Regensburg Center of Health Sciences and Technology - RCHST
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- Regensburg Center of Health Sciences and Technology - RCHST (16)
- Fakultät Angewandte Sozial- und Gesundheitswissenschaften (8)
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- Regensburg Center of Biomedical Engineering - RCBE (5)
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- peer-reviewed (8)
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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.
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.
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.
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.
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.
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.
Hintergrund/Fragestellung:
Eine Rheumatoide Arthritis (RA) verläuft chronisch schubweise und Betroffene leiden unter zunehmenden und schwankenden Symptomen. In der Physiotherapie (PT) werden individuell abgestimmte Interventionen in einem ambulanten (aPT) oder Gruppensetting (gPT) eingesetzt. In Leitlinien wird PT unspezifisch empfohlen. Ärzte verordnen spezifische Formen der PT. Wie ist eine evidenzbasierte und am Bedarf aus Sicht Betroffener orientierte Versorgung zu gestalten?
Methoden/Material:
Drei Methoden wurden kombiniert: 1) Evidenz zu PT wurde mittels eines Systematischen Reviews ermittelt und deren Qualität in Anlehnung an GRADE beurteilt. 2) Einflussfaktoren auf eine Versorgung wurden mittels Sekundäranalyse von Versorgungsdaten aus einer Längsschnittstudie bestimmt. 3) Einflussfaktoren auf den Bedarf an sowie die Verordnung von PT aus Sicht der Betroffenen wurden mittels Fragebogen erhoben. Die Daten wurden soweit möglich mittels Metaanalysen (1), binären (2) sowie multiplen linearen Regressionen und Gruppenvergleichen für abhängige Stichproben (3) analysiert. Es wurden Häufigkeiten (3) der Nennung spezifischer Interventionen sowie von Übereinstimmungen zwischen Bedarf und wahrgenommenem Verordnungsverhalten bestimmt.
Ergebnisse:
1) Eine gPT zeigte bei jungen Betroffenen mit guter Funktion Effekte, war aber bei Vorschäden der Gelenke weder effektiv noch sicher. Zu aPT liegt keine Evidenz vor. 2) Der einzige relevante Vorhersagefaktor für eine Versorgung mit PT ist eine bereits bestehende Versorgung (Exp(B)=10,81 - 171,53, p<0,001). 3) Den stärksten bedarfssteigernden Einfluss hat ein schlechtes körperliches Befinden (B=0,25, p<0,05/MWD 1,3 (NAS 1-10), p<0,001). Teilnehmer (n=305) gaben am häufigsten Bedarf an aPT an, bei schlechtem Befinden vor allem an passiven Maßnahmen. In besseren Phasen besteht zusätzlich Bedarf an gPT. Betroffene haben individuell unterschiedliche Bedarfe und eine Berücksichtigung bedarfsbestimmender Faktoren bei der Verordnung wird kaum wahrgenommen.
Schlussfolgerungen:
Angesichts der geringen Evidenzlage und individueller und schwankender Bedarfe wird ein stark individualisiertes Vorgehen empfohlen. Effektivität, Sicherheit und Passgenauigkeit sind regelmäßig gemeinsam mit den an der Versorgung Beteiligten sowie den Betroffenen zu überprüfen. Insbesondere bei schlechtem Befinden sollte über die Möglichkeit einer aPT und die notwendige Überprüfung aufgeklärt werden. Es besteht dringender Forschungsbedarf für aPT bei RA.
Entwicklung eines interprofessionellen Online-Kurses für Medizin- und Physiotherapiestudierende
(2019)
Das sich verändernde Gesundheitssystem macht Anpassungen in der Ausbildung der Gesundheitsberufe notwendig. Dabei wird die Stärkung der Interprofessionalität besonders betont. Die Entwicklung und Implementierung von interprofessionellen Lernszenarien birgt jedoch oftmals organisatorisch-logistische Herausforderungen. Es fehlen derzeit noch orts- und zeitunabhängige Lernszenarien, die asynchrones interprofessionelles Lernen ermöglichen.
Der neu entwickelte Online-Kurs „Medizin und Physiotherapie in der Rehabilitation“ hat zum Ziel, zur Schließung dieser Lücke beizutragen.
Die Kursentwicklung ist Teil eines Forschungsprojekts, das den Einfluss von interprofessionellem Online-Lernen auf die Kooperationskompetenz untersucht.