@misc{AuerReinkerSuessetal., author = {Auer, Simon and Reinker, Lukas and S{\"u}ß, Franz and Dendorfer, Sebastian}, title = {Comparing calculated and measured muscle activity of thigh muscles in dynamic motion.}, series = {27th Congress of the European Society of Biomechanics, 26 - 29 June 2022, Porto, Portugal}, journal = {27th Congress of the European Society of Biomechanics, 26 - 29 June 2022, Porto, Portugal}, pages = {640}, language = {en} } @misc{MelznerPfeiferSuessetal., author = {Melzner, Maximilian and Pfeifer, Christian and S{\"u}ß, Franz and Dendorfer, Sebastian}, title = {Muskuloskeletal analysis of elbow stability for common injury patterns}, series = {27th Congress of the European Society of Biomechanics, 26 - 29 June 2022, Porto, Portugal}, journal = {27th Congress of the European Society of Biomechanics, 26 - 29 June 2022, Porto, Portugal}, pages = {654}, language = {en} } @misc{KoeglerIsmailRusavyetal., author = {K{\"o}gler, Michael and Ismail, Khaled M. and Rusavy, Zdenek and Kalis, Vladimir and S{\"u}ß, Franz and Dendorfer, Sebastian}, title = {Influence of bed height and stance on accoucheurs lower back and glenohumeral load during simulated childbirth}, series = {31st meeting of Czech Urogynaecological Society, Prague, 2022}, journal = {31st meeting of Czech Urogynaecological Society, Prague, 2022}, language = {en} } @misc{PfingstenBorgettoTomlinetal., author = {Pfingsten, Andrea and Borgetto, Bernhard and Tomlin, George S. and Max, Susanne and Trickes, Claudia}, title = {The Research Pyramid. Establishing the value of evidence from multiple research approaches in a systematic review}, series = {World Physical Therapy Congress der World Confederation for Physical Therapy (WCPT), Amsterdam, 2011}, journal = {World Physical Therapy Congress der World Confederation for Physical Therapy (WCPT), Amsterdam, 2011}, abstract = {Goal: The Research Pyramid is a model that values and integrates external evidence from multiple research approaches: experimental and observational as well as quantitative and qualitative. It provides a basis to collect and synthesize research findings for answering questions that emerge in therapy practice and for subsequent decision making, based on research evidence.}, language = {en} } @misc{AltenbuchnerHaugMohretal., author = {Altenbuchner, Amelie and Haug, Sonja and Mohr, Christa and Scorna, Ulrike and Weber, Karsten}, title = {The impact of nurse advisors and online advice services on treatment adherence in multiple sclerosis (MS)}, series = {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}, journal = {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}, language = {en} } @techreport{PfingstenBorgetto, author = {Pfingsten, Andrea and Borgetto, Bernhard}, title = {Efficacy and effectiveness of physical therapy in adults with rheumatoid arthritis}, edition = {Version: 14.05.2020}, abstract = {What is the efficacy of physical therapy in the treatment of rheumatoid arthritis? What is the effectiveness of physical therapy in the treatment of rheumatoid arthritis? Which differences do exist between efficacy and effectiveness of rheumatoid arthritis?}, language = {en} } @article{MuehlingSandriesserDendorferetal., author = {M{\"u}hling, Mischa and Sandriesser, Sabrina and Dendorfer, Sebastian and Augat, Peter}, title = {Assessment of implant internal stresses under physiological femoral loading: Translation to a simplified bending load model}, series = {Journal of Biomechanics}, journal = {Journal of Biomechanics}, number = {112229}, publisher = {Elsevier}, issn = {1873-2380}, doi = {10.1016/j.jbiomech.2024.112229}, pages = {17}, abstract = {The success of surgical treatment for fractures hinges on various factors, notably accurate surgical indication. The process of developing and certifying a new osteosynthesis device is a lengthy and costly process that requires multiple cycles of review and validation. Current methods, however, often rely on predecessor standards rather than physiological loads in specific anatomical locations. This study aimed to determine actual loads experienced by an osteosynthesis plate, exemplified by a standard locking plate for the femoral shaft, utilizing finite elements analysis (FEA) and to obtain the bending moments for implant development standard tests. A protocol was developed, involving the creation and validation of a fractured femur model fixed with a locking plate, mechanical testing, and FEA. The model's validation demonstrated exceptional accuracy in predicting deformations, and the FEA revealed peak stresses in the fracture bridging zone. Results of a parametric analysis indicate that larger fracture gaps significantly impact implant mechanical behavior, potentially compromising stability. This study underscores the critical need for realistic physiological conditions in implant evaluations, providing an innovative translational approach to identify internal loads and optimize implant designs. In conclusion, this research contributes to enhancing the understanding of implant performance under physiological conditions, promoting improved designs and evaluations in fracture treatments.}, language = {en} } @article{RusavyCechovaDendorferetal., author = {Rusavy, Zdenek and Cechova, Hana and Dendorfer, Sebastian and Kalis, Vladimir and Ismail, Khaled M.}, title = {Is the Finnish grip tight enough? A manometric study of two manual perineal protection techniques}, series = {Acta Obstetricia et Gynecologica Scandinavica}, journal = {Acta Obstetricia et Gynecologica Scandinavica}, edition = {Early view}, publisher = {Wiley}, doi = {10.1111/aogs.15033}, language = {en} } @article{MelznerSuessDendorfer, author = {Melzner, Maximilian and Suess, Franz and Dendorfer, Sebastian}, title = {The impact of anatomical uncertainties on the predictions of a musculoskeletal hand model - a sensitivity study}, series = {Computer Methods in Biomechanics and Biomedical Engineering}, volume = {25}, journal = {Computer Methods in Biomechanics and Biomedical Engineering}, number = {2}, publisher = {Taylor \& Francis}, issn = {1476-8259}, doi = {10.1080/10255842.2021.1940974}, pages = {156 -- 164}, abstract = {Outputs of musculoskeletal models should be considered probabilistic rather than deterministic as they are affected by inaccuracies and estimations associated with the development of the model. One of these uncertainties being critical for modeling arises from the determination of the muscles' line of action and the physiological cross-sectional area. Therefore, the aim of this study was to evaluate the outcome sensitivity of model predictions from a musculoskeletal hand model in comparison to the uncertainty of these input parameters. For this purpose, the kinematics and muscle activities of different hand movements (abduction of the fingers, abduction of the thumb, and flexion of the thumb) were recorded. One thousand simulations were calculated for each movement using the Latin hypercube sampling method with a corresponding variation of the muscle origin/insertion points and the cross-sectional area. Comparing the standard hand to simulations incorporating uncertainties of input parameters shows no major deviations in on- and off-set time point of muscle activities. About 60\% of simulations are located within a ± 30\% interval around the standard model concerning joint reaction forces. The comparison with the variation of the input data leads to the conclusion that the standard hand model is able to provide not over-scattered outcomes and, therefore, can be considered relatively stable. These results are of practical importance to the personalization of a musculoskeletal model with subject-specific bone geometries and hence changed muscle line of action.