, so stehen drei Viertel der Einrichtungen nach eigener Aussage noch ganz am Anfang der Umset-zung. Für Informationssicherheit und Datenschutz sind Konzepte und Beauftragte meist vorhanden; die Abteilungen für Informationstechnik sind meist relativ klein und häufig beim Träger, nicht direkt bei der Rehabilitationseinrichtung angesiedelt.
Der mit der Anbindung an die Telematikinfrastruktur (TI) erwartete Aufwand ist im Bereich der Installation, aber auch der Anpassung der Arbeitsorganisation und der Schulung des Personals sehr hoch. Dahingegen wird nicht erwartet, dass die hausin-ternen TI-Komponenten sehr wartungsintensiv sind.
Hoffnungen an die Digitalisierung allgemein betreffen Erleichterungen und Vereinfa-chungen, die zu Qualitäts- und Effizienzsteigerungen führen. Herausforderungen werden häufig in der Implementierung in der Alltagspraxis gesehen. Auch techni-sche Herausforderungen und die Finanzierung von Digitalisierung allgemein und insbesondere der Anbindung an die TI wurden häufig genannt. Festzustellen ist eine verbreitete Ausstattung mit Klinischem Arbeitsplatzsystem (KAS) und Kranken-hausinformationssystem (KIS), jedoch ist in der Hälfte der Einrichtungen der techni-sche Stand der IT nicht für Anwendungen der Telematikinfrastruktur wie Kommuni-kation im Medizinwesen (KIM)/eArztbrief geeignet. Hier ist generell und insbesonde-re im Hinblick auf die Informationssicherheit sehr hoher finanzieller Aufwand not-wendig.
Einrichtungen, die Daten bislang noch analog dokumentieren und verwalten, wer-den durch die Anbindung an die TI vor sehr große Herausforderungen gestellt. Dies betrifft jedoch nur einen kleinen Teil der Einrichtungen. Die Mehrzahl hingegen ar-beitet bereits jetzt sowohl digital als auch analog. Insbesondere die häufig analoge Übermittlung an Patient*innen sowie an Hausärzt*innen verweist darauf, dass bei der Umstellung der Prozesse in den Reha-Einrichtungen die Schnittstellen und der Digitalisierungsgrad in den Privathaushalten oder den Hausarztpraxen entscheidend sind. Die Wandlungsgeschwindigkeit wird somit durch die jeweils erfolgten Schritte auf deren Seite begrenzt.
The endoscopic features associated with eosinophilic esophagitis (EoE) may be missed during routine endoscopy. We aimed to develop and evaluate an Artificial Intelligence (AI) algorithm for detecting and quantifying the endoscopic features of EoE in white light images, supplemented by the EoE Endoscopic Reference Score (EREFS). An AI algorithm (AI-EoE) was constructed and trained to differentiate between EoE and normal esophagus using endoscopic white light images extracted from the database of the University Hospital Augsburg. In addition to binary classification, a second algorithm was trained with specific auxiliary branches for each EREFS feature (AI-EoE-EREFS). The AI algorithms were evaluated on an external data set from the University of North Carolina, Chapel Hill (UNC), and compared with the performance of human endoscopists with varying levels of experience. The overall sensitivity, specificity, and accuracy of AI-EoE were 0.93 for all measures, while the AUC was 0.986. With additional auxiliary branches for the EREFS categories, the AI algorithm (AI-EoEEREFS) performance improved to 0.96, 0.94, 0.95, and 0.992 for sensitivity, specificity, accuracy, and AUC, respectively. AI-EoE and AI-EoE-EREFS performed significantly better than endoscopy beginners and senior fellows on the same set of images. An AI algorithm can be trained to detect and quantify endoscopic features of EoE with excellent performance scores. The addition of the EREFS criteria improved the performance of the AI algorithm, which performed significantly better than endoscopists with a lower or medium experience level.
