@misc{Dendorfer, author = {Dendorfer, Sebastian}, title = {Diagnostische Genauigkeit und klinische Anwendungen tragbarer Bewegungssensoren f{\"u}r die Kniegelenksrehabilitation}, address = {N{\"u}rnberg}, language = {de} } @misc{ScheppachWeberNunesArizietal., author = {Scheppach, Markus W. and Weber Nunes, Danilo and Arizi, X. and Rauber, David and Probst, Andreas and Nagl, Sandra and R{\"o}mmele, Christoph and Meinikheim, Michael and Palm, Christoph and Messmann, Helmut and Ebigbo, Alanna}, title = {Procedural phase recognition in endoscopic submucosal dissection (ESD) using artificial intelligence (AI)}, series = {Endoscopy}, volume = {56}, journal = {Endoscopy}, number = {S 02}, publisher = {Thieme}, address = {Stuttgart}, doi = {10.1055/s-0044-1783804}, pages = {S439}, abstract = {Aims Recent evidence suggests the possibility of intraprocedural phase recognition in surgical operations as well as endoscopic interventions such as peroral endoscopic myotomy and endoscopic submucosal dissection (ESD) by AI-algorithms. The intricate measurement of intraprocedural phase distribution may deepen the understanding of the procedure. Furthermore, real-time quality assessment as well as automation of reporting may become possible. Therefore, we aimed to develop an AI-algorithm for intraprocedural phase recognition during ESD. Methods A training dataset of 364385 single images from 9 full-length ESD videos was compiled. Each frame was classified into one procedural phase. Phases included scope manipulation, marking, injection, application of electrical current and bleeding. Allocation of each frame was only possible to one category. This training dataset was used to train a Video Swin transformer to recognize the phases. Temporal information was included via logarithmic frame sampling. Validation was performed using two separate ESD videos with 29801 single frames. Results The validation yielded sensitivities of 97.81\%, 97.83\%, 95.53\%, 85.01\% and 87.55\% for scope manipulation, marking, injection, electric application and bleeding, respectively. Specificities of 77.78\%, 90.91\%, 95.91\%, 93.65\% and 84.76\% were measured for the same parameters. Conclusions The developed algorithm was able to classify full-length ESD videos on a frame-by-frame basis into the predefined classes with high sensitivities and specificities. Future research will aim at the development of quality metrics based on single-operator phase distribution.}, language = {en} } @misc{ScheppachMendelRauberetal., author = {Scheppach, Markus W. and Mendel, Robert and Rauber, David and Probst, Andreas and Nagl, Sandra and R{\"o}mmele, Christoph and Meinikheim, Michael and Palm, Christoph and Messmann, Helmut and Ebigbo, Alanna}, title = {Artificial Intelligence (AI) improves endoscopists' vessel detection during endoscopic submucosal dissection (ESD)}, series = {Endoscopy}, volume = {56}, journal = {Endoscopy}, number = {S 02}, publisher = {Thieme}, address = {Stuttgart}, doi = {10.1055/s-0044-1782891}, pages = {S93}, abstract = {Aims While AI has been successfully implemented in detecting and characterizing colonic polyps, its role in therapeutic endoscopy remains to be elucidated. Especially third space endoscopy procedures like ESD and peroral endoscopic myotomy (POEM) pose a technical challenge and the risk of operator-dependent complications like intraprocedural bleeding and perforation. Therefore, we aimed at developing an AI-algorithm for intraprocedural real time vessel detection during ESD and POEM. Methods A training dataset consisting of 5470 annotated still images from 59 full-length videos (47 ESD, 12 POEM) and 179681 unlabeled images was used to train a DeepLabV3+neural network with the ECMT semi-supervised learning method. Evaluation for vessel detection rate (VDR) and time (VDT) of 19 endoscopists with and without AI-support was performed using a testing dataset of 101 standardized video clips with 200 predefined blood vessels. Endoscopists were stratified into trainees and experts in third space endoscopy. Results The AI algorithm had a mean VDR of 93.5\% and a median VDT of 0.32 seconds. AI support was associated with a statistically significant increase in VDR from 54.9\% to 73.0\% and from 59.0\% to 74.1\% for trainees and experts, respectively. VDT significantly decreased from 7.21 sec to 5.09 sec for trainees and from 6.10 sec to 5.38 sec for experts in the AI-support group. False positive (FP) readings occurred in 4.5\% of frames. FP structures were detected significantly shorter than true positives (0.71 sec vs. 5.99 sec). Conclusions AI improved VDR and VDT of trainees and experts in third space endoscopy and may reduce performance variability during training. Further research is needed to evaluate the clinical impact of this new technology.}, language = {en} } @misc{EbigboRauberAyoubetal., author = {Ebigbo, Alanna and Rauber, David and Ayoub, Mousa and Birzle, Lisa and Matsumura, Tomoaki and Probst, Andreas and Steinbr{\"u}ck, Ingo and Nagl, Sandra and R{\"o}mmele, Christoph and Meinikheim, Michael and Scheppach, Markus W. and Palm, Christoph and Messmann, Helmut}, title = {Early Esophageal Cancer and the Generalizability of Artificial Intelligence}, series = {Endoscopy}, volume = {56}, journal = {Endoscopy}, number = {S 02}, publisher = {Thieme}, address = {Stuttgart}, doi = {10.1055/s-0044-1783775}, pages = {S428}, abstract = {Aims Artificial Intelligence (AI) systems in gastrointestinal endoscopy are narrow because they are trained to solve only one specific task. Unlike Narrow-AI, general AI systems may be able to solve multiple and unrelated tasks. We aimed to understand whether an AI system trained to detect, characterize, and segment early Barrett's neoplasia (Barrett's AI) is only capable of detecting this pathology or can also detect and segment other diseases like early squamous cell cancer (SCC). Methods 120 white light (WL) and narrow-band endoscopic images (NBI) from 60 patients (1 WL and 1 NBI image per patient) were extracted from the endoscopic database of the University Hospital Augsburg. Images were annotated by three expert endoscopists with extensive experience in the diagnosis and endoscopic resection of early esophageal neoplasias. An AI system based on DeepLabV3+architecture dedicated to early Barrett's neoplasia was tested on these images. The AI system was neither trained with SCC images nor had it seen the test images prior to