}, subject = {Biomechanik}, language = {en} } @article{MelznerEngelhardtSimonetal., author = {Melzner, Maximilian and Engelhardt, Lucas and Simon, Ulrich and Dendorfer, Sebastian}, title = {Electromyography-Based Validation of a Musculoskeletal Hand Model}, series = {Journal of Biomechanical Engineering}, volume = {144}, journal = {Journal of Biomechanical Engineering}, number = {2}, publisher = {American Society of Mechanical Engineers, ASME}, doi = {10.1115/1.4052115}, pages = {8}, abstract = {Regarding the prevention of injuries and rehabilitation of the human hand, musculoskeletal simulations using an inverse dynamics approach allow for insights of the muscle recruitment and thus acting forces on the hand. Currently, several hand models from various research groups are in use, which are mainly validated by the comparison of numerical and anatomical moment arms. In contrast to this validation and model-building technique by cadaver studies, the aim of this study is to further validate a recently published hand model [1] by analyzing numerically calculated muscle activities in comparison to experimentally measured electromyographical signals of the muscles. Therefore, the electromyographical signals of 10 hand muscles of five test subjects performing seven different hand movements were measured. The kinematics of these tasks were used as input for the hand model, and the numerical muscle activities were computed. To analyze the relationship between simulated and measured activities, the time difference of the muscle on- and off-set points was calculated, which resulted in a mean on- and off-set time difference of 0.58 s between the experimental data and the model. The largest differences were detected for movements that mainly addressed the wrist. One major issue comparing simulated and measured muscle activities of the hand is cross-talk. Nevertheless, the results show that the hand model fits the experiment quite accurately despite some limitations and is a further step toward patient-specific modeling of the upper extremity.}, subject = {Elektromyographie}, language = {en} } @inproceedings{GlemarecHoermannLaueretal., author = {Gl{\´e}marec, Yann and H{\"o}rmann, Amelie and Lauer, Norina and Buche, C{\´e}dric and Lugrin, Jean-Luc and Latoschik, Marc Erich}, title = {Towards Virtual Audience Simulation For Speech Therapy}, series = {Proceedings of the 23rd ACM International Conference on Intelligent Virtual Agents (IVA '23), W{\"u}rzburg, September 19 - 22, 2023}, booktitle = {Proceedings of the 23rd ACM International Conference on Intelligent Virtual Agents (IVA '23), W{\"u}rzburg, September 19 - 22, 2023}, publisher = {Association for Computing Machinery}, address = {New York}, isbn = {78-1-4503-9994-4}, doi = {10.1145/3570945.3607348}, pages = {1-3}, abstract = {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.}, subject = {Stimme}, language = {en} } @article{MennCorstenLaueretal., author = {Menn, Lara and Corsten, Sabine and Lauer, Norina and Wallace, Sarah J.}, title = {The effectiveness of biographical approaches in long-term care: a systematic review}, series = {Gerontologist}, volume = {60}, journal = {Gerontologist}, number = {4}, doi = {10.1093/geront/gnz074}, pages = {309 -- 328}, abstract = {Background and Objectives Older adults in long-term residential aged care experience loneliness and reduced quality of life (QoL). Biographical approaches use the recall of the past events to increase self-efficacy, promote acceptance, and improve QoL. The aim of the systematic review was to examine the effectiveness of biographical approaches in improving the QoL of older adults in long-term residential aged care. Research Design and Methods This systematic review was conducted in accordance with PRISMA guidelines, using databases Medline, CINAHL, and the Cochrane Library. A 2-phase search strategy was used to identify research literature relating to the use of biographical interventions. Narrative analysis was used to synthesize results. Results Twenty-one studies met inclusion criteria for this review, comprising both group and individual interventions. The findings were inconsistent. Subjective elements of QoL of older adults improved in a narrow majority of the studies. Of a total of 21 outcomes investigated in group interventions, 11 resulted in significant improvements in QoL. The 16 outcomes of the individual interventions resulted in 10 significant improvements. The most significant influence was observed in life satisfaction. Group reminiscence also had a particularly strong influence on self-esteem. Discussion and Implications Although benefits were observed, it remains unclear why some interventions led to improvements in subjective elements of QoL and others did not. Exploration of the participant perspective through qualitative data collection and more detailed description of interventions in future studies could lead to a better understanding of the treatment components that are related to improved outcomes for older adults.}, language = {en} } @misc{LauerCorsten, author = {Lauer, Norina and Corsten, Sabine}, title = {Supporting peer-led aphasia support groups with a specific training program: a first proof of concept}, series = {International Aphasia Rehabilitation Conference, London}, journal = {International Aphasia Rehabilitation Conference, London}, language = {en} } @article{LauerCorsten, author = {Lauer, Norina and Corsten, Sabine}, title = {Quality of life in leaders and members of peer-led aphasia support groups - preliminary results of a systematic approach}, series = {Aphasiology}, volume = {32}, journal = {Aphasiology}, number = {sup1: International Aphasia Rehabilitation Conference (IARC)}, publisher = {Taylor\&Francis}, doi = {10.1080/02687038.2018.1485839}, pages = {119 -- 121}, language = {en} } @inproceedings{BirkenmaierKrenkel, author = {Birkenmaier, Clemens and Krenkel, Lars}, title = {Convolutional Neural Networks for Approximation of Internal Non-Newtonian Multiphase Flow Fields}, series = {14th World Congress on Computational Mechanics (WCCM), ECCOMAS Congress 2020: 19-24 July 2020, Paris, France}, booktitle = {14th World Congress on Computational Mechanics (WCCM), ECCOMAS Congress 2020: 19-24 July 2020, Paris, France}, editor = {Chinesta, F. and Abgrall, R. and Allix, O. and Kalistke, M}, publisher = {CIMNE}, doi = {10.23967/wccm-eccomas.2020.107}, abstract = {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.}, language = {en} } @inproceedings{StelzerTauwaldVielsmeieretal., author = {Stelzer, Vera and Tauwald, Sandra Melina and Vielsmeier, Veronika and Cieplik, Fabian and Kandulski, Arne and Schneider-Brachert, Wulf and W{\"u}nsch, Olaf and R{\"u}tten, Markus and Krenkel, Lars}, title = {Generation and Distribution of Surgical Smoke During High Frequency Electrocauterization}, series = {New Results in Numerical and Experimental Fluid Mechanics XIV. STAB/DGLR Symposium 2022}, booktitle = {New Results in Numerical and Experimental Fluid Mechanics XIV. STAB/DGLR Symposium 2022}, editor = {Dillman, Andreas and Heller, Gerd and Kraemer, Ewald and Wagner, Claus and Weiss, Julien}, publisher = {Springer Nature Switzerland AG}, address = {Cham, Switzerland}, isbn = {978-3-031-40481-8}, doi = {10.1007/978-3-031-40482-5_53}, pages = {559 -- 568}, abstract = {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.