@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} } @incollection{Seifert, author = {Seifert, Ruth}, title = {Weibliche Soldaten}, series = {Frauen im Milit{\"a}r}, booktitle = {Frauen im Milit{\"a}r}, publisher = {VS Verlag f{\"u}r Sozialwissenschaften}, address = {Wiesbaden}, isbn = {978-3-8100-4136-4}, doi = {10.1007/978-3-322-81003-8_13}, pages = {230 -- 241}, abstract = {Seit Ende der 80er Jahre wird in der amerikanischen Milit{\"a}rsoziologie die Frage diskutiert, ob der Beruf des Soldaten eine „profession of arms" ist, die in einer Institution mit besonderen Merkmalen ausge{\"u}bt wird, oder sich vielmehr zu einem „Job wie jeder andere", also einer „occupation" entwickelt habe. Versucht man den Unterschied auf einen knappen Nenner zu bringen, so legitimiert sich eine Institution durch spezifische Normen und Werte und verfolgt einen Zweck, der das individuelle Eigeninteresse transzendiert; ein „Job" oder ein „Beruf wie jeder andere" folgt demgegen{\"u}ber dem Prinzip von Angebot und Nachfrage, beruht auf einer Fixierung von Rechten und Pflichten und wird von den Individuen auf der Grundlage utilitaristischer Erw{\"a}gungen gew{\"a}hlt (vgl. Moskos 1988).}, language = {de} } @masterthesis{Weiss, type = {Bachelor Thesis}, author = {Weiß, Luiza}, title = {Wie verh{\"a}lt sich der Beckenboden unter Spontangeburt?}, doi = {10.35096/othr/pub-7130}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-71307}, school = {Ostbayerische Technische Hochschule Regensburg}, pages = {56}, abstract = {Die hohe Rate an Geburtsverletzungen unter Spontangeburt mit teilweise gravierenden Kurz- und Langzeitfolgen stellt eine physische und psychosoziale Belastung f{\"u}r Geb{\"a}rende dar, sodass der Wunsch nach vollst{\"a}ndig erhaltenem Damm und erhaltener Beckenbodenmuskulatur stark vorhanden ist. Als Erhaltung der Gesundheit von Mutter und Kind stellt der Schutz des Dammes und Beckenbodens eine Kernkompetenz der Hebammen dar. Um dieser nachzukommen, wird die Pathophysiologie des Dammrisses er{\"o}rtert. Nachdem der weibliche Beckenboden anatomisch und histologisch aufgearbeitet wurde sowie die Pathophysiologie der Wundentstehung, konnte unter Einbeziehung des Verhaltens des Beckenbodens unter Spontangeburt die Pathophysiologie des Dammrisses aufgezeigt werden. Diese ist ein Zusammenspiel aus Druck auf das Gewebe durch das tiefertretende Kind und zum Teil aus den wirkenden Scherkr{\"a}ften der stattfindenden Dehnung. Der entscheidende Mechanismus liegt jedoch beim Druck, da dieser eine Minderversorgung der Zellen verursacht, sodass es zur hypoxischen Isch{\"a}mie und schließlich zum nekrotischen Zelltod kommt. Es konnte aufgezeigt werden, dass zus{\"a}tzliche Anspannung einen stark negativen Faktor f{\"u}r Geburtsverletzungen darstellt, weshalb die genetische Disposition sowie das maternales Alter valide Risikofaktoren sind und aus welchen Gr{\"u}nden eine trainierte Beckenbodenmuskulatur von Vorteil sein d{\"u}rfte.}, subject = {Dammriss}, language = {de} } @article{KnoedlerDeanKnoedleretal., author = {Knoedler, Leonard and Dean, Jillian and Knoedler, Samuel and Kauke-Navarro, Martin and Hollmann, Katharina and Alfertshofer, Michael and Helm, Sabrina and Prantl, Lukas and Schliermann, Rainer}, title = {Hard shell, soft core? Multi-disciplinary and multi-national insights into mental toughness among surgeons}, series = {Frontiers in Surgery}, volume = {11}, journal = {Frontiers in Surgery}, publisher = {Frontiers}, doi = {10.3389/fsurg.2024.1361406}, pages = {7}, abstract = {Background: With the prevalence of burnout among surgeons posing a significant threat to healthcare outcomes, the mental toughness of medical professionals has come to the fore. Mental toughness is pivotal for surgical performance and patient safety, yet research into its dynamics within a global and multi-specialty context remains scarce. This study aims to elucidate the factors contributing to mental toughness among surgeons and to understand how it correlates with surgical outcomes and personal well-being. Methods: Utilizing a cross-sectional design, this study surveyed 104 surgeons from English and German-speaking countries using the Mental Toughness Questionnaire (MTQ-18) along with additional queries about their surgical practice and general life satisfaction. Descriptive and inferential statistical analyses were applied to investigate the variations in mental toughness across different surgical domains and its correlation with professional and personal factors. Results: The study found a statistically significant higher level of mental toughness in micro-surgeons compared to macro-surgeons and a positive correlation between mental toughness and surgeons' intent to continue their careers. A strong association was also observed between general life satisfaction and mental toughness. No significant correlations were found between the application of psychological skills and mental toughness. Conclusion: Mental toughness varies significantly among surgeons from different specialties and is influenced by professional dedication and personal life satisfaction. These findings suggest the need for targeted interventions to foster mental toughness in the surgical community, potentially enhancing surgical performance and reducing burnout. Future research should continue to explore these correlations, with an emphasis on longitudinal data and the development of resilience-building programs.