evaluation. The overlap between the three expert annotations („expert-agreement") was the ground truth for evaluating AI performance. Results Barrett's AI detected early SCC with a mean intersection over reference (IoR) of 92\% when at least 1 pixel of the AI prediction overlapped with the expert-agreement. When the threshold was increased to 5\%, 10\%, and 20\% overlap with the expert-agreement, the IoR was 88\%, 85\% and 82\%, respectively. The mean Intersection Over Union (IoU) - a metric according to segmentation quality between the AI prediction and the expert-agreement - was 0.45. The mean expert IoU as a measure of agreement between the three experts was 0.60. Conclusions In the context of this pilot study, the predictions of SCC by a Barrett's dedicated AI showed some overlap to the expert-agreement. Therefore, features learned from Barrett's cancer-related training might be helpful also for SCC prediction. Our results allow different possible explanations. On the one hand, some Barrett's cancer features generalize toward the related task of assessing early SCC. On the other hand, the Barrett's AI is less specific to Barrett's cancer than a general predictor of pathological tissue. However, we expect to enhance the detection quality significantly by extending the training to SCC-specific data. The insight of this study opens the way towards a transfer learning approach for more efficient training of AI to solve tasks in other domains.}, language = {en} } @misc{ZellmerRauberProbstetal., author = {Zellmer, Stephan and Rauber, David and Probst, Andreas and Weber, Tobias and Braun, Georg and R{\"o}mmele, Christoph and Nagl, Sandra and Schnoy, Elisabeth and Messmann, Helmut and Ebigbo, Alanna and Palm, Christoph}, title = {Artificial intelligence as a tool in the detection of the papillary ostium during ERCP}, series = {Endoscopy}, volume = {56}, journal = {Endoscopy}, number = {S 02}, publisher = {Thieme}, address = {Stuttgart}, doi = {10.1055/s-0044-1783138}, pages = {S198}, abstract = {Aims Endoscopic retrograde cholangiopancreaticography (ERCP) is the gold standard in the diagnosis as well as treatment of diseases of the pancreatobiliary tract. However, it is technically complex and has a relatively high complication rate. In particular, cannulation of the papillary ostium remains challenging. The aim of this study is to examine whether a deep-learning algorithm can be used to detect the major duodenal papilla and in particular the papillary ostium reliably and could therefore be a valuable tool for inexperienced endoscopists, particularly in training situation. Methods We analyzed a total of 654 retrospectively collected images of 85 patients. Both the major duodenal papilla and the ostium were then segmented. Afterwards, a neural network was trained using a deep-learning algorithm. A 5-fold cross-validation was performed. Subsequently, we ran the algorithm on 5 prospectively collected videos of ERCPs. Results 5-fold cross-validation on the 654 labeled data resulted in an F1 value of 0.8007, a sensitivity of 0.8409 and a specificity of 0.9757 for the class papilla, and an F1 value of 0.5724, a sensitivity of 0.5456 and a specificity of 0.9966 for the class ostium. Regardless of the class, the average F1 value (class papilla and class ostium) was 0.6866, the sensitivity 0.6933 and the specificity 0.9861. In 100\% of cases the AI-detected localization of the papillary ostium in the prospectively collected videos corresponded to the localization of the cannulation performed by the endoscopist. Conclusions In the present study, the neural network was able to identify the major duodenal papilla with a high sensitivity and high specificity. In detecting the papillary ostium, the sensitivity was notably lower. However, when used on videos, the AI was able to identify the location of the subsequent cannulation with 100\% accuracy. In the future, the neural network will be trained with more data. Thus, a suitable tool for ERCP could be established, especially in the training situation.}, language = {en} } @misc{RoserMeinikheimMendeletal., author = {Roser, D. A. and Meinikheim, Michael and Mendel, Robert and Palm, Christoph and Probst, Andreas and Muzalyova, A. and Scheppach, Markus W. and Nagl, S. and Schnoy, Elisabeth and R{\"o}mmele, Christoph and Schulz, D. and Schlottmann, Jakob and Prinz, Friederike and Rauber, David and R{\"u}ckert, Tobias and Matsumura, T. and Fernandez-Esparrach, G. and Parsa, N. and Byrne, M. and Messmann, Helmut and Ebigbo, Alanna}, title = {Human-Computer Interaction: Impact of Artificial Intelligence on the diagnostic confidence of endoscopists assessing videos of Barrett's esophagus}, series = {Endoscopy}, volume = {56}, journal = {Endoscopy}, number = {S 02}, publisher = {Georg Thieme Verlag}, issn = {1438-8812}, doi = {10.1055/s-0044-1782859}, pages = {79}, abstract = {Aims Human-computer interactions (HCI) may have a relevant impact on the performance of Artificial Intelligence (AI). Studies show that although endoscopists assessing Barrett's esophagus (BE) with AI improve their performance significantly, they do not achieve the level of the stand-alone performance of AI. One aspect of HCI is the impact of AI on the degree of certainty and confidence displayed by the endoscopist. Indirectly, diagnostic confidence when using AI may be linked to trust and acceptance of AI. In a BE video study, we aimed to understand the impact of AI on the diagnostic confidence of endoscopists and the possible correlation with diagnostic performance. Methods 22 endoscopists from 12 centers with varying levels of BE experience reviewed ninety-six standardized endoscopy videos. Endoscopists were categorized into experts and non-experts and randomly assigned to assess the videos with and without AI. Participants were randomized in two arms: Arm A assessed videos first without AI and then with AI, while Arm B assessed videos in the opposite order. Evaluators were tasked with identifying BE-related neoplasia and rating their confidence with and without AI on a scale from 0 to 9. Results The utilization of AI in Arm A (without AI first, with AI second) significantly elevated confidence levels for experts and non-experts (7.1 to 8.0 and 6.1 to 6.6, respectively). Only non-experts benefitted from AI with a significant increase in accuracy (68.6\% to 75.5\%). Interestingly, while the confidence levels of experts without AI were higher than those of non-experts with AI, there was no significant difference in accuracy between these two groups (71.3\% vs. 75.5\%). In Arm B (with AI first, without AI second), experts and non-experts experienced a significant reduction in confidence (7.6 to 7.1 and 6.4 to 6.2, respectively), while maintaining consistent accuracy levels (71.8\% to 71.8\% and 67.5\% to 67.1\%, respectively). Conclusions AI significantly enhanced confidence levels for both expert and non-expert endoscopists. Endoscopists felt significantly more uncertain in their assessments without AI. Furthermore, experts with or without AI consistently displayed higher confidence levels than non-experts with AI, irrespective of comparable outcomes. These findings underscore the possible role of AI in improving diagnostic confidence during endoscopic assessment.