}, language = {en} } @misc{TauwaldQuadrioRuettenetal., author = {Tauwald, Sandra Melina and Quadrio, Maurizio and R{\"u}tten, Markus and Stemmer, Christian and Krenkel, Lars}, title = {High Spatial Resolution Tomo-PIV of the Nasopharynx Focussing on the Physiological Breathing Cycle}, series = {23. DGLR Fach-Symposium Str{\"o}mungen mit Abl{\"o}sung, 09./10. November 2022, Berlin, Deutschland}, journal = {23. DGLR Fach-Symposium Str{\"o}mungen mit Abl{\"o}sung, 09./10. November 2022, Berlin, Deutschland}, address = {G{\"o}ttingen}, organization = {Deutsche Gesellschaft f{\"u}r Luft- und Raumfahrt e.V. / Arbeitsgemeinschaft Str{\"o}mungen mit Abl{\"o}sung, AG STAB}, abstract = {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.}, language = {en} } @misc{StelzerTauwaldVielsmeieretal., author = {Stelzer, Vera and Tauwald, Sandra Melina and Vielsmeier, Veronika and Cieplik, Fabian and Kandulski, Arne and Schneider-Brachert, Wulf and Wuensch, Olaf and R{\"u}tten, Markus and Krenkel, Lars}, title = {Generation, Distribution, and Contagiousness of Surgical Smoke during Tracheotomies}, series = {23. DGLR Fach-Symposium Str{\"o}mungen mit Abl{\"o}sung, 09./10. November 2022, Berlin, Deutschland}, journal = {23. DGLR Fach-Symposium Str{\"o}mungen mit Abl{\"o}sung, 09./10. November 2022, Berlin, Deutschland}, address = {G{\"o}ttingen}, organization = {Deutsche Gesellschaft f{\"u}r Luft- und Raumfahrt e.V. / Arbeitsgemeinschaft Str{\"o}mungen mit Abl{\"o}sung, AG STAB}, abstract = {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.}, language = {en} } @article{StelzerKrenkel, author = {Stelzer, Vera and Krenkel, Lars}, title = {2D numerical investigations derived from a 3D dragonfly wing captured with a high-resolution micro-CT}, series = {Technology and health care : official journal of the European Society for Engineering and Medicine}, volume = {30}, journal = {Technology and health care : official journal of the European Society for Engineering and Medicine}, number = {1}, publisher = {IOS Press}, doi = {10.3233/THC-219010}, pages = {283 -- 289}, abstract = {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 K{\´a}rm{\´a}n 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.}, language = {en} } @article{SchecklmannSchmausserKlingeretal., author = {Schecklmann, Martin and Schmausser, Maximilian and Klinger, Felix and Kreuzer, Peter M. and Krenkel, Lars and Langguth, Berthold}, title = {Resting motor threshold and magnetic field output of the figure-of-8 and the double-cone coil}, series = {scientific reports}, volume = {10}, journal = {scientific reports}, number = {1}, publisher = {Nature}, doi = {10.1038/s41598-020-58034-2}, abstract = {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.}, language = {en} } @misc{StelzerRuettenKrenkel, author = {Stelzer, Vera and R{\"u}tten, Markus and Krenkel, Lars}, title = {Numerical Investigation of a 3D Dragonfly Wing Captured with a High-Resolution Micro-CT}, series = {8th European Congress on Computational Methods in Applied Sciences and Engineering, ECCOMAS Congress 2022, 5-9 June 2022, Oslo, Norway}, journal = {8th European Congress on Computational Methods in Applied Sciences and Engineering, ECCOMAS Congress 2022, 5-9 June 2022, Oslo, Norway}, abstract = {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.}, language = {en} } @article{TauwaldMichelBrandtetal., author = {Tauwald, Sandra Melina and Michel, Johanna and Brandt, Marie and Vielsmeier, Veronika and Stemmer, Christian and Krenkel, Lars}, title = {Experimental studies and mathematical modeling of the viscoelastic rheology of tracheobronchial mucus from respiratory healthy patients}, series = {Multidisciplinary Respiratory Medicine}, volume = {18}, journal = {Multidisciplinary Respiratory Medicine}, number = {1}, publisher = {PAGEPress}, address = {Pavia, Italy}, issn = {2049-6958}, doi = {10.4081/mrm.2023.923}, pages = {12}, abstract = {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.}, language = {en} } @inproceedings{BirkenmaierKrenkel, author = {Birkenmaier, Clemens and Krenkel, Lars}, title = {Convolutional Neural Networks for Approximation of Blood Flow in Artificial Lungs}, series = {New Results in Numerical and Experimental Fluid Mechanics XIII: Contributions to the 22nd STAB/DGLR Symposium}, booktitle = {New Results in Numerical and Experimental Fluid Mechanics XIII: Contributions to the 22nd STAB/DGLR Symposium}, number = {1. Auflage}, editor = {Dillmann, Andreas and Heller, Gerd and Kr{\"a}mer, Ewald and Wagner, Claus}, publisher = {Springer International Publishing}, address = {Cham}, isbn = {978-3-030-79560-3}, doi = {10.1007/978-3-030-79561-0_43}, pages = {451 -- 460}, abstract = {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.}, language = {en} } @inproceedings{TauwaldQuadrioRuettenetal., author = {Tauwald, Sandra Melina and Quadrio, Maurizio and R{\"u}tten, Markus and Stemmer, Christian and Krenkel, Lars}, title = {High Spatial Resolution Tomo-PIV of the Trachea Focussing on the Physiological Breathing Cycle}, series = {New Results in Numerical and Experimental Fluid Mechanics XIV - Contributions to the 23nd STAB/DGLR Symposium}, booktitle = {New Results in Numerical and Experimental Fluid Mechanics XIV - Contributions to the 23nd STAB/DGLR Symposium}, publisher = {Springer}, abstract = {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.}, language = {en} } @article{EigenbergerFelthausSchratzenstalleretal., author = {Eigenberger, Andreas and Felthaus, Oliver and Schratzenstaller, Thomas and Haerteis, Silke and Utpatel, Kirsten and Prantl, Lukas}, title = {The Effects of Shear Force-Based Processing of Lipoaspirates on White Adipose Tissue and the Differentiation Potential of Adipose Derived Stem Cells}, series = {cells}, volume = {11}, journal = {cells}, number = {16}, publisher = {MDPI}, address = {Basel}, doi = {10.3390/cells11162543}, pages = {13}, abstract = {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.}, language = {en} } @misc{GschossmannDendorfer, author = {Gschoßmann, Lukas and Dendorfer, Sebastian}, title = {Personalized Rehabilitation after Total Knee Arthroplasty}, series = {32nd Annual Meeting of the European Orthopaedic Research Society (EORS 2024), 18 - 20 September 2024, Aalborg}, journal = {32nd Annual Meeting of the European Orthopaedic Research Society (EORS 2024), 18 - 20 September 2024, Aalborg}, language = {en} } @misc{AdlerFoerstlSuessetal., author = {Adler, Ina and F{\"o}rstl, Nikolas and S{\"u}ß, Franz and Dendorfer, Sebastian}, title = {Biomechanical exposition of the factors influencing the pelvic floor muscle activity}, series = {ESB 2024, 29th Congress of the European Society of Biomechanics, 30 June-3 July 2024, Edinburgh, Scotland}, journal = {ESB 2024, 29th Congress of the European Society of Biomechanics, 30 June-3 July 2024, Edinburgh, Scotland}, abstract = {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}, language = {en} } @misc{GschossmannSteinDendorfer, author = {Gschoßmann, Lukas and Stein, Leonhard and Dendorfer, Sebastian}, title = {Subject Specific Scaling Of Muscle Strengths In The Lower Extremity For Musculoskeletal Models}, series = {ESB 2024, 29th Congress of the European Society of Biomechanics, 30 June-3 July 2024, Edinburgh, Scotland}, journal = {ESB 2024, 29th Congress of the European Society of Biomechanics, 30 June-3 July 2024, Edinburgh, Scotland}, language = {en} } @inproceedings{SuessMelznerDendorfer, author = {Suess, Franz and Melzner, Maximilian and Dendorfer, Sebastian}, title = {Towards ergonomics working - machine learning algorithms and musculoskeletal modeling}, series = {IOP Conference Series: Materials Science and Engineering}, volume = {1208}, booktitle = {IOP Conference Series: Materials Science and Engineering}, publisher = {IOP Publishing}, issn = {1757-899X}, doi = {10.1088/1757-899X/1208/1/012001}, abstract = {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.