Der Einsatz von künstlicher Intelligenz im Gesundheitsbereich verspricht besonders großen Nutzen durch eine bessere Versorgung sowie effizientere Abläufe und bietet damit letztlich auch ökonomische Vorteile. Dem stehen unter anderem Befürchtungen entgegen, dass sich durch den Einsatz von künstlicher Intelligenz das Arzt-Patienten-Verhältnis verändern könnte, Arbeitsplätze gefährdet seien oder die Ökonomisierung des Gesundheitswesens einen weiteren Schub erfahren könnte. Zuweilen wird die Debatte um diese Technologie, zumal in der Öffentlichkeit, emotional und fern sachlicher Argumente geführt. Die Autorinnen und Autoren untersuchen die Geschichte des KI-Einsatzes in der Medizin, deren öffentliche Wahrnehmung, Governance der KI, die Möglichkeiten und Grenzen der Technik sowie Einsatzgebiete, die bisher noch nicht oder nur wenig im Fokus der Aufmerksamkeit waren. Dabei erweist sich die KI als leistungsfähiges Werkzeug, das zahlreiche ethische und soziale Fragen aufwirft, die bei der Einführung anderer Technologien bereits gestellt wurden; allerdings gibt es auch neue Herausforderungen, denen sich Professionen, Politik und Gesellschaft stellen müssen.
Sind Impfbereitschaft und Impfablehnung rational erklärbar?
Und wenn ja: Welche Rolle spielen dabei Verschwörungsüberzeugungen
und die Nutzung sozialer Medien?
Welchen Einfluss haben Freunde und Bekannte und das
Wohl der Gesellschaft? Und inwieweit vertraut die Bevölkerung
in Deutschland überhaupt in Impfungen und staatliche
Institutionen wie das Robert Koch-Institut?
Aufgrund fehlender Informationen zu tatsächlich eingesetzten digitalen Assistenztechniken in der ambulanten und stationären Pflege sind die Auswirkungen auf die Pflegepraxis und Pflegepersonal dieser Systeme weitestgehend unerschlossen. Das Projekt DAAS-KIN (Diffusion altersgerechter Assistenzsysteme – Kennzahlenerhebung und Identifikation von Nutzungshemmnissen) untersucht mittels eines Mixed-Methods-Ansatzes (Fragebogen, Experteninterviews, Wertbaumanalyse) Verbreitung, Reaktion und Auswirkung digitaler Assistenzsysteme sowie potentielle Nutzungs- und Diffusionshemmnisse
Der Einsatz von Robotern in der Rehabilitation könnte neue Möglichkeiten in der Behandung eröffnen. Maßnahmen im Bereich von Teletherapie und Telenursing, die nach einem Schlaganfall mit einem Telepräsenzroboter durchgeführt werden, erweitern das Spektrum der ambulanten Therapie und Pflege. Wie sehen Betroffene, Angehörige, Pflegende und Therapeut*innen die robotischen Systeme?
Die Entwicklung einer an die spezifischen Bedürfnisse von Menschen mit Aphasie angepassten Smartphone-basierten App erfordert einen umfangreichen Entwicklungsprozess. Dabei ist es wichtig, die Zielgruppe von Anfang an in den Prozess einzubeziehen, um die spezifischen Wünsche und Anforderungen an die App erfassen und in den Entwicklungsprozess integrieren zu können. In diesem Beitrag wird die nutzerzentrierte, partizipative Entwicklung der App PeerPAL vorgestellt. Mit der App sollen neben einem digitalen Austausch auch reale Face-to-Face-Treffen stimuliert werden mit dem Ziel, die autonome Vernetzung unter den Betroffenen zu fördern und dadurch die Lebensqualität von Menschen mit Aphasie zu steigern.