}, language = {en} } @misc{Hipp, author = {Hipp, Susanne}, title = {Small antennas in huge environments - simulation using hybrid methods}, series = {Workshop des ITG-Fachausschuss HF1: Innovative Beamforming Antennas}, journal = {Workshop des ITG-Fachausschuss HF1: Innovative Beamforming Antennas}, language = {en} } @misc{ChowWhiteBruceetal., author = {Chow, Rosan and White, Hazel and Bruce, Fraser and Press, Michael}, title = {Rip+Mix: A Design Method Emerging from Case Transfer}, editor = {Lalande, Philippe}, publisher = {University of Dundee}, address = {Montreal}, language = {en} } @misc{Dendorfer, author = {Dendorfer, Sebastian}, title = {Patientenindividuelle biomechanisch optimierte Rehabilitation}, series = {Prim{\"a}r- und Revisionsendoprothetik des Kniegelenks. Trends und zuk{\"u}nftige Herausforderungen. Regensburg 2023}, journal = {Prim{\"a}r- und Revisionsendoprothetik des Kniegelenks. Trends und zuk{\"u}nftige Herausforderungen. Regensburg 2023}, language = {de} } @misc{BielmeierMauerer, author = {Bielmeier, Benno and Mauerer, Wolfgang}, title = {Semi-Formal Verification of Embedded Linux Systems Using Trace-Based Models}, series = {Semi-Formal Verification of Embedded Linux Systems Using Trace-Based Models}, journal = {Semi-Formal Verification of Embedded Linux Systems Using Trace-Based Models}, language = {en} } @misc{BraunWilson, author = {Braun, Carina N. and Wilson, Edith. E.}, title = {Professor:innen an HAW - Berufswahl zwischen Lehre, Forschung \& Transfer}, series = {Strategische Entwicklung von Hochschulen f{\"u}r Angewandte Wissenschaften Zur Bedeutung von Forschung \& Transfer, 28-29. September 2023, HIS-HE, Hannover}, journal = {Strategische Entwicklung von Hochschulen f{\"u}r Angewandte Wissenschaften Zur Bedeutung von Forschung \& Transfer, 28-29. September 2023, HIS-HE, Hannover}, abstract = {Professor:innen an HAW - Berufswahl zwischen Lehre, Forschung \& Transfer richtet sich ausgehend von dem Forschungsprojekt ZAP.Karrierewege (Teilprojekt von ZAP.OTHR) darauf, Herausforderungen der Personalgewinnung zu untersuchen und diese mit konkreten Handlungsempfehlungen zu verbinden.}, language = {de} } @misc{ScheppachMendelProbstetal., author = {Scheppach, Markus W. and Mendel, Robert and Probst, Andreas and Nagl, Sandra and Meinikheim, Michael and Yip, Hon Chi and Lau, Louis Ho Shing and Chiu, Philip Wai Yan and Palm, Christoph and Messmann, Helmut and Ebigbo, Alanna}, title = {Effekt eines K{\"u}nstliche Intelligenz (KI) - Algorithmus auf die Gef{\"a}ßdetektion bei third space Endoskopien}, series = {Zeitschrift f{\"u}r Gastroenterologie}, volume = {61}, journal = {Zeitschrift f{\"u}r Gastroenterologie}, number = {08}, publisher = {Thieme}, address = {Stuttgart}, doi = {10.1055/s-0043-1771980}, pages = {e528-e529}, abstract = {Einleitung Third space Endoskopieprozeduren wie die endoskopische Submukosadissektion (ESD) und die perorale endoskopische Myotomie (POEM) sind technisch anspruchsvoll und gehen mit untersucherabh{\"a}ngigen Komplikationen wie Blutungen und Perforationen einher. Grund hierf{\"u}r ist die unabsichtliche Durchschneidung von submukosalen Blutgef{\"a}ßen ohne pr{\"a}emptive Koagulation. Ziele Die Forschungsfrage, ob ein KI-Algorithmus die intraprozedurale Gef{\"a}ßerkennung bei ESD und POEM unterst{\"u}tzen und damit Komplikationen wie Blutungen verhindern k{\"o}nnte, erscheint in Anbetracht des erfolgreichen Einsatzes von KI bei der Erkennung von Kolonpolypen interessant. Methoden Auf 5470 Einzelbildern von 59 third space Endoscopievideos wurden submukosale Blutgef{\"a}ße annotiert. Zusammen mit weiteren 179.681 nicht-annotierten Bildern wurde ein DeepLabv3+neuronales Netzwerk mit dem ECMT-Verfahren f{\"u}r semi-supervised learning trainiert, um Blutgef{\"a}ße in Echtzeit erkennen zu k{\"o}nnen. F{\"u}r die Evaluation wurde ein Videotest mit 101 Videoclips aus 15 vom Trainingsdatensatz separaten Prozeduren mit 200 vordefinierten Gef{\"a}ßen erstellt. Die Gef{\"a}ßdetektionsrate, -zeit und -dauer, definiert als der Prozentsatz an Einzelbildern eines Videos bezogen auf den Goldstandard, auf denen ein definiertes Gef{\"a}ß erkannt wurde, wurden erhoben. Acht erfahrene Endoskopiker wurden mithilfe dieses Videotests im Hinblick auf Gef{\"a}ßdetektion getestet, wobei eine H{\"a}lfte der Videos nativ, die andere H{\"a}lfte nach Markierung durch den KI-Algorithmus angesehen wurde. Ergebnisse Der mittlere Dice Score des Algorithmus f{\"u}r Blutgef{\"a}ße war 68\%. Die mittlere Gef{\"a}ßdetektionsrate im Videotest lag bei 94\% (96\% f{\"u}r ESD; 74\% f{\"u}r POEM). Die mediane Gef{\"a}ßdetektionszeit des Algorithmus lag bei 0,32 Sekunden (0,3 Sekunden f{\"u}r ESD; 0,62 Sekunden f{\"u}r POEM). Die mittlere Gef{\"a}ßdetektionsdauer lag bei 59,1\% (60,6\% f{\"u}r ESD; 44,8\% f{\"u}r POEM) des Goldstandards. Alle Endoskopiker hatten mit KI-Unterst{\"u}tzung eine h{\"o}here Gef{\"a}ßdetektionsrate als ohne KI. Die mittlere Gef{\"a}ßdetektionsrate ohne KI lag bei 56,4\%, mit KI bei 71,2\% (p<0.001). Schlussfolgerung KI-Unterst{\"u}tzung war mit einer statistisch signifikant h{\"o}heren Gef{\"a}ßdetektionsrate vergesellschaftet. Die mediane Gef{\"a}ßdetektionszeit von deutlich unter einer Sekunde sowie eine Gef{\"a}ßdetektionsdauer von gr{\"o}ßer 50\% des Goldstandards wurden f{\"u}r den klinischen Einsatz als ausreichend erachtet. In prospektiven Anwendungsstudien sollte der KI-Algorithmus auf klinische Relevanz getestet werden.