TY - GEN A1 - Adler, Ina A1 - Förstl, Nikolas A1 - Süß, Franz A1 - Dendorfer, Sebastian T1 - Biomechanical exposition of the factors influencing the pelvic floor muscle activity T2 - ESB 2024, 29th Congress of the European Society of Biomechanics, 30 June-3 July 2024, Edinburgh, Scotland N2 - Introduction About 40 % of the women worldwide suffer from Pelvic Floor Dysfunctions [1]. In previous studies, musculoskeletal simulations helped to understand and facilitate the complex issues of musculoskeletal disorders in several different branches [2, 3]. A fundamental prerequisite for the generation of these models is to have a basic understanding of the stresses acting on the pelvic floor. The aim of this study is to investigate the biomechanical factors influencing the activity of the pelvic floor muscles by establishing a parameterised model. Methods To identify the factors that affect the pelvic floor muscles, the process is divided into several stages. The first step is to investigate the loads acting on the pelvic floor. For this, a simplified sagittal cross-sectional model of the upper body is analysed. The discretization of the abdominal cavity is presented with resulting force vectors representing the organs in the abdominal and pelvic region, the tensile force of the structures that fixate the organs as well as the intraabdominal pressure. A second step is the examination of the basic mechanics of the pelvic floor muscles. As the functional anatomy of these muscles demonstrate differences compared to other muscle groups [4], the understanding of how they differ is crucial for the construction of musculoskeletal models. After a static examination of loads on a simplified muscle model, an extended model including the physiological properties of a muscle must be involved in the considerations. Therefore, the influence of parameters such as the ideal muscle strength and fibre length, as well as elasticity or passive stiffness of the elements must be taken into account. The influencing factors of the variables can be analysed in a simplified pelvic floor muscle model by systematically changing the parameters. Results The process of analysing the factors influencing the activity of the pelvic floor muscles follows two main strategies. The review of the effects of the whole body identifies the forces acting on the pelvic floor muscles and effect their activity. With the examination of the basic mechanics of a simplified pelvic floor muscle model a better understanding is achieved of how the pelvic floor muscles absorb the loads acting in the body. Discussion This process results in the main influence factors on the activity of the pelvic floor. The consequent parameters form the basis for a biomechanically justified construction of musculoskeletal simulation models of the pelvic floor. Nevertheless, it must be considered, that these mechanical studies represent simplifications of the reality. References 1. Wang et al, Front Public Health, 10:975829, 2022. 2. Bulat et al., Curr Sports Med Rep, 18(6):210-216, 2019 3. Melzner et al., Eur J Obstet Gynecol Reprod Biol, 264:306-313, 2021 4. Ashton-Miller et al., Ann N Y Acad Sci, 1101:266-96, 2007 Y1 - 2024 ER - TY - GEN A1 - Altenbuchner, Amelie A1 - Haug, Sonja A1 - Mohr, Christa A1 - Scorna, Ulrike A1 - Weber, Karsten T1 - The impact of nurse advisors and online advice services on treatment adherence in multiple sclerosis (MS) T2 - 7th Joint European Committee for Treatment and Research in Multiple Sclerosis - Americas Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS-ACTRIMS), 25.