}, language = {en} } @inproceedings{ChowFrommeldGlanzetal., author = {Chow, Rosan and Frommeld, Debora and Glanz, Jessica and Boettger, Lars and Stadlbauer, Jana and Weber, Karsten}, title = {Envision a Future of Living with Robots through Participatory Theatre}, series = {Social Robotics}, booktitle = {Social Robotics}, editor = {Palinko, Oskar}, publisher = {Springer Nature}, address = {Singapore}, isbn = {978-981-96-3519-1}, issn = {1611-3349}, doi = {10.1007/978-981-96-3519-1_42}, pages = {470 -- 475}, abstract = {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.}, language = {en} } @article{FoerstlSuessEnglertetal., author = {F{\"o}rstl, Nikolas and S{\"u}ß, Franz and Englert, Carsten and Dendorfer, Sebastian}, title = {Design of a reverse shoulder implant to measure shoulder stiffness during implant component positioning}, series = {Medical Engineering \& Physics}, volume = {121}, journal = {Medical Engineering \& Physics}, edition = {Journal Pre-proof}, publisher = {Elsevier}, doi = {10.1016/j.medengphy.2023.104059}, pages = {22}, abstract = {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.}, language = {en} } @article{FoerstlAdlerSuessetal., author = {F{\"o}rstl, Nikolas and Adler, Ina and S{\"u}ß, Franz and Dendorfer, Sebastian}, title = {Technologies for Evaluation of Pelvic Floor Functionality: A Systematic Review}, series = {Sensors}, volume = {24}, journal = {Sensors}, number = {12}, publisher = {MDPI}, doi = {10.3390/s24124001}, abstract = {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.}, language = {en} } @misc{FoerstlAdlerSuessetal., author = {F{\"o}rstl, Nikolas and Adler, Ina and Suess, Franz and Čechov{\´a}, Hana and Jansov{\´a}, Magdalena and Cimrman, Robert and Vychytil, Jan and Dendorfer, Sebastian}, title = {Workflow for the development of a non-invasive feedback device to assess pelvic floor contractions}, series = {ESB 2024, 29th Congress of the European Society of Biomechanics, 30 June-3 July 2024, Edinburgh, Scotland}, journal = {ESB 2024, 29th Congress of the European Society of Biomechanics, 30 June-3 July 2024, Edinburgh, Scotland}, abstract = {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. B{\o} 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.}, language = {en} } @misc{FoerstlSuessEnglertetal., author = {F{\"o}rstl, Nikolas and S{\"u}ß, Franz and Englert, Carsten and Dendorfer, Sebastian}, title = {Design of a reverse shoulder implant to measure shoulder stiffness during implant component positioning}, series = {Book of abstracts / ESB 2023, 28th Congress of the European Society of Biomechanics, 9-12 July 2023, Maastricht, The Netherlands.}, journal = {Book of abstracts / ESB 2023, 28th Congress of the European Society of Biomechanics, 9-12 July 2023, Maastricht, The Netherlands.}, abstract = {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.}, language = {en} } @article{RusavyCechovaDendorferetal., author = {Rusavy, Zdenek and Cechova, Hana and Dendorfer, Sebastian and Kalis, Vladimir and Ismail, Khaled M.}, title = {Is the Finnish grip tight enough? A manometric study of two manual perineal protection techniques}, series = {Acta Obstetricia et Gynecologica Scandinavica}, volume = {104}, journal = {Acta Obstetricia et Gynecologica Scandinavica}, number = {2}, publisher = {Wiley}, doi = {10.1111/aogs.15033}, pages = {423 -- 425}, language = {en} } @article{MoragKieningerEissnertetal., author = {Morag, Sarah and Kieninger, Martin and Eissnert, Christoph and Auer, Simon and Dendorfer, Sebastian and Popp, Daniel and Hoffmann, Johannes and Kieninger, B{\"a}rbel}, title = {Comparison of different techniques for prehospital cervical spine immobilization: Biomechanical measurements with a wireless motion capture system}, series = {PLOS ONE}, volume = {18}, journal = {PLOS ONE}, number = {11}, publisher = {PLOS}, address = {San Francisco, California}, doi = {10.1371/journal.pone.0292300}, pages = {1 -- 14}, language = {en} } @article{GiordanoLauerLeuschetal., author = {Giordano, Katharina and Lauer, Norina and Leusch, Vera and Kreiter, Daniel and Corsten, Sabine}, title = {Tablet-based biography work in long-term care homes to support quality of life and prevent depression}, series = {Educational Gerontology}, journal = {Educational Gerontology}, publisher = {Taylor and Francis}, address = {London}, issn = {1521-0472}, doi = {10.1080/03601277.2023.2225362}, pages = {1 -- 14}, abstract = {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.}, subject = {Biografieforschung}, language = {en} } @article{KurfessCorstenNickeletal., author = {Kurfess, Christina and Corsten, Sabine and Nickel, Maren and Kreiter, Daniel and Lauer, Norina}, title = {Improving health-related quality of life for people with aphasia through peer support using the digital network PeerPAL}, series = {Aphasiology}, journal = {Aphasiology}, publisher = {Taylor \& Francis}, doi = {10.1080/02687038.2025.2505641}, pages = {1 -- 24}, abstract = {Background: Aphasia is often associated with psychosocial changes that may affect health-related quality of life, risk of depression or social participation. One possible intervention to address these psychosocial changes is peer contact. However, this often requires professional support (e.g. supervised support groups), whereas a digital option may allow for autonomous, asynchronous peer contact. Therefore, we developed an app adapted to the specific needs of people with aphasia to stimulate digital and analogue interactions. Aims: The aim of the study was to evaluate a tailored app for people with aphasia that can be used to establish peer contact and enable digital and analogue interactions. The impact of using the app on health-related quality of life, social participation and depression prevention was analysed. Methods \& Procedures: The study design was a pre-post wait-list-controlled comparison in which half of the participants (n = 18) waited three months before starting the intervention, and the other half of the participants (n = 18) started the intervention immediately. During the intervention, the app was used by all participants (n = 36) for three months after a training session, followed by three months of optional app use until follow-up. Health-related quality of life (SAQOL39g, GHQ-12), communicative participation (CPIB), social support (F-SozU), depression markers (GDS, DISCs), and activity in the app were recorded at each time point. Analyses were mainly non-parametric to calculate changes during the intervention and to compare the intervention with the waiting period. The study is registered in the German Register of Clinical Trials (DRKS00023855). Outcomes \& Results: SAQOL-39g data improved significantly for the whole group during the intervention period (z = -3.043, p = 0.002, r = -0.598), but not during the waiting period (z = 0.402, p = 0.705). Scores remained stable until follow-up, and there was no worsening of depression markers over the entire period. No linear correlation was found between the improvement in SAQOL-39g and activity in the app (p = 0.329, r = 0.167). Conclusions: People with aphasia were able to use the app and showed an intervention-specific effect on health-related quality of life. The amount of activity in the app seems to be less critical for changes. Other factors, such as feeling connected to peers, appear to be relevant. Future studies should explore who might benefit most from the app.