@unpublished{LaubmannReschke, author = {Laubmann, Julia and Reschke, Johannes}, title = {Tackling fake images in cybersecurity - interpretation of a StyleGAN and lifting its black-box}, doi = {10.48550/arXiv.2507.13722}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-83930}, pages = {11}, abstract = {In today's digital age, concerns about the dangers of AI-generated images are increasingly common. One powerful tool in this domain is StyleGAN (style-based generative adversarial networks), a generative adversarial network capable of producing highly realistic synthetic faces. To gain a deeper understanding of how such a model operates, this work focuses on analyzing the inner workings of StyleGAN's generator component. Key architectural elements and techniques, such as the Equalized Learning Rate, are explored in detail to shed light on the model's behavior. A StyleGAN model is trained using the PyTorch framework, enabling direct inspection of its learned weights. Through pruning, it is revealed that a significant number of these weights can be removed without drastically affecting the output, leading to reduced computational requirements. Moreover, the role of the latent vector -- which heavily influences the appearance of the generated faces -- is closely examined. Global alterations to this vector primarily affect aspects like color tones, while targeted changes to individual dimensions allow for precise manipulation of specific facial features. This ability to finetune visual traits is not only of academic interest but also highlights a serious ethical concern: the potential misuse of such technology. Malicious actors could exploit this capability to fabricate convincing fake identities, posing significant risks in the context of digital deception and cybercrime.}, language = {en} } @inproceedings{FolgerHachaniFuxenetal., author = {Folger, Fabian and Hachani, Murad and Fuxen, Philipp and Graf, Julian and Fischer, Sebastian and Hackenberg, Rudolf}, title = {A Transformer-Based Framework for Anomaly Detection in Multivariate Time Series}, series = {CLOUD COMPUTING 2025, The Sixteenth International Conference on Cloud Computing, GRIDs, and Virtualization, April 06, 2025 to April 10, 2025, Valencia, Spain}, booktitle = {CLOUD COMPUTING 2025, The Sixteenth International Conference on Cloud Computing, GRIDs, and Virtualization, April 06, 2025 to April 10, 2025, Valencia, Spain}, publisher = {IARIA}, isbn = {978-1-68558-258-6}, issn = {2308-4294}, pages = {52 -- 57}, abstract = {This paper introduces a comprehensive Transformer-based architecture for anomaly detection in multivariate time series. Using self-attention, the framework efficiently processes high-dimensional sensor data without extensive feature engineering, enabling early detection of unusual patterns to prevent critical system failures. In a subsequent laboratory setup, the framework will be applied using fuzzing techniques to induce anomalies in an Electronic Control Unit, while monitoring side channels, such as temperature, voltage, and Controller Area Network messages. The overall structure of the architecture, as well as the necessary preprocessing steps, such as temporal aggregation and classification up to the optimization of the hyperparameters of the model, are presented. The evaluation of the model architecture with the postulated restrictions shows that the model handles anomaly scenarios in the dataset robustly. It is necessary to evaluate the extent to which the model can be used in practical applications in areas, such as cloud environments or the industrial Internet of Things. Overall, the results highlight the potential of Transformer models for the automated and reliable monitoring of complex time series data for deviations.