-27.10.2017, Paris Y1 - 2017 UR - https://onlinelibrary.ectrims-congress.eu/ectrims/2017/ACTRIMS-ECTRIMS2017/199863/amelie.altenbuchner.the.impact.of.nurse.advisors.and.online.advice.services.on.html ER - TY - JOUR A1 - Arribas, Julia A1 - Antonelli, Giulio A1 - Frazzoni, Leonardo A1 - Fuccio, Lorenzo A1 - Ebigbo, Alanna A1 - van der Sommen, Fons A1 - Ghatwary, Noha A1 - Palm, Christoph A1 - Coimbra, Miguel A1 - Renna, Francesco A1 - Bergman, Jacques J.G.H.M. A1 - Sharma, Prateek A1 - Messmann, Helmut A1 - Hassan, Cesare A1 - Dinis-Ribeiro, Mario J. T1 - Standalone performance of artificial intelligence for upper GI neoplasia: a meta-analysis JF - Gut N2 - Objective: Artificial intelligence (AI) may reduce underdiagnosed or overlooked upper GI (UGI) neoplastic and preneoplastic conditions, due to subtle appearance and low disease prevalence. Only disease-specific AI performances have been reported, generating uncertainty on its clinical value. Design: We searched PubMed, Embase and Scopus until July 2020, for studies on the diagnostic performance of AI in detection and characterisation of UGI lesions. Primary outcomes were pooled diagnostic accuracy, sensitivity and specificity of AI. Secondary outcomes were pooled positive (PPV) and negative (NPV) predictive values. We calculated pooled proportion rates (%), designed summary receiving operating characteristic curves with respective area under the curves (AUCs) and performed metaregression and sensitivity analysis. Results: Overall, 19 studies on detection of oesophageal squamous cell neoplasia (ESCN) or Barrett's esophagus-related neoplasia (BERN) or gastric adenocarcinoma (GCA) were included with 218, 445, 453 patients and 7976, 2340, 13 562 images, respectively. AI-sensitivity/specificity/PPV/NPV/positive likelihood ratio/negative likelihood ratio for UGI neoplasia detection were 90% (CI 85% to 94%)/89% (CI 85% to 92%)/87% (CI 83% to 91%)/91% (CI 87% to 94%)/8.2 (CI 5.7 to 11.7)/0.111 (CI 0.071 to 0.175), respectively, with an overall AUC of 0.95 (CI 0.93 to 0.97). No difference in AI performance across ESCN, BERN and GCA was found, AUC being 0.94 (CI 0.52 to 0.99), 0.96 (CI 0.95 to 0.98), 0.93 (CI 0.83 to 0.99), respectively. Overall, study quality was low, with high risk of selection bias. No significant publication bias was found. Conclusion: We found a high overall AI accuracy for the diagnosis of any neoplastic lesion of the UGI tract that was independent of the underlying condition. This may be expected to substantially reduce the miss rate of precancerous lesions and early cancer when implemented in clinical practice. KW - Artificial Intelligence Y1 - 2021 U6 - https://doi.org/10.1136/gutjnl-2020-321922 VL - 70 IS - 8 SP - 1458 EP - 1468 PB - BMJ CY - London ER - TY - GEN A1 - Auer, Simon A1 - Reinker, Lukas A1 - Süß, Franz A1 - Dendorfer, Sebastian T1 - Comparing calculated and measured muscle activity of thigh muscles in dynamic motion. T2 - 27th Congress of the European Society of Biomechanics, 26 - 29 June 2022, Porto, Portugal Y1 - 2022 UR - https://drive.google.com/uc?id=1RBguxyHZE-Wr2y6ktOWK06_3lQg2M9Rb&export=download&confirm=t SP - 640 ER - TY - CHAP A1 - Birkenmaier, Clemens A1 - Krenkel, Lars ED - Chinesta, F. ED - Abgrall, R. ED - Allix, O. ED - Kalistke, M T1 - Convolutional Neural Networks for Approximation of Internal Non-Newtonian Multiphase Flow Fields T2 - 14th World Congress on Computational Mechanics (WCCM), ECCOMAS Congress 2020: 19–24 July 2020, Paris, France N2 - 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. KW - Deep Learning KW - Convolutional neural networks KW - Non-Newtonian multiphase flow Y1 - 2021 U6 - https://doi.org/10.23967/wccm-eccomas.2020.107 PB - CIMNE ER - TY - CHAP A1 - Birkenmaier, Clemens A1 - Krenkel, Lars ED - Dillmann, Andreas ED - Heller, Gerd ED - Krämer, Ewald ED - Wagner, Claus T1 - Convolutional Neural Networks for Approximation of Blood Flow in Artificial Lungs T2 - New Results in Numerical and Experimental Fluid Mechanics XIII: Contributions to the 22nd STAB/DGLR Symposium N2 - 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. KW - Deep learning fluid mechanics KW - Multiphase blood flow Y1 - 2021 SN - 978-3-030-79560-3 U6 - https://doi.org/10.1007/978-3-030-79561-0_43 IS - 1. Auflage SP - 451 EP - 460 PB - Springer International Publishing CY - Cham ER - TY - CHAP A1 - Chow, Rosan A1 - Frommeld, Debora A1 - Glanz, Jessica A1 - Boettger, Lars A1 - Stadlbauer, Jana A1 - Weber, Karsten ED - Palinko, Oskar T1 - Envision a Future of Living with Robots through Participatory Theatre BT - A Field Report T2 - Social Robotics N2 - With the goal to facilitate societal discussion, the interactive exhibitioin “Living with Robots”, open to the public at the Science Night in Regens-burg, Germany in 2024 gave visitors the opportunity to imagine a life with robots at home, at work, for care, and education. It was set up with multiple stations of which an improvisation theatre was the highlight. Most of public presentations of robots emphasize the power of technology; the exhibition was intentionally designed to set apart from this. We chose theatric play as the medium of engagement and communication, and it was shown to be fruitful. In this paper, we draw implications from our experiences and present some preliminary results on the public opinions of living with robots. Y1 - 2025 SN - 978-981-96-3519-1 U6 - https://doi.org/10.1007/978-981-96-3519-1_42 SN - 1611-3349 SP - 470 EP - 475 PB - Springer Nature CY - Singapore ER - TY - JOUR A1 - Deuter, Daniel A1 - Haj, Amer A1 - Brawanski, Alexander A1 - Krenkel, Lars A1 - Schmidt, Nils Ole A1 - Doenitz, Christian T1 - Fast simulation of hemodynamics in intracranial aneurysms for clinical use JF - Acta Neurochirurgica N2 - BACKGROUND: A widely accepted tool to assess hemodynamics, one of the most important factors in aneurysm pathophysiology, is Computational Fluid Dynamics (CFD). As current workflows are still time consuming and difficult to operate, CFD is not yet a standard tool in the clinical setting. There it could provide valuable information on aneurysm treatment, especially regarding local risks of rupture, which might help to optimize the individualized strategy of neurosurgical dissection during microsurgical aneurysm clipping. METHOD: We established and validated a semi-automated workflow using 3D rotational angiographies of 24 intracranial aneurysms from patients having received aneurysm treatment at our centre. Reconstruction of vessel geometry and generation of volume meshes was performed using AMIRA 6.2.0 and ICEM 17.1. For solving ANSYS CFX was used. For validational checks, tests regarding the volumetric impact of smoothing operations, the impact of mesh sizes on the results (grid convergence), geometric mesh quality and time tests for the time needed to perform the workflow were conducted in subgroups. RESULTS: Most of the steps of the workflow were performed directly on the 3D images requiring no programming experience. The workflow led to final CFD results in a mean time of 22 min 51.4 s (95%-CI 20 min 51.562 s-24 min 51.238 s, n = 5). Volume of the geometries after pre-processing was in mean 4.46% higher than before in the analysed subgroup (95%-CI 3.43-5.50%). Regarding mesh sizes, mean relative aberrations of 2.30% (95%-CI 1.51-3.09%) were found for surface meshes and between 1.40% (95%-CI 1.07-1.72%) and 2.61% (95%-CI 1.93-3.29%) for volume meshes. Acceptable geometric mesh quality of volume meshes was found. CONCLUSIONS: We developed a semi-automated workflow for aneurysm CFD to benefit from hemodynamic data in the clinical setting. The ease of handling opens the workflow to clinicians untrained in programming. As previous studies have found that the distribution of hemodynamic parameters correlates with thin-walled aneurysm areas susceptible to rupture, these data might be beneficial for the operating neurosurgeon during aneurysm surgery, even in acute cases. Y1 - 2025 U6 - https://doi.org/10.1007/s00701-025-06469-9 VL - 167 PB - Springer ER - TY - JOUR A1 - Ebigbo, Alanna A1 - Mendel, Robert A1 - Probst, Andreas A1 - Manzeneder, Johannes A1 - Prinz, Friederike A1 - Souza