Convolutional Neural Networks for Approximation of Internal Non-Newtonian Multiphase Flow Fields
(2021)
Neural networks (NNs) as an alternative method for universal approximation of differential equations have proven to be computationally efficient and still sufficiently accurate compared to established methods such as the finite volume method (FVM). Additionally, analysing weights and biases can give insights into the underlying physical laws. FVM and NNs are both based upon spacial discretisation. Since a Cartesian and equidistant grid is a raster graphics, image-to-image regression techniques can be used to predict phase velocity fields as well as particle and pressure distributions from simple mass flow boundary conditions. The impact of convolution layer depth and number of channels of a ConvolutionDeconvolution Regression Network (CDRN), on prediction performance of internal non-Newtownian multiphase flows is investigated. Parametric training data with 2055 sets is computed using FVM. To capture significant non-Newtownian effects of a particle-laden fluid (e.g. blood) flowing through small and non-straight channels, an Euler-Euler multiphase approach is used. The FVM results are normalized and mapped onto an equidistant grid as supervised learning target. The investigated NNs consist of n= {3, 5, 7} corresponding encoding/decoding blocks and different skip connections. Regardless of the convolution depth (i.e. number of blocks), the deepest spacial down-sampling via strided convolution is adjusted to result in a 1 × 1 × f · 2nfeature map, with f = {8, 16, 32}. The prediction performance expressed is as channel-averaged normalized root mean squared error (NRMSE). With a NRMSE of < 2 · 10-3, the best preforming NN has f = 32 initial feature maps, a kernel size of k = 4, n = 5 blocks and dense skip connections. Average inference time from this NN takes < 7 · 10-3s. Worst accuracy at NRMSE of approx 9 · 10-3is achieved without any skips, at k = 2, f = 16 and n = 3, but deployment takes only < 2 · 10-3s Given an adequate training, the prediction accuracy improves with convolution depth, where more features have higher impact on deeper NNs. Due to skip connections and batch normalisation, training is similarly efficient, regardless of the depth. This is further improved by blocks with dense connections, but at the price of a drastically larger model. Depending on geometrical complexity, spacial resolution is critical, as it increases the number of learnables and memory requirements massively.
Das Forschungsprojekt „Dein Haus 4.0 Oberpfalz – Telepräsenzroboter für die Pflege und Unterstützung von Schlaganfallpatientinnen und -patienten (TePUS)“ untersucht den Einsatz von zwei Varianten von Telepräsenzrobotern. Das Arbeitspapier stellt die sozialwissenschaftliche Begleitforschung des Projekts vor. Diese hat zum einen zum Ziel, Akzeptanz und Potenzial der eingesetzten technischen Assistenzsysteme empirisch zu untersuchen. Daneben werden ethisch, rechtlich, gesellschaftlich und organisatorisch relevante Fragestellungen des Technikeinsatzes und der Mensch-Technik-Interaktion analysiert (ELSI-Begleitstudie).
Der Beitrag untersucht die Nutzung altersgerechter Assistenz-systeme und insbesondere Roboter. Die Studie basiert auf einem Scoping Review, das einen Überblick über aktuelle Forschungsergebnisse zum Einsatz von Robotern in der Pflege gibt, sowie auf Ergebnissen einer qualitativen und quantitativen Studie. In die-ser Untersuchung zeigen sich einige Diffusionshemmnisse, aber auch Chancen für den Einsatz, die anhand eines Fallbeispiels zum Einsatz von Telepräsenzrobotern zu Pflege und Therapie diskutiert werden.
Hintergrund
In Deutschland liegen unterschiedliche Befunde zur Akzeptanz von Schutzimpfungen gegen COVID-19 vor. Die Impfbereitschaft schwankt je nach Stichprobe, Messung und Erhebungszeitraum.
Ziel der Arbeit (Fragestellung)
Ziel der Studie ist die Untersuchung der Impfbereitschaft mit einem COVID-19-Vakzin an einer Zufallsstichprobe der Gesamtbevölkerung. Der Schwerpunkt der Studie liegt in der Beschreibung des Zusammenhangs zwischen Impfbereitschaft, wahrgenommenen Risiken einer Erkrankung, den subjektiven Überzeugungen zu Nebenwirkungen und dem Vertrauen in Institutionen sowie dem Einfluss sozialer Bezugsgruppen.