}, language = {de} } @misc{ZellmerRauberProbstetal., author = {Zellmer, Stephan and Rauber, David and Probst, Andreas and Weber, Tobias and Nagl, Sandra and R{\"o}mmele, Christoph and Schnoy, Elisabeth and Palm, Christoph and Messmann, Helmut and Ebigbo, Alanna}, title = {Verwendung k{\"u}nstlicher Intelligenz bei der Detektion der Papilla duodeni major}, series = {Zeitschrift f{\"u}r Gastroenterologie}, volume = {61}, journal = {Zeitschrift f{\"u}r Gastroenterologie}, number = {08}, publisher = {Thieme}, address = {Stuttgart}, doi = {10.1055/s-0043-1772000}, pages = {e593-e540}, abstract = {Einleitung Die Endoskopische Retrograde Cholangiopankreatikographie (ERCP) ist der Goldstandard in der Diagnostik und Therapie von Erkrankungen des pankreatobili{\"a}ren Trakts. Jedoch ist sie technisch sehr anspruchsvoll und weist eine vergleichsweise hohe Komplikationsrate auf. Ziele In der vorliegenden Machbarkeitsstudie soll gepr{\"u}ft werden, ob mithilfe eines Deep-learning-Algorithmus die Papille und das Ostium zuverl{\"a}ssig detektiert werden k{\"o}nnen und somit f{\"u}r Endoskopiker mit geringer Erfahrung ein geeignetes Hilfsmittel, insbesondere f{\"u}r die Ausbildungssituation, darstellen k{\"o}nnten. Methodik Wir betrachteten insgesamt 606 Bilddatens{\"a}tze von 65 Patienten. In diesen wurde sowohl die Papilla duodeni major als auch das Ostium segmentiert. Anschließend wurde eine neuronales Netz mittels eines Deep-learning-Algorithmus trainiert. Außerdem erfolgte eine 5-fache Kreuzvaldierung. Ergebnisse Bei einer 5-fachen Kreuzvaldierung auf den 606 gelabelten Daten konnte f{\"u}r die Klasse Papille eine F1-Wert von 0,7908, eine Sensitivit{\"a}t von 0,7943 und eine Spezifit{\"a}t von 0,9785 erreicht werden, f{\"u}r die Klasse Ostium eine F1-Wert von 0,5538, eine Sensitivit{\"a}t von 0,5094 und eine Spezifit{\"a}t von 0,9970 (vgl. [Tab. 1]). Unabh{\"a}ngig von der Klasse zeigte sich gemittelt (Klasse Papille und Klasse Ostium) ein F1-Wert von 0,6673, eine Sensitivit{\"a}t von 0,6519 und eine Spezifit{\"a}t von 0,9877 (vgl. [Tab. 2]). Schlussfolgerung In vorliegende Machbarkeitsstudie konnte das neuronale Netz die Papilla duodeni major mit einer hohen Sensitivit{\"a}t und sehr hohen Spezifit{\"a}t identifizieren. Bei der Detektion des Ostiums war die Sensitivit{\"a}t deutlich geringer. Zuk{\"u}nftig soll das das neuronale Netz mit mehr Daten trainiert werden. Außerdem ist geplant, den Algorithmus auch auf Videos anzuwenden. Somit k{\"o}nnte langfristig ein geeignetes Hilfsmittel f{\"u}r die ERCP etabliert werden.}, language = {de} } @misc{LehrerStockerDuddecketal., author = {Lehrer, Tobias and Stocker, Philipp and Duddeck, Fabian and Wagner, Marcus}, title = {Comparison of Low- vs. High-Dimensional Machine Learning Approaches for Sheet Metal Drawability Assessment}, series = {Third International Conference on Computational Science and AI in Industry (CSAI 2023), Trondheim, Norway, 28-30 August 2023}, journal = {Third International Conference on Computational Science and AI in Industry (CSAI 2023), Trondheim, Norway, 28-30 August 2023}, publisher = {International Centre for Numerical Methods in Engineering}, doi = {10.35096/othr/pub-6477}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-64772}, abstract = {Developing new deep-drawn sheet metal parts is a challenging task due to conflicting demands for low costs, durability, and crash properties. Ensuring manufacturability throughout geometrical changes adds to the complexity, leading engineers to rely on experience-driven iterative design changes that compromise requirements and lack reproducibility. Finite Element (FE) simulation models are employed to ensure manufacturability, albeit at the expense of high computational costs and delays in part development. To improve efficiency, a Machine learning (ML)-centered approach was proposed to ensure manufacturability. However, the limited availability of data raises uncertainty about whether a low- or high-dimensional ML approach is most suitable for drawability assessment. This work compares the accuracy of a low-dimensional, feature-based Linear Support Vector surrogate and an adapted high-dimensional PointNet model under different dataset sizes. The dataset is composed of parametrically generated, U-shaped structural sheet metal parts. We use a one-step simulation scheme and evaluate results with a Forming Limit Diagram (FLD) to label drawability. Results show the point of transition to be at about 500 training samples, from which onwards Deep learning is advantageous. Moreover, the generalizability of these models is tested on a second dataset with topologically similar components. This is to assess the potential for a geometrically more comprehensive evaluation. We discuss several influences on model performances and outline future potentials.}, language = {en} } @misc{MeinikheimMendelProbstetal., author = {Meinikheim, Michael and Mendel, Robert and Probst, Andreas and Scheppach, Markus W. and Nagl, Sandra and Schnoy, Elisabeth and R{\"o}mmele, Christoph and Prinz, Friederike and Schlottmann, Jakob and Messmann, Helmut and Palm, Christoph and Ebigbo, Alanna}, title = {Einfluss von K{\"u}nstlicher Intelligenz auf die Performance von niedergelassenen Gastroenterolog:innen bei der Beurteilung von Barrett-{\"O}sophagus}, series = {Zeitschrift f{\"u}r Gastroenterologie}, volume = {61}, journal = {Zeitschrift f{\"u}r Gastroenterologie}, number = {8}, publisher = {Thieme}, address = {Stuttgart}, doi = {10.1055/s-0043-1771711}, abstract = {Einleitung Die Differenzierung zwischen nicht dysplastischem Barrett-{\"O}sophagus (NDBE) und mit Barrett-{\"O}sophagus assoziierten Neoplasien (BERN) w{\"a}hrend der endoskopischen Inspektion erfordert viel Expertise. Die fr{\"u}he Diagnosestellung ist wichtig f{\"u}r die weitere Prognose des Barrett-Karzinoms. In Deutschland werden Patient:innen mit einem Barrett-{\"O}sophagus (BE) in der Regel im niedergelassenen Sektor {\"u}berwacht. Ziele Ziel ist es, den Einfluss von einem auf K{\"u}nstlicher Intelligenz (KI) basierenden klinischen Entscheidungsunterst{\"u}tzungssystems (CDSS) auf die Performance von niedergelassenen Gastroenterolog:innen (NG) bei der Evaluation von Barrett-{\"O}sophagus (BE) zu untersuchen. Methodik Es erfolgte die prospektive Sammlung von 96 unver{\"a}nderten hochaufl{\"o}senden Videos mit F{\"a}llen von Patient:innen mit histologisch best{\"a}tigtem NDBE und BERN. Alle eingeschlossenen F{\"a}lle enthielten mindestens zwei der folgenden Darstellungsmethoden: HD-Weißlichtendoskopie, Narrow Band Imaging oder Texture and Color Enhancement Imaging. Sechs NG