Jr., Luis Antonio de A1 - Papa, João Paulo A1 - Palm, Christoph A1 - Messmann, Helmut T1 - Real-time use of artificial intelligence in the evaluation of cancer in Barrett’s oesophagus JF - Gut N2 - Based on previous work by our group with manual annotation of visible Barrett oesophagus (BE) cancer images, a real-time deep learning artificial intelligence (AI) system was developed. While an expert endoscopist conducts the endoscopic assessment of BE, our AI system captures random images from the real-time camera livestream and provides a global prediction (classification), as well as a dense prediction (segmentation) differentiating accurately between normal BE and early oesophageal adenocarcinoma (EAC). The AI system showed an accuracy of 89.9% on 14 cases with neoplastic BE. KW - Speiseröhrenkrankheit KW - Diagnose KW - Maschinelles Lernen KW - Barrett's esophagus KW - Deep learning KW - real-time Y1 - 2020 U6 - https://doi.org/10.1136/gutjnl-2019-319460 VL - 69 IS - 4 SP - 615 EP - 616 PB - BMJ CY - London ER - TY - JOUR A1 - Ebigbo, Alanna A1 - Mendel, Robert A1 - Rückert, Tobias A1 - Schuster, Laurin A1 - Probst, Andreas A1 - Manzeneder, Johannes A1 - Prinz, Friederike A1 - Mende, Matthias A1 - Steinbrück, Ingo A1 - Faiss, Siegbert A1 - Rauber, David A1 - Souza Jr., Luis Antonio de A1 - Papa, João Paulo A1 - Deprez, Pierre A1 - Oyama, Tsuneo A1 - Takahashi, Akiko A1 - Seewald, Stefan A1 - Sharma, Prateek A1 - Byrne, Michael F. A1 - Palm, Christoph A1 - Messmann, Helmut T1 - Endoscopic prediction of submucosal invasion in Barrett’s cancer with the use of Artificial Intelligence: A pilot Study JF - Endoscopy N2 - Background and aims: The accurate differentiation between T1a and T1b Barrett’s cancer has both therapeutic and prognostic implications but is challenging even for experienced physicians. We trained an Artificial Intelligence (AI) system on the basis of deep artificial neural networks (deep learning) to differentiate between T1a and T1b Barrett’s cancer white-light images. Methods: Endoscopic images from three tertiary care centres in Germany were collected retrospectively. A deep learning system was trained and tested using the principles of cross-validation. A total of 230 white-light endoscopic images (108 T1a and 122 T1b) was evaluated with the AI-system. For comparison, the images were also classified by experts specialized in endoscopic diagnosis and treatment of Barrett’s cancer. Results: The sensitivity, specificity, F1 and accuracy of the AI-system in the differentiation between T1a and T1b cancer lesions was 0.77, 0.64, 0.73 and 0.71, respectively. There was no statistically significant difference between the performance of the AI-system and that of human experts with sensitivity, specificity, F1 and accuracy of 0.63, 0.78, 0.67 and 0.70 respectively. Conclusion: This pilot study demonstrates the first multicenter application of an AI-based system in the prediction of submucosal invasion in endoscopic images of Barrett’s cancer. AI scored equal to international experts in the field, but more work is necessary to improve the system and apply it to video sequences and in a real-life setting. Nevertheless, the correct prediction of submucosal invasion in Barret´s cancer remains challenging for both experts and AI. KW - Maschinelles Lernen KW - Neuronales Netz KW - Speiseröhrenkrebs KW - Diagnose KW - Artificial Intelligence KW - Machine learning KW - Adenocarcinoma KW - Barrett’s cancer KW - submucosal invasion Y1 - 2021 U6 - https://doi.org/10.1055/a-1311-8570 VL - 53 IS - 09 SP - 878 EP - 883 PB - Thieme CY - Stuttgart ER - TY - JOUR A1 - Ebigbo, Alanna A1 - Palm, Christoph A1 - Messmann, Helmut T1 - Barrett esophagus: What to expect from Artificial Intelligence? JF - Best Practice & Research Clinical Gastroenterology N2 - The evaluation and assessment of Barrett’s esophagus is challenging for both expert and nonexpert endoscopists. However, the