}, language = {en} } @article{ZhaoGaschler, author = {Zhao, Fang and Gaschler, Robert}, title = {Sending motivational emails in text-picture personalised form can be feasible in e-learning: An asynchronous course case}, series = {Open Learning: The Journal of Open, Distance and e-Learning}, journal = {Open Learning: The Journal of Open, Distance and e-Learning}, publisher = {Taylor \& Francis}, doi = {10.1080/02680513.2024.2326002}, pages = {1 -- 15}, abstract = {ntervention with motivational emails can have a positive effect on course retention in e-learning. It is, however, not yet clear whether different forms of emails affect course retention and how students make progress during the sending of emails. We therefore used a voluntary asynchronous online course with 206 students. Students were randomly divided into four groups: text-picture personalised email vs. text personalised email vs. generalised email vs. no email. Emails were sent weekly for 3 months. Results yield that more students made progress in the text-picture personalised email group than in the control group. Students in all email groups progressed by more units than students in the control group. Only students in email groups completed the course and only students in personalised email groups reacted to the emails. Emails were accepted by most students enrolled. The findings suggest that cost-effective and easily implemented emails can encourage students to progress from unit to unit.}, language = {en} } @article{BrandtZornPfingsten, author = {Brandt, Hanna and Zorn, Martin and Pfingsten, Andrea}, title = {Markerloses Tracking von Bewegung}, series = {pt - Zeitschrift f{\"u}r Physiotherapeuten}, volume = {75, 7}, journal = {pt - Zeitschrift f{\"u}r Physiotherapeuten}, number = {7}, publisher = {Richard Pflaum}, pages = {21 -- 26}, language = {de} } @article{HaugSchnellRaptisetal., author = {Haug, Sonja and Schnell, Rainer and Raptis, Georgios and Dotter, Caroline and Weber, Karsten}, title = {Wissen und Einstellung zur Speicherung und Nutzung von Gesundheitsdaten: Ergebnisse einer Bev{\"o}lkerungsbefragung}, series = {Zeitschrift f{\"u}r Evidenz, Fortbildung und Qualit{\"a}t im Gesundheitswesen}, journal = {Zeitschrift f{\"u}r Evidenz, Fortbildung und Qualit{\"a}t im Gesundheitswesen}, publisher = {Elsevier}, issn = {1865-9217}, doi = {10.1016/j.zefq.2023.11.001}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-67461}, pages = {50 -- 58}, abstract = {Hintergrund/Zielsetzung Der Beitrag befasst sich mit dem Wissenstand und der Einstellung der Bev{\"o}lkerung. Betrachtet werden die {\"U}bermittlung und Verf{\"u}gbarkeit von Gesundheitsdaten, Gesundheitsregister, die elektronische Patientenakte, Einwilligungsverfahren f{\"u}r die {\"U}bermittlung von Daten und der Zugriff auf Gesundheitsdaten zu Forschungszwecken. Methoden Die Studie basiert auf einer computergest{\"u}tzten Telefonbefragung (Dual-Frame) bei einer Zufallsstichprobe der Bev{\"o}lkerung in Deutschland im Zeitraum 01.-27.06.2022 (n = 1.308). Ergebnisse Der Wissensstand zur {\"U}bermittlung von Gesundheitsdaten an Krankenkassen ist hoch, wohingegen das Vorhandensein zentraler Sterbe-, Impf- und Gesundheitsregister sowie der Zugriff auf Gesundheitsdaten durch behandelnde {\"A}rztinnen und {\"A}rzte {\"u}bersch{\"a}tzt werden. Die Akzeptanz medizinischer Register ist sehr hoch. Die elektronische Patientenakte ist bei der H{\"a}lfte der Bev{\"o}lkerung unbekannt, die Nutzungsbereitschaft ist eher gering ausgepr{\"a}gt; bei der {\"U}bertragung von Daten wird eine Zustimmungsoption bevorzugt, und {\"u}ber achtzig Prozent w{\"u}rden die Daten der elektronischen Patientenakte zur Forschung freigeben. Drei Viertel w{\"u}rden ihre Gesundheitsdaten allgemein zur Forschung freigeben, insbesondere an Universit{\"a}ten in Deutschland, wobei meist Anonymit{\"a}t Bedingung ist. Die Bereitschaft zur Datenfreigabe steigt mit der H{\"o}he des Vertrauens in die Presse sowie in Universit{\"a}ten und Hochschulen, und sie sinkt, wenn ein Datenleck als schwerwiegend erachtet wird. Diskussion und Schlussfolgerung In Deutschland besteht, wie in anderen europ{\"a}ischen L{\"a}ndern, eine große Bereitschaft zur Freigabe von Gesundheitsdaten zu Forschungszwecken. Dagegen ist der Wunsch zur Nutzung der elektronischen Patientenakte eher gering. Ebenso niedrig ist die Akzeptanz einer Widerspruchsoption, die jedoch als Voraussetzung f{\"u}r eine erfolgreiche Einf{\"u}hrung einer elektronischen Patientenakte gilt. Vertrauen in die Forschung und staatliche Stellen, die Gesundheitsdaten verarbeiten, sind zentrale Faktoren.