}, subject = {Aphasie}, language = {en} } @article{LingelHausPaschkeetal., author = {Lingel, Maximilian P. and Haus, Moritz and Paschke, Lukas and Foltan, Maik and Lubnow, Matthias and Gruber, Michael and Krenkel, Lars and Lehle, Karla}, title = {Clinical relevance of cell-free DNA during venovenous extracorporeal membrane oxygenation}, series = {Artificial organs}, volume = {47}, journal = {Artificial organs}, number = {11}, publisher = {Wiley}, issn = {1525-1594}, doi = {10.1111/aor.14616}, pages = {1720 -- 1731}, abstract = {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.}, language = {en} } @article{FoltanDinhGruberetal., author = {Foltan, Maik and Dinh, D. and Gruber, Michael and M{\"u}ller, Thomas and Hart, C. and Krenkel, Lars and Schmid, C. and Lehle, Karla}, title = {Incidence of neutrophil extracellular traps (NETs) in different membrane oxygenators: pilot in vitro experiments in commercially available coated membranes}, series = {Journal of artificial organs : the official journal of the Japanese Society for Artificial Organs}, journal = {Journal of artificial organs : the official journal of the Japanese Society for Artificial Organs}, doi = {10.1007/s10047-024-01486-4}, abstract = {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.}, language = {en} } @article{DeuterHajBrawanskietal., author = {Deuter, Daniel and Haj, Amer and Brawanski, Alexander and Krenkel, Lars and Schmidt, Nils Ole and Doenitz, Christian}, title = {Fast simulation of hemodynamics in intracranial aneurysms for clinical use}, series = {Acta Neurochirurgica}, volume = {167}, journal = {Acta Neurochirurgica}, publisher = {Springer}, doi = {10.1007/s00701-025-06469-9}, pages = {14}, abstract = {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.}, language = {en} } @inproceedings{RuettenKrenkelQuadrio, author = {R{\"u}tten, Markus and Krenkel, Lars and Quadrio, Maurizio}, title = {Simulation and Analyis of the Unsteady Flow within Nasal Airways}, series = {9th European Congress on Computational Methods in Applied Sciences and Engineering - ECCOMAS Congress, 3-7 June 2024, Lisbon, Portugal}, booktitle = {9th European Congress on Computational Methods in Applied Sciences and Engineering - ECCOMAS Congress, 3-7 June 2024, Lisbon, Portugal}, language = {en} } @article{EbigboPalmProbstetal., author = {Ebigbo, Alanna and Palm, Christoph and Probst, Andreas and Mendel, Robert and Manzeneder, Johannes and Prinz, Friederike and Souza Jr., Luis Antonio de and Papa, Jo{\~a}o Paulo and Siersema, Peter and Messmann, Helmut}, title = {A technical review of artificial intelligence as applied to gastrointestinal endoscopy: clarifying the terminology}, series = {Endoscopy International Open}, volume = {07}, journal = {Endoscopy International Open}, number = {12}, publisher = {Georg Thieme Verlag}, address = {Stuttgart}, doi = {10.1055/a-1010-5705}, pages = {1616 -- 1623}, abstract = {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.}, subject = {Diagnose}, language = {en} } @misc{ScheppachRauberStallhoferetal., author = {Scheppach, Markus W. and Rauber, David and Stallhofer, Johannes and Muzalyova, Anna and Otten, Vera and Manzeneder, Carolin and Schwamberger, Tanja and Wanzl, Julia and Schlottmann, Jakob and Tadic, Vidan and Probst, Andreas and Schnoy, Elisabeth and R{\"o}mmele, Christoph and Fleischmann, Carola and Meinikheim, Michael and Miller, Silvia and M{\"a}rkl, Bruno and Palm, Christoph and Messmann, Helmut and Ebigbo, Alanna}, title = {Performance comparison of a deep learning algorithm with endoscopists in the detection of duodenal villous atrophy (VA)}, series = {Endoscopy}, volume = {55}, journal = {Endoscopy}, number = {S02}, publisher = {Thieme}, doi = {10.1055/s-0043-1765421}, pages = {S165}, abstract = {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.}, language = {en} } @article{EbigboMendelProbstetal., author = {Ebigbo, Alanna and Mendel, Robert and Probst, Andreas and Manzeneder, Johannes and Prinz, Friederike and Souza Jr., Luis Antonio de and Papa, Jo{\~a}o Paulo and Palm, Christoph and Messmann, Helmut}, title = {Real-time use of artificial intelligence in the evaluation of cancer in Barrett's oesophagus}, series = {Gut}, volume = {69}, journal = {Gut}, number = {4}, publisher = {BMJ}, address = {London}, doi = {10.1136/gutjnl-2019-319460}, pages = {615 -- 616}, abstract = {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.}, subject = {Speiser{\"o}hrenkrankheit}, language = {en} } @article{ArribasAntonelliFrazzonietal., author = {Arribas, Julia and Antonelli, Giulio and Frazzoni, Leonardo and Fuccio, Lorenzo and Ebigbo, Alanna and van der Sommen, Fons and Ghatwary, Noha and Palm, Christoph and Coimbra, Miguel and Renna, Francesco and Bergman, Jacques J.G.H.M. and Sharma, Prateek and Messmann, Helmut and Hassan, Cesare and Dinis-Ribeiro, Mario J.}, title = {Standalone performance of artificial intelligence for upper GI neoplasia: a meta-analysis}, series = {Gut}, volume = {70}, journal = {Gut}, number = {8}, publisher = {BMJ}, address = {London}, doi = {10.1136/gutjnl-2020-321922}, pages = {1458 -- 1468}, abstract = {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.}, language = {en} } @article{EbigboMendelRueckertetal., author = {Ebigbo, Alanna and Mendel, Robert and R{\"u}ckert, Tobias and Schuster, Laurin and Probst, Andreas and Manzeneder, Johannes and Prinz, Friederike and Mende, Matthias and Steinbr{\"u}ck, Ingo and Faiss, Siegbert and Rauber, David and Souza Jr., Luis Antonio de and Papa, Jo{\~a}o Paulo and Deprez, Pierre and Oyama, Tsuneo and Takahashi, Akiko and Seewald, Stefan and Sharma, Prateek and Byrne, Michael F. and Palm, Christoph and Messmann, Helmut}, title = {Endoscopic prediction of submucosal invasion in Barrett's cancer with the use of Artificial Intelligence: A pilot Study}, series = {Endoscopy}, volume = {53}, journal = {Endoscopy}, number = {09}, publisher = {Thieme}, address = {Stuttgart}, doi = {10.1055/a-1311-8570}, pages = {878 -- 883}, abstract = {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.}, subject = {Maschinelles Lernen}, language = {en} } @inproceedings{SouzaJrPassosMendeletal., author = {Souza Jr., Luis Antonio de and Passos, Leandro A. and Mendel, Robert and Ebigbo, Alanna and Probst, Andreas and Messmann, Helmut and Palm, Christoph and Papa, Jo{\~a}o Paulo}, title = {Fine-tuning Generative Adversarial Networks using Metaheuristics}, series = {Bildverarbeitung f{\"u}r die Medizin 2021. Proceedings, German Workshop on Medical Image Computing, Regensburg, March 7-9, 2021}, booktitle = {Bildverarbeitung f{\"u}r die Medizin 2021. Proceedings, German Workshop on Medical Image Computing, Regensburg, March 7-9, 2021}, publisher = {Springer Vieweg}, address = {Wiesbaden}, isbn = {978-3-658-33197-9}, doi = {10.1007/978-3-658-33198-6_50}, pages = {205 -- 210}, abstract = {Barrett's esophagus denotes a disorder in the digestive system that affects the esophagus' mucosal cells, causing reflux, and showing potential convergence to esophageal adenocarcinoma if not treated in initial stages. Thus, fast and reliable computer-aided diagnosis becomes considerably welcome. Nevertheless, such approaches usually suffer from imbalanced datasets, which can be addressed through Generative Adversarial Networks (GANs). Such techniques generate realistic images based on observed samples, even though at the cost of a proper selection of its hyperparameters. Many works employed a class of nature-inspired algorithms called metaheuristics to tackle the problem considering distinct deep learning approaches. Therefore, this paper's main contribution is to introduce metaheuristic techniques to fine-tune GANs in the context of Barrett's esophagus identification, as well as to investigate the feasibility of generating high-quality synthetic images for early-cancer assisted identification.