early diagnosis of cancer in Barrett’s esophagus is crucial for its prognosis, and could save costs. Pre-clinical and clinical studies on the application of Artificial Intelligence (AI) in Barrett’s esophagus have shown promising results. In this review, we focus on the current challenges and future perspectives of implementing AI systems in the management of patients with Barrett’s esophagus. KW - Deep Learning KW - Künstliche Intelligenz KW - Computerunterstützte Medizin KW - Barrett KW - Adenocarcinoma KW - Artificial intelligence KW - Deep learning KW - Convolutional neural networks Y1 - 2021 U6 - https://doi.org/10.1016/j.bpg.2021.101726 SN - 1521-6918 VL - 52-53 IS - June-August PB - Elsevier ER - TY - JOUR A1 - Ebigbo, Alanna A1 - Palm, Christoph A1 - Probst, Andreas A1 - Mendel, Robert A1 - Manzeneder, Johannes A1 - Prinz, Friederike A1 - Souza Jr., Luis Antonio de A1 - Papa, João Paulo A1 - Siersema, Peter A1 - Messmann, Helmut T1 - A technical review of artificial intelligence as applied to gastrointestinal endoscopy: clarifying the terminology JF - Endoscopy International Open N2 - The growing number of publications on the application of artificial intelligence (AI) in medicine underlines the enormous importance and potential of this emerging field of research. In gastrointestinal endoscopy, AI has been applied to all segments of the gastrointestinal tract most importantly in the detection and characterization of colorectal polyps. However, AI research has been published also in the stomach and esophagus for both neoplastic and non-neoplastic disorders. The various technical as well as medical aspects of AI, however, remain confusing especially for non-expert physicians. This physician-engineer co-authored review explains the basic technical aspects of AI and provides a comprehensive overview of recent publications on AI in gastrointestinal endoscopy. Finally, a basic insight is offered into understanding publications on AI in gastrointestinal endoscopy. KW - Diagnose KW - Maschinelles Lernen KW - Gastroenterologie KW - Künstliche Intelligenz KW - Barrett's esophagus KW - Deep learning Y1 - 2019 U6 - https://doi.org/10.1055/a-1010-5705 VL - 07 IS - 12 SP - 1616 EP - 1623 PB - Georg Thieme Verlag CY - Stuttgart ER - TY - JOUR A1 - Eigenberger, Andreas A1 - Felthaus, Oliver A1 - Schratzenstaller, Thomas A1 - Haerteis, Silke A1 - Utpatel, Kirsten A1 - Prantl, Lukas T1 - The Effects of Shear Force-Based Processing of Lipoaspirates on White Adipose Tissue and the Differentiation Potential of Adipose Derived Stem Cells JF - cells N2 - 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. KW - white adipose tissue KW - lipograft KW - stem cells KW - fat grafting KW - surgery KW - cell-enriched lipotransfer KW - CELT KW - lipoaspirate Y1 - 2022 U6 - https://doi.org/10.3390/cells11162543 VL - 11 IS - 16 PB - MDPI CY - Basel ER - TY - JOUR A1 - Foltan, Maik A1 - Dinh, D. A1 - Gruber, Michael A1 - Müller, Thomas A1 - Hart, C. A1 - Krenkel, Lars A1 - Schmid, C. A1 - Lehle, Karla T1 - Incidence of neutrophil extracellular traps (NETs) in different membrane oxygenators: pilot in vitro experiments in commercially available coated membranes JF - Journal of artificial organs : the official journal of the Japanese Society for Artificial Organs N2 - Neutrophil extracellular traps (NETs) were detected in blood samples and in cellular deposits of oxygenator membranes during extracorporeal membrane oxygenation (ECMO) therapy and may be responsible for thrombogenesis. The aim was to evaluate the effect of the base material of gas fiber (GF, polymethylpentene) and heat exchange (HE) membranes and different antithrombogenic coatings on isolated granulocytes from healthy volunteers under static culture conditions. Contact of granulocytes with membranes from different ECMO oxygenators (with different surface coatings) and uncoated-GFs allowed detection of adherent cells and