von sechs unterschiedlichen Praxen wurden als Proband:innen eingeschlossen. Es erfolgte eine permutierte Block-Randomisierung der Videof{\"a}lle in entweder Gruppe A oder Gruppe B. Gruppe A implizierte eine Evaluation des Falls durch Proband:innen zun{\"a}chst ohne KI und anschließend mit KI als CDSS. In Gruppe B erfolgte die Evaluation in umgekehrter Reihenfolge. Anschließend erfolgte eine zuf{\"a}llige Wiedergabe der so entstandenen Subgruppen im Rahmen des Tests. Ergebnis In diesem Test konnte ein von uns entwickeltes KI-System (Barrett-Ampel) eine Sensitivit{\"a}t von 92,2\%, eine Spezifit{\"a}t von 68,9\% und eine Accuracy von 81,3\% erreichen. Mit der Hilfe von KI verbesserte sich die Sensitivit{\"a}t der NG von 64,1\% auf 71,2\% (p<0,001) und die Accuracy von 66,3\% auf 70,8\% (p=0,006) signifikant. Eine signifikante Verbesserung dieser Parameter zeigte sich ebenfalls, wenn die Proband:innen die F{\"a}lle zun{\"a}chst ohne KI evaluierten (Gruppe A). Wurde der Fall jedoch als Erstes mit der Hilfe von KI evaluiert (Gruppe B), blieb die Performance nahezu konstant. Schlussfolgerung Es konnte ein performantes KI-System zur Evaluation von BE entwickelt werden. NG verbessern sich bei der Evaluation von BE durch den Einsatz von KI.}, language = {de} } @misc{GradHahnNosteretal., author = {Grad, Marius and Hahn, Konstantin and Noster, Ulf and Spieß, Lothar}, title = {Increasing the lifetime of titanium implants through a diffusion-controlled surface treatment}, series = {Th{\"u}ringer Werkstofftag 2023, 14. M{\"a}rz 2023, ILmenau}, journal = {Th{\"u}ringer Werkstofftag 2023, 14. M{\"a}rz 2023, ILmenau}, doi = {10.13140/RG.2.2.35933.59360}, 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{PaetzelSchlesingerPricopetal., author = {Paetzel, Maike and Schlesinger, Philipp and Pricop, Mircea and Comaneci, Radu and Baumann, Timo and Menzel, Wolfgang}, title = {Inkrementelle und zustandsbasierte Verarbeitung in einem hybriden Sprachdialogsystem}, language = {en} } @misc{ScheppachRauberStallhoferetal., author = {Scheppach, Markus 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} } @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} } @misc{ScheppachMendelProbstetal., author = {Scheppach, Markus W. and Mendel, Robert and Probst, Andreas and Rauber, David and Rueckert, 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} } @misc{BirkenmaierKrenkelLehle, author = {Birkenmaier, Clemens and Krenkel, Lars and Lehle, Karla}, title = {Linking flow conditions in membrane oxygenators to arrangements of multimeric von-Willebrand-factor as indication for coagulation}, series = {World Congress of Biomechanics 2018, Convention Centre Dublin, 8.-12. Juli 2018}, journal = {World Congress of Biomechanics 2018, Convention Centre Dublin, 8.-12. Juli 2018}, abstract = {Introduction Shear induced multimerisation of von-Willebrand-factor (vWF) is supposed to play an important role in coagulation inside extracorporeal membrane oxygenators. However, there is no proof that links observed vWF structures to computed or measured flow conditions. Methods The structures of multimeric vWF fibers, observed in clinically used membrane oxygenators is examined using immunofluorescence microscopy (IFM) using Carstairs' staining method (positive ethics committee vote). The flow around the membrane fibres inside the oxygenator is investigated in terms of shear rate, wall shear velocity and streamlines by using CFD (RANS, Carreau-Yasuda viscosity, geometry remodelled after high-resolution µCT-scans). By interpreting the histological and numerical results in this common context, indications for shear induced coagulation mechanisms can be identified. Results The fibre structures of multimeric vWF build regular but not exactly symmetric formations around the contact face (CF) between the crosswise stacked oxygenator fibres (OF), see fig.1B, vWF marked red. Annular around the CF arranged, cells are likely to be found, see fig.1B, nuclei marked blue. The computed streamlines around the OF show attached flow around the circular fibres. However, the irregular arrangement of real OF produce considerable cross flow between the interconnected neighbouring channels, in contrast to previous 2D-simulations. Thus, the CF are washed around closely by blood, also from neighbouring channels. The wall shear velocity streamlines form regular, slightly asymmetric shapes around the contact faces. The occurring maximum shear rates are in the range of 1,000 1/s. Discussion The shapes of vWF structures found in clinically used oxygenators match the computational results in terms of wall shear velocity and streamlines well. The accumulation of cells close to the CF can also be explained by fluid mechanics, as there are small shear gradients and slow velocities. However, occurring shear rates between OFs are too low to trigger multimerisation of vWF. That raises the question where in the circuit the actual activation of vWF is started and how, at least partly chained, vWF multimeres are attracted towards the OF surface. A next step will be the investigation of the actual shear rate triggered (or mediated) multimerisation of vWF. Towards this end, microfluidic experiments with shear triggered coagulation will be performed. Also of big interest is the computation of the flow situation in the oxygenator in proximity to chaining threads, which have been ignored in computations so far. However, first a realistic representation of the effective viscosity in computations is needed, which is not available yet.}, language = {en} } @misc{WildGalkaWappleretal., author = {Wild, Tobias and Galka, Stefan and Wappler, Mona and Xing, Lu}, title = {Integrating supply chain aspects into factory planning, a simulation approach}, series = {The journey to sustainable supply chains: proceedings of the 26th International Symposium on Logistics (ISL 2022), 10-13th July 2022, Cork, Ireland}, journal = {The journey to sustainable supply chains: proceedings of the 26th International Symposium on Logistics (ISL 2022), 10-13th July 2022, Cork, Ireland}, editor = {Potter, A. and Pawar, K. S. and O'Reilly, S. and Jimo, A.