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 -