NETotic nuclear structures (normal, swollen, ruptured) using nuclear staining. Flow cytometry was used to identify cell activation (CD11b/CD62L, oxidative burst) of non-adherent cells. Uncoated-GFs were used as a reference. Within 3 h, granulocytes adhered to the same extent on all surfaces. In contrast, the ratio of normal to NETotic cells was significantly higher for uncoated-GFs (56-83%) compared to all coated GFs (34-72%) (p < 0.001) with no difference between the coatings. After material contact, non-adherent cells remained vital with unchanged oxidative burst function and the proportion of activated cells remained low. The expression of activation markers was independent of the origin of the GF material. In conclusion, the polymethylpentene surfaces of the GFs already induce NET formation. Antithrombogenic coatings can already reduce the proportion of NETotic nuclei. However, it cannot be ruled out that NET formation can induce thrombotic events. Therefore, new surfaces or coatings are required for future ECMO systems and long-term implantable artificial lungs. Y1 - 2025 U6 - https://doi.org/10.1007/s10047-024-01486-4 ER - TY - GEN A1 - Förstl, Nikolas A1 - Adler, Ina A1 - Suess, Franz A1 - Čechová, Hana A1 - Jansová, Magdalena A1 - Cimrman, Robert A1 - Vychytil, Jan A1 - Dendorfer, Sebastian T1 - Workflow for the development of a non-invasive feedback device to assess pelvic floor contractions T2 - ESB 2024, 29th Congress of the European Society of Biomechanics, 30 June-3 July 2024, Edinburgh, Scotland N2 - Introduction Pelvic floor disorders affect about 40% of women worldwide [1]. Pelvic floor muscle (PFM) training is both a preventive and a therapeutic intervention. Current PFM training devices are invasive and have little scientific evidence. The idea is to develop a noninvasive feedback device to assess adequate PFM contraction. Therefore, evidence-based female musculoskeletal models, non-invasive data acquisition, sensor technology and artificial intelligence (AI) will be combined. This work presents the workflow to achieve such a feedback device and describes the interaction of the technologies used. Methods Exercises that induce PFM contractions have been evaluated and defined. Motion capture of these exercises will provide input for female musculoskeletal models. A combination of biomechanical rigid body and FEM simulations will be used to estimate PFM contractions. In addition, a non-invasive sensor will measure pelvic floor activity. The simulated and measured data will be used to develop an AI model that provides feedback on PFM contractions based on non-invasive data collection. Results The AMMR (AnyBody Managed Model Repository) of the AnyBody modelling system (AMS, Aalborg, Denmark) serves as the initial model for performing inverse dynamic simulations of the exercises. To calculate the PFM forces, the full-body model must be supplemented with the relevant pelvic floor structures and a mass model of the internal organs. A modified abdominal pressure model must also be incorporated. The AMS calculates the PFM activities caused by the internal organ loads and the generated abdominal pressure during the exercises. The muscle activities are transferred to a FEM model of the female pelvic floor (SfePy, simple finite elements in Python). The identical pelvic floor structures were integrated into the FEM model as in the AMS. Active PFM contractions can be simulated using the FEM model. Movement of the coccyx due to PFM contractions has been reported in the literature [2,3]. Therefore, a noninvasive coccyx motion sensor will be developed to provide additional information on PFM contractions. The measured data (coccyx motion sensor, motion capture) and the simulation results of the models will be combined to create an AI feedback model using Python. The final feedback device will consist of the AI model and the developed coccyx motion sensor, which can