}, publisher = {Nottingham University Business School, UK}, address = {Nottingham, UK}, isbn = {978-0-85358-350-9}, pages = {6}, abstract = {One major goal of green-field factory planning is to decide on space requirements in the plant. In this phase, detailed information about the supply chain network (e.g. which suppliers deliver which parts) is often unavailable. Nevertheless, typical decisions in supply chain management, like the definition of replenishment processes and quantities or make-or-buy decisions, impact space requirements in the factory and should therefore be considered in the factory planning. This research article proposes a simulation approach for a factory simulation in which raw material replenishment is integrated to evaluate the space requirements for raw materials.}, language = {de} } @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{TauwaldKrenkel, author = {Tauwald, Sandra Melina and Krenkel, Lars}, title = {Elementary experimental setup for flow visualization in upper human respiratory tract}, series = {25th Congress of the European Society of Biomechanics, July 7-10, 2019, Vienna, Austria}, journal = {25th Congress of the European Society of Biomechanics, July 7-10, 2019, Vienna, Austria}, language = {en} } @misc{BirkenmaierDorniaLehleetal., author = {Birkenmaier, Clemens and Dornia, Christian and Lehle, Karla and Krenkel, Lars}, title = {Feasibility of detecting thrombotic deposits in membrane oxygenators using micro computed tomography}, series = {25th Congress of the European Society of Biomechanics, July 7-10, 2019, Vienna, Austria}, journal = {25th Congress of the European Society of Biomechanics, July 7-10, 2019, Vienna, Austria}, language = {en} } @misc{FriedrichRivoireScholzetal., author = {Friedrich, Janet and Rivoire, Julien and Scholz, Alexander and Wiegbert, and Terekov, Maxim and Kbrich, Rainer and Krenkel, Lars and Wagner, Claus and Schreiber, Laura Maria}, title = {Exploration of Gas Flow During High Frequency Oscillated Ventilation by 19F-Gas-MRI}, series = {Proceedings of the International Society for Magnetic Resonance in Medicine}, volume = {18}, journal = {Proceedings of the International Society for Magnetic Resonance in Medicine}, abstract = {To detect convective gas flow inside the large airways during high frequency oscillated ventilation (HFOV) the fluorinated contrast gas Heptafluoropropane was used for 19F-MRI. In a first study the comparison between constant flow measurements and Computational Fluid Dynamics (CFD) simulations provided a good agreement. In a following experiment oscillated flow was applied to a lung phantom consisting of ventilation bag and long pipe. The pressure wave inside the pipe was explored point-by-point and corresponding velocities were determined. With these experiments it could be shown for the first time that flow measurement during HFOV using fluorinated contrast gas is feasible.}, 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} } @misc{KrenkelMichelKeiletal., author = {Krenkel, Lars and Michel, Johanna and Keil, Niklas and Daschner, Jan}, title = {Experimental Investigation of Logitudinal Folds in Endotracheal Tube Cuffs and their Correlation to Silent Breathing}, 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 = {Air leakage past High-Volume-Low-Pressure (HVLP) endotracheal tube (ETT) cuffs creates a potential infection risk for health care professionals during ventilation of patients suffering from contagious airborne diseases. However, unlike silent aspiration, a phenomenon where fluids enter the airways of intubated patients, the aspect of aerosol emergence through cuff folds -what we called accordingly "silent breathing" (SB)- has not been investigated in detail so far. This study investigates air leakage past HVLP cuffs with varying cuff pressures under realistic artificial breathing scenarios experimentally and in addition numerically. The focus was laid on the parametric investigation of the occurrence and furthermore on different influencing factors of silent breathing. The morphology of the folds responsible for the leakage was captured using high-resolution 3D microcomputed tomography (μCT). For the numerical investigations (Com-putational Fluid Dynamics - CFD), the commercial CFD Software package FLUENT 2021 R2 (ANSYS, Inc., Canonsburg, PA, US), as well as the DLR in-house research code THETA has been used.}, 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} } @misc{BirkenmaierKrenkel, author = {Birkenmaier, Clemens and Krenkel, Lars}, title = {Towards a realistic model of blood viscosity and coagulation in membrane oxygenators}, series = {6th European Conference on Computational Mechanics (Solids, Structures and Coupled Problems) - ECCM 6; 7th European Conference on Computational Fluid Dynamics - ECFD 7 : Glasgow, Scotland, UK, June 11-15, 2018}, journal = {6th European Conference on Computational Mechanics (Solids, Structures and Coupled Problems) - ECCM 6; 7th European Conference on Computational Fluid Dynamics - ECFD 7 : Glasgow, Scotland, UK, June 11-15, 2018}, abstract = {Modelling blood flow an shear induced coagulation in membraene oxygenators (MO) is challenging. The relevant geometry of oxygenator fibers (OF) and chaining threads is complex and spans several length scales. In relevant scales and regimes blood shows several significant non-Newtonian effects. Existing models are only capable of accounting for some, but not all relevant effects. Additionally, coagulation processes are influencing fluid properties and geometry significantly. Due to the enormous size of the discretised geometries highly detailed viscosity and coagulation properties of blodd flow in MOs. First step is to find a gemoetry dependent viscosity representation on basis of parametric micro channel experiments with anti-coagulated blood. Next step is a statistic coagulation model, based on micro channel experiments with human (re-calcified citrated) whole blood an evaluation of clinically used osygenators. Since shear rate dependent (i.e. viscosity dependet) coagulation in return influences the viscosity, a combined model with suitable implementation in a RANS framework is necessary. Towards this end, micro channel experiments with new and used single OFs triggering coagulation are performed. Structures of multimeric von Willebrand fibers (vWF), as indicator for shear induced coagulation, are compared to computed and measured flow conditions, using immunofluorescence microscopy, RANS-computations and µPIV, respectively. Preliminary examinations in clinically used MOs show good agreement between occurring structures of vWF, cell depositions and computed flow patterns (geometry form µCT-Scans). However, computed shear rates might be to low to actually trigger activation of vWF. The complex geometry of MOs results in huge meshes, which makes RANS with statistical modelling of viscosity and coagulation a reasonable approach. Towards this end, experimental data on micro channel level with evaluation on real application level is crucial. Especially regarding clotting processes, micro fluidic experiments are powerful research tool.