}, language = {en} } @article{MeinikheimMendelPalmetal., author = {Meinikheim, Michael and Mendel, Robert and Palm, Christoph and Probst, Andreas and Muzalyova, Anna and Scheppach, Markus W. and Nagl, Sandra and Schnoy, Elisabeth and R{\"o}mmele, Christoph and Schulz, Dominik A. H. and Schlottmann, Jakob and Prinz, Friederike and Rauber, David and Rueckert, Tobias and Matsumura, Tomoaki and Fern{\´a}ndez-Esparrach, Gl{\`o}ria and Parsa, Nasim and Byrne, Michael F. and Messmann, Helmut and Ebigbo, Alanna}, title = {Influence of artificial intelligence on the diagnostic performance of endoscopists in the assessment of Barrett's esophagus: a tandem randomized and video trial}, series = {Endoscopy}, journal = {Endoscopy}, publisher = {Georg Thieme Verlag}, address = {Stuttgart}, doi = {10.1055/a-2296-5696}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-72818}, pages = {9}, abstract = {Background This study evaluated the effect of an artificial intelligence (AI)-based clinical decision support system on the performance and diagnostic confidence of endoscopists in their assessment of Barrett's esophagus (BE). Methods 96 standardized endoscopy videos were assessed by 22 endoscopists with varying degrees of BE experience from 12 centers. Assessment was randomized into two video sets: group A (review first without AI and second with AI) and group B (review first with AI and second without AI). Endoscopists were required to evaluate each video for the presence of Barrett's esophagus-related neoplasia (BERN) and then decide on a spot for a targeted biopsy. After the second assessment, they were allowed to change their clinical decision and confidence level. Results AI had a stand-alone sensitivity, specificity, and accuracy of 92.2\%, 68.9\%, and 81.3\%, respectively. Without AI, BE experts had an overall sensitivity, specificity, and accuracy of 83.3\%, 58.1\%, and 71.5\%, respectively. With AI, BE nonexperts showed a significant improvement in sensitivity and specificity when videos were assessed a second time with AI (sensitivity 69.8\% [95\%CI 65.2\%-74.2\%] to 78.0\% [95\%CI 74.0\%-82.0\%]; specificity 67.3\% [95\%CI 62.5\%-72.2\%] to 72.7\% [95\%CI 68.2\%-77.3\%]). In addition, the diagnostic confidence of BE nonexperts improved significantly with AI. Conclusion BE nonexperts benefitted significantly from additional AI. BE experts and nonexperts remained significantly below the stand-alone performance of AI, suggesting that there may be other factors influencing endoscopists' decisions to follow or discard AI advice.}, language = {en} } @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} } @article{SouzaJrPassosSantanaetal., author = {Souza Jr., Luis Antonio de and Passos, Leandro A. and Santana, Marcos Cleison S. and Mendel, Robert and Rauber, David and Ebigbo, Alanna and Probst, Andreas and Messmann, Helmut and Papa, Jo{\~a}o Paulo and Palm, Christoph}, title = {Layer-selective deep representation to improve esophageal cancer classification}, series = {Medical \& Biological Engineering \& Computing}, journal = {Medical \& Biological Engineering \& Computing}, publisher = {Springer Nature}, address = {Heidelberg}, doi = {10.1007/s11517-024-03142-8}, pages = {18}, abstract = {Even though artificial intelligence and machine learning have demonstrated remarkable performances in medical image computing, their accountability and transparency level must be improved to transfer this success into clinical practice. The reliability of machine learning decisions must be explained and interpreted, especially for supporting the medical diagnosis.For this task, the deep learning techniques' black-box nature must somehow be lightened up to clarify its promising results. Hence, we aim to investigate the impact of the ResNet-50 deep convolutional design for Barrett's esophagus and adenocarcinoma classification. For such a task, and aiming at proposing a two-step learning technique, the output of each convolutional layer that composes the ResNet-50 architecture was trained and classified for further definition of layers that would provide more impact in the architecture. We showed that local information and high-dimensional features are essential to improve the classification for our task. Besides, we observed a significant improvement when the most discriminative layers expressed more impact in the training and classification of ResNet-50 for Barrett's esophagus and adenocarcinoma classification, demonstrating that both human knowledge and computational processing may influence the correct learning of such a problem.}, language = {en} }