}, subject = {Endoskopie}, language = {en} } @misc{RoemmeleMendelRauberetal., author = {R{\"o}mmele, Christoph and Mendel, Robert and Rauber, David and R{\"u}ckert, Tobias and Byrne, Michael F. and Palm, Christoph and Messmann, Helmut and Ebigbo, Alanna}, title = {Endoscopic Diagnosis of Eosinophilic Esophagitis Using a deep Learning Algorithm}, series = {Endoscopy}, volume = {53}, journal = {Endoscopy}, number = {S 01}, publisher = {Georg Thieme Verlag}, address = {Stuttgart}, doi = {10.1055/s-0041-1724274}, abstract = {Aims Eosinophilic esophagitis (EoE) is easily missed during endoscopy, either because physicians are not familiar with its endoscopic features or the morphologic changes are too subtle. In this preliminary paper, we present the first attempt to detect EoE in endoscopic white light (WL) images using a deep learning network (EoE-AI). Methods 401 WL images of eosinophilic esophagitis and 871 WL images of normal esophageal mucosa were evaluated. All images were assessed for the Endoscopic Reference score (EREFS) (edema, rings, exudates, furrows, strictures). Images with strictures were excluded. EoE was defined as the presence of at least 15 eosinophils per high power field on biopsy. A convolutional neural network based on the ResNet architecture with several five-fold cross-validation runs was used. Adding auxiliary EREFS-classification branches to the neural network allowed the inclusion of the scores as optimization criteria during training. EoE-AI was evaluated for sensitivity, specificity, and F1-score. In addition, two human endoscopists evaluated the images. Results EoE-AI showed a mean sensitivity, specificity, and F1 of 0.759, 0.976, and 0.834 respectively, averaged over the five distinct cross-validation runs. With the EREFS-augmented architecture, a mean sensitivity, specificity, and F1-score of 0.848, 0.945, and 0.861 could be demonstrated respectively. In comparison, the two human endoscopists had an average sensitivity, specificity, and F1-score of 0.718, 0.958, and 0.793. Conclusions To the best of our knowledge, this is the first application of deep learning to endoscopic images of EoE which were also assessed after augmentation with the EREFS-score. The next step is the evaluation of EoE-AI using an external dataset. We then plan to assess the EoE-AI tool on endoscopic videos, and also in real-time. This preliminary work is encouraging regarding the ability for AI to enhance physician detection of EoE, and potentially to do a true "optical biopsy" but more work is needed.}, language = {en} } @article{ScheppachRauberStallhoferetal., author = {Scheppach, Markus W. and Rauber, David and Stallhofer, Johannes and Muzalyova, Anna and Otten, Vera and Manzeneder, Carolin and Schwamberger, Tanja and Wanzl, Julia and Schlottmann, Jakob and Tadic, Vidan and Probst, Andreas and Schnoy, Elisabeth and R{\"o}mmele, Christoph and Fleischmann, Carola and Meinikheim, Michael and Miller, Silvia and M{\"a}rkl, Bruno and Stallmach, Andreas and Palm, Christoph and Messmann, Helmut and Ebigbo, Alanna}, title = {Detection of duodenal villous atrophy on endoscopic images using a deep learning algorithm}, series = {Gastrointestinal Endoscopy}, journal = {Gastrointestinal Endoscopy}, publisher = {Elsevier}, doi = {10.1016/j.gie.2023.01.006}, abstract = {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.}, language = {en} } @misc{MeinikheimMendelProbstetal., author = {Meinikheim, Michael and Mendel, Robert and Probst, Andreas and Scheppach, Markus W. and Schnoy, Elisabeth and Nagl, Sandra and R{\"o}mmele, Christoph and Prinz, Friederike and Schlottmann, Jakob and Golger, Daniela and Palm, Christoph and Messmann, Helmut and Ebigbo, Alanna}, title = {AI-assisted detection and characterization of early Barrett's neoplasia: Results of an Interim analysis}, series = {Endoscopy}, volume = {55}, journal = {Endoscopy}, number = {S02}, publisher = {Thieme}, doi = {10.1055/s-0043-1765437}, pages = {S169}, abstract = {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.}, language = {en} } @article{ScheppachMendelMuzalyovaetal., author = {Scheppach, Markus W. and Mendel, Robert and Muzalyova, Anna and Rauber, David and Probst, Andreas and Nagl, Sandra and R{\"o}mmele, Christoph and Yip, Hon Chi and Lau, Louis Ho Shing and G{\"o}lder, Stefan Karl and Schmidt, Arthur and Kouladouros, Konstantinos and Abdelhafez, Mohamed and Walter, Benjamin M. and Meinikheim, Michael and Chiu, Philip Wai Yan and Palm, Christoph and Messmann, Helmut and Ebigbo, Alanna}, title = {Artificial intelligence improves submucosal vessel detection during third space endoscopy}, series = {Endoscopy}, journal = {Endoscopy}, publisher = {Thieme}, address = {Stuttgart}, doi = {10.1055/a-2534-1164}, abstract = {Background and study aims: While artificial intelligence (AI) shows high potential in decision support for diagnostic gastrointestinal endoscopy, its role in therapeutic endoscopy remains unclear. Third space endoscopic procedures pose the risk of intraprocedural bleeding. Therefore, we aimed to develop an AI algorithm for intraprocedural blood vessel detection. Patients and Methods: Using a test dataset with 101 standardized video clips containing 200 predefined submucosal blood vessels, 19 endoscopists were evaluated for the vessel detection rate (VDR) and time (VDT) with and without support of an AI algorithm. Test subjects were grouped according to experience in ESD. Results: With AI support, endoscopists VDR increased from 56.4\% [CI 54.1-58.6] to 72.4\% [CI 70.3-74.4]. Endoscopists' VDT dropped from 6.7sec [CI 6.2-7.1] to 5.2sec [CI 4.8-5.7]. False positive (FP) readings appeared in 4.5\% of frames and were marked significantly shorter than true positives (6.0sec [CI 5.28-6.70] vs. 0.7sec [CI 0.55-0.87]). Conclusions: AI improved the vessel detection rate and time of endoscopists during third space endoscopy. While these data need to be corroborated by clinical trials, AI may prove to be an invaluable tool for the improvement of endoscopic interventions.}, language = {en} } @misc{ScheppachRauberMendeletal., author = {Scheppach, Markus W. and Rauber, David and Mendel, Robert and Palm, Christoph and Byrne, Michael F. and Messmann, Helmut and Ebigbo, Alanna}, title = {Detection Of Celiac Disease Using A Deep Learning Algorithm}, series = {Endoscopy}, volume = {53}, journal = {Endoscopy}, number = {S 01}, publisher = {Georg Thieme Verlag}, address = {Stuttgart}, doi = {10.1055/s-0041-1724970}, abstract = {Aims Celiac disease (CD) is a complex condition caused by an autoimmune reaction to ingested gluten. Due to its polymorphic manifestation and subtle endoscopic presentation, the diagnosis is difficult and thus the disorder is underreported. We aimed to use deep learning to identify celiac disease on endoscopic images of the small bowel. Methods Patients with small intestinal histology compatible with CD (MARSH classification I-III) were extracted retrospectively from the database of Augsburg University hospital. They were compared to patients with no clinical signs of CD and histologically normal small intestinal mucosa. In a first step MARSH III and normal small intestinal mucosa were differentiated with the help of a deep