reproduce the resulting PFM contractions based on the sensor data and simplified motion tracking. Discussion The creation of the AMS and the FEM model is a prerequisite for the development of the feedback device. The relevant structures in the models are located inside the body. This limits the ability to observe the structures during the exercises, which can lead to difficulties in model validation. The development of a user-friendly sensor with sufficient measuring accuracy of the coccyx motion is another challenge. Nevertheless, the workflow represents a promising approach to develop a noninvasive feedback system to assess PFM contraction. References 1. Wang et al, Front Public Health, 10:975829, 2022. 2. Bø et al, Neurourol Urodyn, 20:167–174, 2001. 3. Fujisaki et al, J Phys Ther Sci, 30:544–548, 2018. Acknowledgements This work was supported by the project no. BYCZ01-014 of the Program INTERREG Bavaria – Czechia 2021–2027. Y1 - 2024 ER - TY - JOUR A1 - Förstl, Nikolas A1 - Adler, Ina A1 - Süß, Franz A1 - Dendorfer, Sebastian T1 - Technologies for Evaluation of Pelvic Floor Functionality: A Systematic Review JF - Sensors N2 - Pelvic floor dysfunction is a common problem in women and has a negative impact on their quality of life. The aim of this review was to provide a general overview of the current state of technology used to assess pelvic floor functionality. It also provides literature research of the physiological and anatomical factors that correlate with pelvic floor health. This systematic review was conducted according to the PRISMA guidelines. The PubMed, ScienceDirect, Cochrane Library, and IEEE databases were searched for publications on sensor technology for the assessment of pelvic floor functionality. Anatomical and physiological parameters were identified through a manual search. In the systematic review, 114 publications were included. Twelve different sensor technologies were identified. Information on the obtained parameters, sensor position, test activities, and subject characteristics was prepared in tabular form from each publication. A total of 16 anatomical and physiological parameters influencing pelvic floor health were identified in 17 published studies and ranked for their statistical significance. Taken together, this review could serve as a basis for the development of novel sensors which could allow for quantifiable prevention and diagnosis, as well as particularized documentation of rehabilitation processes related to pelvic floor dysfunctions. Y1 - 2024 U6 - https://doi.org/10.3390/s24124001 N1 - Die Preprint-Version ist ebenfalls in diesem Repositorium verzeichnet unter: https://opus4.kobv.de/opus4-oth-regensburg/frontdoor/index/index/docId/7306 VL - 24 IS - 12 PB - MDPI ER - TY - JOUR A1 - Förstl, Nikolas A1 - Süß, Franz A1 - Englert, Carsten A1 - Dendorfer, Sebastian T1 - Design of a reverse shoulder implant to measure shoulder stiffness during implant component positioning JF - Medical Engineering & Physics N2 - 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. Y1 - 2023 U6 - https://doi.org/10.1016/j.medengphy.2023.104059 N1 - Corresponding author: Sebstian Dendorfer VL - 121 PB - Elsevier ET - Journal Pre-proof ER - TY - GEN A1 - Förstl, Nikolas A1 - Süß, Franz A1 - Englert, Carsten A1 - Dendorfer, Sebastian T1 - Design of a reverse shoulder implant to measure shoulder stiffness during implant component positioning T2 - Book of abstracts / ESB 2023, 28th Congress of the European Society of Biomechanics, 9-12 July 2023, Maastricht, The Netherlands. N2 - Introduction Dislocation of the shoulder joint is one of the more common complications after reverse total shoulder arthroplasty [1], which is often associated with malposition of the prosthetic components [2]. Therefore, achieving sufficient shoulder stability should not be neglected when positioning the implant components. One parameter for assessing shoulder stability