}, language = {en} } @inproceedings{HauerSchultzHaugetal., author = {Hauer, Franziska and Schultz, Maximilian and Haug, Sonja and Weber, Karsten}, title = {Acceptance and Usage of AI Applications in Health-Focused NGOs}, series = {dHealth 2025, Proceedings of the 19th Health Informatics Meets Digital Health Conference}, booktitle = {dHealth 2025, Proceedings of the 19th Health Informatics Meets Digital Health Conference}, editor = {Baumgartner, Martin and Hayn, Dieter and Pfeifer, Bernhard and Schreier, G{\"u}nter}, publisher = {IOS Press}, address = {Wien}, isbn = {978-1-64368-592-2}, doi = {10.3233/SHTI250172}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-80613}, pages = {123 -- 128}, abstract = {Background: AI applications promise to be a valuable tool for healthfocused NGOs. While often operating with limited resources, these organizations recognize the potential of AI to streamline processes and support workflows through automation. However, challenges such as data privacy concerns—especially regarding personal medical data—and the low prioritization of implementation hinder progress. Objectives: This study examines the extent to which German health-focused NGOs can currently benefit from the application of AI. It evaluates demands, available resources, and use cases. Methods: A health-focused subset of data from project KINiro was analyzed. This research utilized a mixed-methods approach, combining two rounds of qualitative interviews with a quantitative survey. Results: The study revealed that while health-focused NGOs are in the early stages of AI implementation, there are hurdles such as ethical concerns and a lack of resources. Conclusion: AI has the potential to support health-focused NGOs in their work, if the challenges like resources, ethics, and data privacy are effectively addressed.}, 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} } @inproceedings{WiedererHoengEder, author = {Wiederer, Jonas and H{\"o}ng, Simon and Eder, Friedrich}, title = {Konzept zur KI-gest{\"u}tzten parametrischen Br{\"u}ckenmodellierung f{\"u}r ressourcen- und kostenoptimierte Bauwerksentw{\"u}rfe}, series = {Tagungsband 35. Forum Bauinformatik, 2024, Hamburg}, booktitle = {Tagungsband 35. Forum Bauinformatik, 2024, Hamburg}, editor = {St{\"u}hrenberg, Jan and Al-Zuriqat, Thamer and Chillon Geck, Carlos}, publisher = {Technische Universit{\"a}t Hamburg, Institut f{\"u}r Digitales und Autonomes Bauen}, address = {Hamburg}, doi = {10.15480/882.13506}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-77922}, pages = {50 -- 57}, abstract = {Br{\"u}ckenbauwerke stellen eine wesentliche Komponente einer soliden Infrastruktur dar. Wegen vernachl{\"a}ssigter Instandsetzungsmaßnahmen und sich ver{\"a}ndernder Anforderungen sind f{\"u}r eine Vielzahl dieser Bauwerke in Deutschland Ersatzneubauten notwendig. Diese Arbeit adressiert die Herausforderungen der Ressourcenverschwendung durch wiederholten Arbeitsaufwand bei individuellen Br{\"u}ckenentw{\"u}rfen f{\"u}r die angesprochenen Bauwerke. Es wird ein parametrisches Entwurfsmodells in Siemens NX entwickelt, um den Entwurfsprozess zu beschleunigen und eine einfache Anpassung an die entsprechenden Rahmenbedingungen zu erm{\"o}glichen. Zudem wird eine effektive Methode zur Anbindung an die Kostenermittlungssoftware RIB iTWO vorgestellt, die eine nachgelagerte Kostenkalkulation nach DIN 276 erm{\"o}glicht. Aufbauend darauf wird ein Optimierungsworkflow vorgestellt, welcher verschiedene Faktoren einbindet, um einen hochwertigen Bauwerksentwurf zu erzielen. Hierbei wird die Einbindung von Techniken aus dem Bereich der K{\"u}nstlichen Intelligenz angestrebt. Die vorgestellte Methodik soll die Wirtschaftlichkeitsbewertung und Entscheidungsfindung f{\"u}r die Realisierung der Br{\"u}ckenentw{\"u}rfe erleichtern und somit zu einer effizienteren Nutzung von Ressourcen im Br{\"u}ckenbau beitragen.}, language = {de} } @inproceedings{RoehrlStauferNadimpallietal., author = {R{\"o}hrl, Simon and Staufer, Susanne and Nadimpalli, Vamsi Krishna and Bugert, Flemming and Bugert, Flemming and Hauser, Florian and Grabinger, Lisa and Bittner, Dominik and Ezer, Timur and Mottok, J{\"u}rgen}, title = {PYTHIA - AI SUGGESTED INDIVIDUAL LEARNING PATHS FOR EVERY STUDENT}, series = {INTED2024 Proceedings: 18th International Technology, Education and Development Conference, Valencia, Spain. 4-6 March, 2024}, booktitle = {INTED2024 Proceedings: 18th International Technology, Education and Development Conference, Valencia, Spain. 