learning algorithm. For this, the endoscopic white light images were divided into five equal-sized subsets. We avoided splitting the images of one patient into several subsets. A ResNet-50 model was trained with the images from four subsets and then validated with the remaining subset. This process was repeated for each subset, such that each subset was validated once. Sensitivity, specificity, and harmonic mean (F1) of the algorithm were determined. Results The algorithm showed values of 0.83, 0.88, and 0.84 for sensitivity, specificity, and F1, respectively. Further data showing a comparison between the detection rate of the AI model and that of experienced endoscopists will be available at the time of the upcoming conference. Conclusions We present the first clinical report on the use of a deep learning algorithm for the detection of celiac disease using endoscopic images. Further evaluation on an external data set, as well as in the detection of CD in real-time, will follow. However, this work at least suggests that AI can assist endoscopists in the endoscopic diagnosis of CD, and ultimately may be able to do a true optical biopsy in live-time.}, language = {en} } @unpublished{WeiherervonRiedheimBrebantetal., author = {Weiherer, Maximilian and von Riedheim, Antonia and Br{\´e}bant, Vanessa and Egger, Bernhard and Palm, Christoph}, title = {iRBSM: A Deep Implicit 3D Breast Shape Model}, doi = {10.48550/arXiv.2412.13244}, pages = {6}, abstract = {We present the first deep implicit 3D shape model of the female breast, building upon and improving the recently proposed Regensburg Breast Shape Model (RBSM). Compared to its PCA-based predecessor, our model employs implicit neural representations; hence, it can be trained on raw 3D breast scans and eliminates the need for computationally demanding non-rigid registration -- a task that is particularly difficult for feature-less breast shapes. The resulting model, dubbed iRBSM, captures detailed surface geometry including fine structures such as nipples and belly buttons, is highly expressive, and outperforms the RBSM on different surface reconstruction tasks. Finally, leveraging the iRBSM, we present a prototype application to 3D reconstruct breast shapes from just a single image. Model and code publicly available at this https URL.}, language = {en} } @misc{MendelSouzaJrRauberetal., author = {Mendel, Robert and Souza Jr., Luis Antonio de and Rauber, David and Papa, Jo{\~a}o Paulo and Palm, Christoph}, title = {Abstract: Semi-supervised Segmentation Based on Error-correcting Supervision}, series = {Bildverarbeitung f{\"u}r die Medizin 2021. Proceedings, German Workshop on Medical Image Computing, Regensburg, March 7-9, 2021}, journal = {Bildverarbeitung f{\"u}r die Medizin 2021. Proceedings, German Workshop on Medical Image Computing, Regensburg, March 7-9, 2021}, publisher = {Springer Vieweg}, address = {Wiesbaden}, isbn = {978-3-658-33197-9}, doi = {10.1007/978-3-658-33198-6_43}, pages = {178}, abstract = {Pixel-level classification is an essential part of computer vision. For learning from labeled data, many powerful deep learning models have been developed recently. In this work, we augment such supervised segmentation models by allowing them to learn from unlabeled data. Our semi-supervised approach, termed Error-Correcting Supervision, leverages a collaborative strategy. Apart from the supervised training on the labeled data, the segmentation network is judged by an additional network.}, subject = {Deep Learning}, language = {en} } @article{EbigboPalmMessmann, author = {Ebigbo, Alanna and Palm, Christoph and Messmann, Helmut}, title = {Barrett esophagus: What to expect from Artificial Intelligence?}, series = {Best Practice \& Research Clinical Gastroenterology}, volume = {52-53}, journal = {Best Practice \& Research Clinical Gastroenterology}, number = {June-August}, publisher = {Elsevier}, issn = {1521-6918}, doi = {10.1016/j.bpg.2021.101726}, abstract = {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.}, subject = {Deep Learning}, language = {en} } @article{PassosSouzaJrMendeletal., author = {Passos, Leandro A. and Souza Jr., Luis Antonio de and Mendel, Robert and Ebigbo, Alanna and Probst, Andreas and Messmann, Helmut and Palm, Christoph and Papa, Jo{\~a}o Paulo}, title = {Barrett's esophagus analysis using infinity Restricted Boltzmann Machines}, series = {Journal of Visual Communication and Image Representation}, volume = {59}, journal = {Journal of Visual Communication and Image Representation}, publisher = {Elsevier}, doi = {10.1016/j.jvcir.2019.01.043}, pages = {475 -- 485}, abstract = {The number of patients with Barret's esophagus (BE) has increased in the last decades. Considering the dangerousness of the disease and its evolution to adenocarcinoma, an early diagnosis of BE may provide a high probability of cancer remission. However, limitations regarding traditional methods of detection and management of BE demand alternative solutions. As such, computer-aided tools have been recently used to assist in this problem, but the challenge still persists. To manage the problem, we introduce the infinity Restricted Boltzmann Machines (iRBMs) to the task of automatic identification of Barrett's esophagus from endoscopic images of the lower esophagus. Moreover, since iRBM requires a proper selection of its meta-parameters, we also present a discriminative iRBM fine-tuning using six meta-heuristic optimization techniques. We showed that iRBMs are suitable for the context since it provides competitive results, as well as the meta-heuristic techniques showed to be appropriate for such task.}, subject = {Speiser{\"o}hrenkrankheit}, language = {en} } @misc{RueckertRiederRauberetal., author = {R{\"u}ckert, Tobias and Rieder, Maximilian and Rauber, David and Xiao, Michel and Humolli, Eg and Feussner, Hubertus and Wilhelm, Dirk and Palm, Christoph}, title = {Augmenting instrument segmentation in video sequences of minimally invasive surgery by synthetic smoky frames}, series = {International Journal of Computer Assisted Radiology and Surgery}, volume = {18}, journal = {International Journal of Computer Assisted Radiology and Surgery}, number = {Suppl 1}, publisher = {Springer Nature}, doi = {10.1007/s11548-023-02878-2}, pages = {S54 -- S56}, language = {en} } @misc{ScheppachMendelProbstetal., author = {Scheppach, Markus W. and Mendel, Robert and Probst, Andreas and Rauber, David and R{\"u}ckert, Tobias and Meinikheim, Michael and Palm, Christoph and Messmann, Helmut and Ebigbo, Alanna}, title = {Real-time detection and delineation of tissue during third-space endoscopy using artificial intelligence (AI)}, series = {Endoscopy}, volume = {55}, journal = {Endoscopy}, number = {S02}, publisher = {Thieme}, doi = {10.1055/s-0043-1765128}, pages = {S53 -- S54}, abstract = {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.