}, language = {en} } @misc{MauererRamsauerKoelbl, author = {Mauerer, Wolfgang and Ramsauer, Ralf and K{\"o}lbl, Andreas}, title = {The Many Approaches to Real-Time and Safety-Critical Linux}, series = {Open Source Summit Japan 2017}, journal = {Open Source Summit Japan 2017}, language = {en} } @misc{SchoenbergerScherzingerMauerer, author = {Sch{\"o}nberger, Manuel and Scherzinger, Stefanie and Mauerer, Wolfgang}, title = {Quantum Computing for DB - Applicability on Multi Query Optimization and Join Order Optimization}, series = {Fr{\"u}hjahrstreffen Fachgruppe Datenbanken in Potsdam, 2022}, journal = {Fr{\"u}hjahrstreffen Fachgruppe Datenbanken in Potsdam, 2022}, language = {en} } @misc{MintelRamsauerLohmannetal., author = {Mintel, Mario and Ramsauer, Ralf and Lohmann, Daniel and Scherzinger, Stefanie and Mauerer, Wolfgang}, title = {Fork {\`a} la carte f{\"u}r In-Memory-Datenbanken}, series = {Fr{\"u}hjahrstreffen der Fachgruppen Betriebssysteme, Hamburg, 17. M{\"a}rz 2022}, journal = {Fr{\"u}hjahrstreffen der Fachgruppen Betriebssysteme, Hamburg, 17. M{\"a}rz 2022}, language = {de} } @misc{Frikel, author = {Frikel, J{\"u}rgen}, title = {Adapted curvelet sparse regularization in limited angle tomography}, series = {Summer School: New Trends and Directions in Harmonic Analysis, Fractional Operator Theory, and Image Analysis, Inzell, Germany, September 17 - 21, 2012}, journal = {Summer School: New Trends and Directions in Harmonic Analysis, Fractional Operator Theory, and Image Analysis, Inzell, Germany, September 17 - 21, 2012}, language = {en} } @misc{SindersbergerDiermeierMonkman, author = {Sindersberger, Dirk and Diermeier, Andreas and Monkman, Gareth J.}, title = {Investigation of the electrical properties of magneto-active polymers - Poster}, series = {DFG-Tagung, Benedikbeuern, September 2016}, journal = {DFG-Tagung, Benedikbeuern, September 2016}, language = {en} } @misc{MonkmanSindersberger, author = {Monkman, Gareth J. and Sindersberger, Dirk}, title = {Investigation of the electrical properties of magneto-active polymers}, series = {DFG-Tagung, Benedikbeuern, September 2015}, journal = {DFG-Tagung, Benedikbeuern, September 2015}, language = {en} } @misc{FuechtmeierNerlichMonkmanetal., author = {F{\"u}chtmeier, Bernd and Nerlich, M. and Monkman, Gareth J. and Egersd{\"o}rfer, Stefan and Raja, Kashif}, title = {Development of a Robotic Navigation and Fracture Fixation System - 7th Intl. Conf. Medical Aspects of Telemedicine, 23 September 2002}, series = {European Journal of Medical Research; Abstracts}, journal = {European Journal of Medical Research; Abstracts}, publisher = {Biomed Central}, address = {London}, issn = {2047-783X}, pages = {28 -- 29}, language = {en} } @misc{FuechtmeierEgersdoerferMonkmanetal., author = {F{\"u}chtmeier, Bernd and Egersd{\"o}rfer, Stefan and Monkman, Gareth J. and Nerlich, M.}, title = {Entwicklung eines Systems zur navigierten und robotergest{\"u}tzten Frakturfixation}, series = {Zweite Jahrestagung der Deutschen Gesellschaft f{\"u}r Computer- und Roboterassistierte Chirurgie : 4.-7. November 2003, N{\"u}rnberg, Germany; CURAC 2003; Abstract CD-ROM}, journal = {Zweite Jahrestagung der Deutschen Gesellschaft f{\"u}r Computer- und Roboterassistierte Chirurgie : 4.-7. November 2003, N{\"u}rnberg, Germany; CURAC 2003; Abstract CD-ROM}, address = {Erlangen-N{\"u}rnberg}, language = {de} } @misc{FrikelGoeppelHaltmeier, author = {Frikel, J{\"u}rgen and G{\"o}ppel, Simon and Haltmeier, Markus}, title = {Combining reconstruction and edge detection in CT}, series = {BVM Workshop 2023, 02.-04. Juli 2023, Braunschweig}, journal = {BVM Workshop 2023, 02.-04. Juli 2023, Braunschweig}, abstract = {We present two methods that combine image reconstruction and edge detection in computed tomography (CT) scans. Our first method is as an extension of the prominent filtered backprojection algorithm. In our second method we employ ℓ1-regularization for stable calculation of the gradient. As opposed to the first method, we show that this approach is able to compensate for undersampled CT data.}, language = {en} } @misc{GreenleeAnstisRosengarthetal., author = {Greenlee, Mark W. and Anstis, Stuart and Rosengarth, Katharina and Goldhacker, Markus and Brandl-R{\"u}hle, Sabine and Plank, Tina}, title = {Neural correlates of perceptual filling-in: fMRI evidence in the foveal projection zone of patients with central scotoma}, series = {Journal of Vision / Vision Sciences Society Annual Meeting Abstract}, volume = {12}, journal = {Journal of Vision / Vision Sciences Society Annual Meeting Abstract}, number = {9}, publisher = {ARVO}, issn = {1534-7362}, doi = {10.1167/12.9.1303}, abstract = {Patients with juvenile retinal dystrophy often report that they are unaware of their central scotoma, suggesting the presence of perceptual filling-in. We used functional Magnetic Resonance Imaging (fMRI) to determine possible neural correlates of perceptual filling-in in patients with retinal distrophy and clinically established central scotoma in both eyes. The data of 5 patients (Stargardt disease, cone-rod dystrophy; mean age 45 yrs; scotoma diameter 10-20°) and of 5 normally sighted controls were analyzed. Fixation behaviour and perimetry were measured with a Nidek microperimeter. Magnetic resonance imaging was performed using a Siemens 3T Allegra scanner. We stimulated the central visual field (30 deg) with a vertically oriented, low spatial frequency (1 c/deg) high-contrast sinewave grating that was either a) continuous, or b) was interrupted by a central grey disk. The disk was either slightly larger than the scotoma (detectable on 75\% of trials) or slightly smaller (detectable on 25\% of trials). To control for attention, an eccentric fixation task was performed during scanning. Data were analyzed using SPM8 (GLM with ROI analysis to obtain percent signal change for foveal projection zone). Results: for all patients, the BOLD signal in the foveal projection area was significantly higher for the small disk (i.e., condition leading to complete filling-in) than for the large disk (i.e., no filling-in). This effect was absent in the control subjects. Our findings support the existence of an active neural process that leads to filling-in in patients with central visual field scotomata.