4-6 March, 2024}, isbn = {978-84-09-59215-9}, issn = {2340-1079}, doi = {10.21125/inted.2024.0783}, pages = {2871 -- 2880}, abstract = {During the COVID-19 pandemic, the importance of digital course rooms, where teachers provide their learning materials, increased dramatically. While these platforms are crucial for providing teaching materials, they often fall short in addressing individual student needs. A system within an academic setting, capable of creating and presenting individual learning paths for each student, can solve these issues. These paths are composed of various learning elements - defined in our previous work as units of educational content with which a learner works. Currently, there is no suitable system that enables the integration of learning path generating algorithms into a digital course room. Therefore we present an application that enables this integration into the Moodle Learning Management System (LMS). More precisely, this paper presents a Moodle plugin together with its framework. It describes the mechanism for effectively collecting data from Moodle, which AI algorithms then use to generate personalized learning paths. Subsequently these paths are visualized with the help of the Moodle plugin. We started with a set of requirements and use cases for the interface connecting Moodle to the AI system, which were established with a group of experts. Based on the requirements, various relevant technologies were assessed, and the best ones were chosen for implementation. Following that, the paper develops a strategy for software structuring as well as an architecture, focusing on performance, modularity, and ease of deployment for widespread use. Furthermore, the architecture ensures a simple method for integrating the algorithms. Afterwards, the framework's concrete implementation is described. A technique for enriching learning elements with metadata is presented, and additionally a concept for presenting these learning elements within a hierarchy. Moreover, it is shown how questionnaire responses and learning analytics are utilized for data collection. We cover in detail techniques for extracting and storing data from the Moodle database, as well as methods for customizing Moodle course rooms and a standard API for incorporating AI algorithms. Finally, the paper discusses the application of the proposed framework in an actual course and how student feedback is collected, which could enhance the framework. It concludes with an assessment of the outcomes obtained and prospects for the framework's future advancements.}, 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 Andreas Helmut Otto and Schlottmann, Jakob and Prinz, Friederike and Rauber, David and R{\"u}ckert, 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}, volume = {56}, 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 = {641 -- 649}, 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{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} } @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} } @article{EbigboMendelScheppachetal., author = {Ebigbo, Alanna and Mendel, Robert and Scheppach, Markus W. and Probst, Andreas and Shahidi, Neal and Prinz, Friederike and Fleischmann, Carola and R{\"o}mmele, Christoph and G{\"o}lder, Stefan Karl and Braun, Georg and Rauber, David and R{\"u}ckert, Tobias and Souza Jr., Luis Antonio de and Papa, Jo{\~a}o Paulo and Byrne, Michael F. and Palm, Christoph and Messmann, Helmut}, title = {Vessel and tissue recognition during third-space endoscopy using a deep learning algorithm}, series = {Gut}, volume = {71}, journal = {Gut}, number = {12}, publisher = {BMJ}, address = {London}, doi = {10.1136/gutjnl-2021-326470}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-54293}, pages = {2388 -- 2390}, abstract = {In this study, we aimed to develop an artificial intelligence clinical decision support solution to mitigate operator-dependent limitations during complex endoscopic procedures such as endoscopic submucosal dissection and peroral endoscopic myotomy, for example, bleeding and perforation. A DeepLabv3-based model was trained to delineate vessels, tissue structures and instruments on endoscopic still images from such procedures. The mean cross-validated Intersection over Union and Dice Score were 63\% and 76\%, respectively. Applied to standardised video clips from third-space endoscopic procedures, the algorithm showed a mean vessel detection rate of 85\% with a false-positive rate of 0.75/min. These performance statistics suggest a potential clinical benefit for procedure safety, time and also training.}, language = {en} }