can be shoulder stiffness. The aim of this work is to develop a reverse shoulder implant prototype that allows intraoperative measurement of shoulder stiffness while varying the position of the implant components. The measured stiffness could provide a quantitative statement regarding the optimal positioning of the implant components, which can be adjusted accordingly in the final reverse shoulder prosthesis. Methods To measure the stiffness of the shoulder joint, it is necessary to record the joint angles and the torques generated during movement. The changes in the rotation angles were measured using 3D hall sensors and magnets. The magnets were placed under the humerosocket, and the hall sensors were integrated into the glenosphere. The strength of the magnetic field was used to determine the position of the humerosocket in relation to the glenosphere. The accuracies of the angle measurements were tested using a test bench. Three thin film pressure sensors were used to record forces at different points under the humerosocket. To obtain a force value from the sensor signal, the sensors were calibrated using a load cell. The variation of the implant components positions was integrated into the prototype implant through different constructive mechanisms to adjust the stiffness of the shoulder joint. Results In the range of ±45° flexion/extension combined with ±15° adduction/abduction, the joint position could be determined with sufficient accuracy (error e ≤ 5°). The areas near the combined maximum deflections of ±45° flexion/extension and ±45° adduction/abduction indicate the greatest deviation from the target angle. The force values of the thin film sensors enable the calculation of moments around two axes. As variable component position parameters, the tray offset, the neck-shaft angle and the humerus version were integrated into the implant prototype. Discussion Ideally, the accuracy of the angle measurements should only depend on the amount of deflection and not on the direction of deflection. The asymmetric behavior indicates a deviation from the correct positioning of the hall sensors. The application of a calibration matrix could compensate for the measurement errors and could demonstrate the potential of the new method for joint angle measurements. The accuracy of the torque measurements and the functionality of the mechanical arresting mechanisms must be investigated in further studies. Overall, the developed measurement method can help to avoid malpositioning of the implant components in reverse total shoulder arthroplasty. References 1. Clark et al, J Shoulder and elbow surgery, 21:36-41 2012. 2. Randelli et al, J Musculoskeletal surgery, 98:15-18, 2014. Y1 - 2023 UR - https://esbiomech.org/conference/archive/2023maastricht/332.pdf ER - TY - JOUR A1 - Giordano, Katharina A1 - Lauer, Norina A1 - Leusch, Vera A1 - Kreiter, Daniel A1 - Corsten, Sabine T1 - Tablet-based biography work in long-term care homes to support quality of life and prevent depression JF - Educational Gerontology N2 - 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. KW - Biografieforschung KW - Senioren KW - Tablet PC KW - biography work KW - long-term care KW - tablet support Y1 - 2023 U6 - https://doi.org/10.1080/03601277.2023.2225362 SN - 1521-0472 SN - 0360-1277 SP - 1 EP - 14 PB - Taylor and Francis CY - London ER - TY - CHAP A1 - Glémarec, Yann A1 - Hörmann, Amelie A1 - Lauer, Norina A1 - Buche, Cédric A1 - Lugrin, Jean-Luc A1 - Latoschik, Marc Erich T1 - Towards Virtual Audience Simulation For Speech Therapy T2 - Proceedings of the 23rd ACM International Conference on Intelligent Virtual Agents (IVA '23), Würzburg, September 19 - 22, 2023 N2 - 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. KW - Stimme KW - Virtuelle Realität Y1 - 2023 SN - 78-1-4503-9994-4 U6 - https://doi.org/10.1145/3570945.3607348 PB - Association for Computing Machinery CY - New York ER -