Material und Methoden
Die Studie basiert auf einer telefonischen Ein-Themen-Bevölkerungsbefragung zur Impfbereitschaft (n=2.014) vor der Zulassung eines COVID-19-Vakzins in Deutschland im November/Dezember 2020.
Ergebnisse
Die Impfbereitschaft liegt bei etwa 67 % und steigt mit dem Anteil der impfbereiten Freunde und Bekannten und dem Vertrauen gegenüber dem Robert-Koch-Institut, bei Zugehörigkeit zu einer Risikogruppe und der Erwartung gefährlicher Konsequenzen bei einer Erkrankung oder Unklarheit darüber. Erfahrungen mit einer Infektion bei den Befragten oder in ihrer sozialen Bezugsgruppe erhöhen die Impfbereitschaft. Eine Überschätzung der Wahrscheinlichkeit ernsthafter Nebenwirkungen bei Grippeimpfungen senkt die Impfbereitschaft gegen COVID-19. Auffällig ist der Befund einer erheblichen Überschätzung der Häufigkeit von ernsthaften Impfnebenwirkungen.
Diskussion
Die Überschätzung der Häufigkeit ernsthafter Nebenwirkungen bei Impfungen weist erstens auf ein Informationsdefizit, zweitens auf weit verbreitete Fehlinformationen und drittens auf kognitive Probleme bei der Interpretation relativer Häufigkeiten hin. Es werden Implikationen für eine Informationskampagne abgeleitet.
Blood flow in channels of varying diameters <500μm exhibits strong non-linear effects. Multiphase finite volume approaches are feasible, but still computationally costly. Here, the feasibility of applying convolutional neural networks for blood flow prediction in artificial lungs is investigated. Training targets are precomputed using an Eulerian two-phase approach. To match with experimental data, the interphase drag and lift, as well as intraphase shear-thinning are adapted. A recursively branching regression network and convolution/deconvolution networks with plain skip connections and densely connected skips are investigated. A priori knowledge is incorporated in the loss functional to prevent the network from learning non-physical solutions. Inference from neural networks is approximately six orders of magnitude faster than the classical finite volume approach. Even if resulting in comparably coarse flow fields, the neural network predictions can be used as close to convergence initial solutions greatly accelerating classical flow computations.
Resting motor threshold and magnetic field output of the figure-of-8 and the double-cone coil
(2020)
The use of the double-cone (DC) coil in transcranial magnetic stimulation (TMS) is promoted with the notion that the DC coil enables stimulation of deeper brain areas in contrast to conventional figure-of-8 (Fo8) coils. However, systematic comparisons of these two coil types with respect to the spatial distribution of the magnetic field output and also to the induced activity in superficial and deeper brain areas are limited. Resting motor thresholds of the left and right first dorsal interosseous (FDI) and tibialis anterior (TA) were determined with the DC and the Fo8 coil in 17 healthy subjects. Coils were orientated over the corresponding motor area in an angle of 45 degrees for the hand area with the handle pointing in posterior direction and in medio-lateral direction for the leg area. Physical measurements were done with an automatic gantry table using a Gaussmeter. Resting motor threshold was higher for the leg area in contrast to the hand area and for the Fo8 in contrast to the DC coil. Muscle by coil interaction was also significant providing higher differences between leg and hand area for the Fo8 (about 27%) in contrast to the DC coil (about 15%). Magnetic field strength was higher for the DC coil in contrast to the Fo8 coil. The DC coil produces a higher magnetic field with higher depth of penetration than the figure of eight coil.