}, subject = {Speiser{\"o}hrenkrankheit}, language = {en} } @inproceedings{WeberNunesRauberPalm, author = {Weber Nunes, Danilo and Rauber, David and Palm, Christoph}, title = {Self-supervised 3D Vision Transformer Pre-training for Robust Brain Tumor Classification}, series = {Bildverarbeitung f{\"u}r die Medizin 2025: Proceedings, German Conference on Medical Image Computing, Regensburg March 09-11, 2025}, booktitle = {Bildverarbeitung f{\"u}r die Medizin 2025: Proceedings, German Conference on Medical Image Computing, Regensburg March 09-11, 2025}, editor = {Palm, Christoph and Breininger, Katharina and Deserno, Thomas M. and Handels, Heinz and Maier, Andreas and Maier-Hein, Klaus H. and Tolxdorff, Thomas}, publisher = {Springer Vieweg}, address = {Wiesbaden}, doi = {10.1007/978-3-658-47422-5_69}, pages = {298 -- 303}, abstract = {Brain tumors pose significant challenges in neurology, making precise classification crucial for prognosis and treatment planning. This work investigates the effectiveness of a self-supervised learning approach-masked autoencoding (MAE)-to pre-train a vision transformer (ViT) model for brain tumor classification. Our method uses non-domain specific data, leveraging the ADNI and OASIS-3 MRI datasets, which primarily focus on degenerative diseases, for pretraining. The model is subsequently fine-tuned and evaluated on the BraTS glioma and meningioma datasets, representing a novel use of these datasets for tumor classification. The pre-trained MAE ViT model achieves an average F1 score of 0.91 in a 5-fold cross-validation setting, outperforming the nnU-Net encoder trained from scratch, particularly under limited data conditions. These findings highlight the potential of self-supervised MAE in enhancing brain tumor classification accuracy, even with restricted labeled data.}, language = {en} } @inproceedings{WeiherervonRiedheimBrebantetal., author = {Weiherer, Maximilian and von Riedheim, Antonia and Br{\´e}bant, Vanessa and Egger, Bernhard and Palm, Christoph}, title = {iRBSM: A Deep Implicit 3D Breast Shape Model}, series = {Bildverarbeitung f{\"u}r die Medizin 2025: Proceedings, German Conference on Medical Image Computing, Regensburg March 09-11, 2025}, booktitle = {Bildverarbeitung f{\"u}r die Medizin 2025: Proceedings, German Conference on Medical Image Computing, Regensburg March 09-11, 2025}, editor = {Palm, Christoph and Breininger, Katharina and Deserno, Thomas M. and Handels, Heinz and Maier, Andreas and Maier-Hein, Klaus H. and Tolxdorff, Thomas}, publisher = {Springer Vieweg}, address = {Wiesbaden}, doi = {10.1007/978-3-658-47422-5_11}, pages = {38 -- 43}, abstract = {We present the first deep implicit 3D shape model of the female breast, building upon and improving the recently proposed Regensburg Breast Shape Model (RBSM). Compared to its PCA-based predecessor, our model employs implicit neural representations; hence, it can be trained on raw 3D breast scans and eliminates the need for computationally demanding non-rigid registration, a task that is particularly difficult for feature-less breast shapes. The resulting model, dubbed iRBSM, captures detailed surface geometry including fine structures such as nipples and belly buttons, is highly expressive, and outperforms the RBSM on different surface reconstruction tasks. Finally, leveraging the iRBSM, we present a prototype application to 3D reconstruct breast shapes from just a single image. Model and code publicly available at https://rbsm.re-mic.de/implicit.}, language = {en} } @inproceedings{RueckertRiederFeussneretal., author = {R{\"u}ckert, Tobias and Rieder, Maximilian and Feussner, Hubertus and Wilhelm, Dirk and R{\"u}ckert, Daniel and Palm, Christoph}, title = {Smoke Classification in Laparoscopic Cholecystectomy Videos Incorporating Spatio-temporal Information}, series = {Bildverarbeitung f{\"u}r die Medizin 2024: Proceedings, German Workshop on Medical Image Computing, March 10-12, 2024, Erlangen}, booktitle = {Bildverarbeitung f{\"u}r die Medizin 2024: Proceedings, German Workshop on Medical Image Computing, March 10-12, 2024, Erlangen}, editor = {Maier, Andreas and Deserno, Thomas M. and Handels, Heinz and Maier-Hein, Klaus H. and Palm, Christoph and Tolxdorff, Thomas}, publisher = {Springeer}, address = {Wiesbaden}, doi = {10.1007/978-3-658-44037-4_78}, pages = {298 -- 303}, abstract = {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.}, language = {en} } @unpublished{MendelRueckertWilhelmetal., author = {Mendel, Robert and R{\"u}ckert, Tobias and Wilhelm, Dirk and R{\"u}ckert, Daniel and Palm, Christoph}, title = {Motion-Corrected Moving Average: Including Post-Hoc Temporal Information for Improved Video Segmentation}, doi = {10.48550/arXiv.2403.03120}, pages = {9}, abstract = {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.}, subject = {Deep Learning}, language = {en} } @misc{ScheppachMendelProbstetal., author = {Scheppach, Markus W. and Mendel, Robert and Probst, Andreas and Meinikheim, Michael and Palm, Christoph and Messmann, Helmut and Ebigbo, Alanna}, title = {Artificial Intelligence (AI) - assisted vessel and tissue recognition during third space endoscopy (Smart ESD)}, series = {Zeitschrift f{\"u}r Gastroenterologie}, volume = {60}, journal = {Zeitschrift f{\"u}r Gastroenterologie}, number = {08}, publisher = {Georg Thieme Verlag}, address = {Stuttgart}, doi = {10.1055/s-0042-1755110}, abstract = {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.}, subject = {Bildgebendes Verfahren}, language = {en} } @unpublished{RueckertRueckertPalm, author = {R{\"u}ckert, Tobias and R{\"u}ckert, Daniel and Palm, Christoph}, title = {Methods and datasets for segmentation of minimally invasive surgical instruments in endoscopic images and videos: A review of the state of the art}, doi = {10.48550/arXiv.2304.13014}, pages = {25}, abstract = {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.}, language = {en} } @article{TroesterEcksteinKenneletal., author = {Tr{\"o}ster, Mark and Eckstein, Simon and Kennel, Paula and Kopp, Verna and Benkiser, Alina and Bihlmeier, Felicitas and Daub, Urban and Maufroy, Christophe and Dendorfer, Sebastian and Fritzsche, Lars and Schneider, Urs and Bauernhasl, Thomas}, title = {Person-specific evaluation method for occupational exoskeletons - Biomechanical body heat map}, series = {Applied Ergonomics}, volume = {132}, journal = {Applied Ergonomics}, publisher = {Elsevier}, doi = {10.1016/j.apergo.2025.104671}, pages = {30}, abstract = {Human-centred and ergonomic work design is one of the most important drivers for increasing the competitiveness of the European Union. As a flexible, person-specific occupational measure, exoskeletons promise great potential for effectively reducing individual ergonomic stress. Digital human models can provide important insights and offer great potential for systematising the effect and targeted use of exoskeletons, supporting their effective implementation in practice. In this article, digital human models are applied on two levels. Firstly, a realistic industrial logistics scenario in which boxes had to be relocated is designed with the help of a digital human model for workplace and process planning and secondly, a new biomechanical evaluation methodology to analyse intended and unintended effects on internal stress on the human body is demonstrated by applying musculoskeletal exoskeleton human models of four test subjects. Finally, the modelled biomechanical support tendencies of one exoskeleton are preliminary validated using EMG measurement data of the back muscles collected from the four male workers. The preliminary analysis of two back-support exoskeletons to demonstrate the new methodological approach confirms the expected, intended effects in the lower back and reveals unintended effects, such as e.g. changes in knee kinetics when applying a soft or hard-frame exoskeleton. Furthermore, the exemplary results to demonstrate the methodological approach expose notable differences between the test subjects, which underlines the relevance of person-specific evaluation and consideration of exoskeleton support. The preliminary validation shows a correlation between the modelled and the EMG-measured biomechanical exoskeleton support of the considered back muscles.}, language = {en} }