}, language = {en} } @misc{Schliermann, author = {Schliermann, Rainer}, title = {Analyse von potenziell leistungsrelevanten psychologischen Variablen bei Basketballerinnen und Basketballern mit versus ohne k{\"o}rperliche Beeintr{\"a}chtigung}, series = {Kreativit{\"a}t - Innovation - Leistung : Wissenschaft bewegt SPORT bewegt Wissenschaft ; 20. dvs-Hochschultag, Halle, 21. - 23. September 2011 ; Abstracts}, journal = {Kreativit{\"a}t - Innovation - Leistung : Wissenschaft bewegt SPORT bewegt Wissenschaft ; 20. dvs-Hochschultag, Halle, 21. - 23. September 2011 ; Abstracts}, editor = {Hottenrott, Kuno and Stoll, Oliver and Wollny, Rainer}, publisher = {Czwalina}, address = {Hamburg}, isbn = {9783880205710}, pages = {48}, language = {de} } @misc{HecknerKesslerEgersdoerferetal., author = {Heckner, T. and Kessler, C. and Egersd{\"o}rfer, Stefan and Monkman, Gareth J.}, title = {Computer based platform for tactile actuator analysis}, series = {Actuator 2006 : 10th International Conference on New Actuators and 4th International Exhibition on Smart Actuators and Drive Systems, Bremen, Germany, 14 - 16 June 2006}, journal = {Actuator 2006 : 10th International Conference on New Actuators and 4th International Exhibition on Smart Actuators and Drive Systems, Bremen, Germany, 14 - 16 June 2006}, publisher = {HVG Hanseatische Veranst.-GmbH}, language = {en} } @misc{SchliermannHeydenreichBungteretal., author = {Schliermann, Rainer and Heydenreich, Patrick and Bungter, Tanja and Anneken, Volker}, title = {Analysis of motivational factors and physical activity and sport of people with visual impairments in vocational promotion centers [Abstract]}, series = {Proceedings 14th European Congress of Sport Psychology : Sport Psychology - Theories and Applications for Performance, Health and Humanity ; 14-19 July 2015, Bern, Switzerland}, journal = {Proceedings 14th European Congress of Sport Psychology : Sport Psychology - Theories and Applications for Performance, Health and Humanity ; 14-19 July 2015, Bern, Switzerland}, editor = {Schmid, Oliver and Seiler, Roland}, publisher = {University of Bern, Institut of Sport Science}, address = {Bern}, isbn = {978-3-033-05129-4}, pages = {230}, language = {en} } @misc{SchliermannScheuerHeydenreichetal., author = {Schliermann, Rainer and Scheuer, Tanja and Heydenreich, P. and Anneken, Volker}, title = {Sport und Lebensqualit{\"a}t von Personen mit Sehsch{\"a}digungen}, series = {Angewandte Sportpsychologie : 45. Jahrestagung der Arbeitsgemeinschaft f{\"u}r Sportpsychologie (asp) vom 09. - 11. Mai 2013 in Halle (Saale)}, journal = {Angewandte Sportpsychologie : 45. Jahrestagung der Arbeitsgemeinschaft f{\"u}r Sportpsychologie (asp) vom 09. - 11. Mai 2013 in Halle (Saale)}, editor = {Stoll, Oliver and Lau, Andreas and Moczall, Simone}, publisher = {Feldhaus, Ed. Czwalina}, address = {Hamburg}, isbn = {978-3-88020-597-0}, pages = {91}, language = {de} } @misc{SaadLoewStolletal., author = {Saad, Amr Mohamed Roushdy and L{\"o}w, M.-O. and Stoll, O. and Schliermann, Rainer and Badran, A.}, title = {Perfektionismus im Sport, Validierung eines Messinstruments und {\"U}berpr{\"u}fung der Wirksamkeit eines Entspannungsverfahrens}, series = {Sportpsychologische Kompetenz und Verantwortung : 44. Jahrestagung der Arbeitsgemeinschaft f{\"u}r Sportpsychologie (asp) vom 17. - 19. Mai in Kiel/Oslo (Abstractband)}, journal = {Sportpsychologische Kompetenz und Verantwortung : 44. Jahrestagung der Arbeitsgemeinschaft f{\"u}r Sportpsychologie (asp) vom 17. - 19. Mai in Kiel/Oslo (Abstractband)}, editor = {Wegner, Manfred and Br{\"u}ckner, Jan-Peter and Kratzenstein, Stefan}, publisher = {Feldhaus, Edition Czwalina}, address = {Hamburg}, isbn = {978-3-88020-585-7}, pages = {132}, language = {de} } @misc{SchliermannSaad, author = {Schliermann, Rainer and Saad, A. R.}, title = {Evaluation einer von Trainern durchgef{\"u}hrten psychologischen Intervention am Beispiel der Sportart Rollstuhlbasketball}, series = {Sport vereinT : Psychologie und Bewegung in Gesellschaft ; 43. Jahrestagung der Arbeitsgemeinschaft f{\"u}r Sportpsychologie (asp) vom 2.-4. Juni 2011 in K{\"o}ln ; Abstractband}, journal = {Sport vereinT : Psychologie und Bewegung in Gesellschaft ; 43. Jahrestagung der Arbeitsgemeinschaft f{\"u}r Sportpsychologie (asp) vom 2.-4. Juni 2011 in K{\"o}ln ; Abstractband}, editor = {Ohlert, Jeannine and Kleinert, Jens}, publisher = {Feldhaus, Edition Czwalina}, address = {Hamburg}, isbn = {978-3-88020-566-6}, pages = {181}, subject = {Vereinssport}, language = {de} } @misc{SaadSchliermann, author = {Saad, Amr Mohamed Roushdy and Schliermann, Rainer}, title = {Perfektionismus im Sport: {\"U}bersetzung und Validierung eines Messinstruments und {\"U}berpr{\"u}fung des Einsatzes einer p{\"a}dagogisch-psychologischen Interventionstechnik in der Sportpraxis}, series = {Sport vereinT : Psychologie und Bewegung in Gesellschaft ; 43. Jahrestagung der Arbeitsgemeinschaft f{\"u}r Sportpsychologie (asp) vom 2.-4. Juni 2011 in K{\"o}ln}, journal = {Sport vereinT : Psychologie und Bewegung in Gesellschaft ; 43. Jahrestagung der Arbeitsgemeinschaft f{\"u}r Sportpsychologie (asp) vom 2.-4. Juni 2011 in K{\"o}ln}, editor = {Ohlert, Jeannine and Kleinert, Jens}, publisher = {Feldhaus, Edition Czwalina}, address = {Hamburg}, organization = {Deutsche Vereinigung f{\"u}r Sportwissenschaft}, isbn = {9783880205666}, pages = {118}, language = {de} } @misc{HagenahSchliermann, author = {Hagenah, J{\"o}rg and Schliermann, Rainer}, title = {Welche Freizeitaktivit{\"a}ten bedingen sportliche Aktivit{\"a}ten in einer mediatisierten Gesellschaft?}, series = {Mediatisierung der Gesellschaft? Mediensoziologische Forschung in vergleichender Perspektive ; 3. Workshop des Medienwissenschaftlichen Lehr­ und Forschungszentrums (MLFZ); Book of Abstracts}, journal = {Mediatisierung der Gesellschaft? Mediensoziologische Forschung in vergleichender Perspektive ; 3. Workshop des Medienwissenschaftlichen Lehr­ und Forschungszentrums (MLFZ); Book of Abstracts}, editor = {Hagenah, J{\"o}rg and Meulemann, Heiner}, publisher = {Universit{\"a}t zu K{\"o}ln, Wirtschafts- und Sozialwissenschaftliche Fakult{\"a}t}, address = {K{\"o}ln}, pages = {42 -- 42}, language = {de} }