BACKGROUND:
Due to their corrugated profile, dragonfly wings have special aerodynamic characteristics during flying and gliding. OBJECTIVE: The aim of this study was to create a realistic 3D model of a dragonfly wing captured with a high-resolution micro-CT. To represent geometry changes in span and chord length and their aerodynamic effects, numerical investigations are carried out at different wing positions. METHODS:
The forewing of a Camacinia gigantea was captured using a micro-CT. After the wing was adapted an error-free 3D model resulted. The wing was cut every 5 mm and 2D numerical analyses were conducted in Fluent® 2020 R2 (ANSYS, Inc., Canonsburg, PA, USA). RESULTS: The highest lift coefficient, as well as the highest lift-to-drag ratio, resulted at 0 mm and an angle of attack (AOA) of 5∘. At AOAs of 10∘ or 15∘, the flow around the wing stalled and a Kármán vortex street behind the wing becomes
CONCLUSIONS:
The velocity is higher on the upper side of the wing compared to the lower side. The pressure acts vice versa. Due to the recirculation zones that are formed in valleys of the corrugation pattern the wing resembles the form of an airfoil.
Ergonomic workplaces lead to fewer work-related musculoskeletal disorders and thus fewer sick days. There are various guidelines to help avoid harmful situations. However, these recommendations are often rather crude and often neglect the complex interaction of biomechanical loading and psychological stress. This study investigates whether machine learning algorithms can be used to predict mechanical and stress-related muscle activity for a standardized motion. For this purpose, experimental data were collected for trunk movement with and without additional psychological stress. Two different algorithms (XGBoost and TensorFlow) were used to model the experimental data. XGBoost in particular predicted the results very well. By combining it with musculoskeletal models, the method shown here can be used for workplace analysis but also for the development of real-time feedback systems in real workplace environments.
The German workshop on medical image computing (BVM) has been held in different locations in Germany for more than 20 years. In terms of content, BVM focused on the computer-aided analysis of medical image data with a wide range of applications, e.g. in the area of imaging, diagnostics, operation planning, computer-aided intervention and visualization.
During this time, there have been remarkable methodological developments and upheavals, on which the BVM community has worked intensively. The area of machine learning should be emphasized, which has led to significant improvements, especially for tasks of classification and segmentation, but increasingly also in image formation and registration. As a result, work in connection with deep learning now dominates the BVM. These developments have also contributed to the establishment of medical image processing at the interface between computer science and medicine as one of the key technologies for the digitization of the health system.
In addition to the presentation of current research results, a central aspect of the BVM is primarily the promotion of young scientists from the diverse BVM community, covering not only Germany but also Austria, Switzerland, The Netherland and other European neighbors. The conference serves primarily doctoral students and postdocs, but also students with excellent bachelor and master theses as a platform to present their work, to enter into professional discourse with the community, and to establish networks with specialist colleagues. Despite the many conferences and congresses that are also relevant for medical image processing, the BVM has therefore lost none of its importance and attractiveness and has retained its permanent place in the annual conference rhythm.
Building on this foundation, there are some innovations and changes this year. The BVM 2021 was organized for the first time at the Ostbayerische Technische Hochschule Regensburg (OTH Regensburg, a technical university of applied sciences). After Aachen, Berlin, Erlangen, Freiburg, Hamburg, Heidelberg, Leipzig, Lübeck, and Munich, Regensburg is not just a new venue. OTH Regensburg is the first representative of the universities of applied sciences (HAW) to organize the conference, which differs to universities, university hospitals, or research centers like Fraunhofer or Helmholtz. This also considers the further development of the research landscape in Germany, where HAWs increasingly contribute to applied research in addition to their focus on teaching. This development is also reflected in the contributions submitted to the BVM in recent years.
At BVM 2021, which was held in a virtual format for the first time due to the Corona pandemic, an attractive and high-quality program was offered. Fortunately, the number of submissions increased significantly. Out of 97 submissions, 26 presentations, 51 posters and 5 software demonstrations were accepted via an anonymized reviewing process with three reviews each. The three best works have been awarded BVM prizes, selected by a separate committee.
Based on these high-quality submissions, we are able to present another special issue in the International Journal of Computer Assisted Radiology and Surgery (IJCARS). Out of the 97 submissions, the ones with the highest scores have been invited to submit an extended version of their paper to be presented in IJCARS. As a result, we are now able to present this special issue with seven excellent articles. Many submissions focus on machine learning in a medical context.