TY - THES A1 - Hördegen, Simone T1 - Überlegungen zu einer sich selbst steuernden Wirbelschichtanlage N2 - Eines der größten Probleme bei Granulationsprozessen in der pharmazeutischen Industrie ist die Feuchtigkeit der Prozessluft. Kann bzw. möchte man die Luft aus ökonomischen oder sonstigen Gründen hinsichtlich ihres absoluten Feuchtgehalts nicht konditionieren, bleibt bei hoher Luftfeuchte – wie sie z.B. beim Aufzug eines Gewitters oder bei heftigen Regenfällen auftritt – oft nur die Option des Produktionsstillstandes. Die vorliegende Arbeit befasst sich einerseits mit der Frage, ob es möglich ist, unabhängig von den Außenluftbedingungen – wie Temperatur, Druck und relative Feuchte – Granulate mit vergleichbaren Eigenschaften zu reproduzieren. Zum anderen soll geklärt werden, welchen Einfluss verschiedene Prozess- und Materialparameter bzw. deren Schwankungen auf das Endprodukt haben, und was dies wiederum für eine Automatisierung des Prozesses bzw. für die Anforderungen an eine Steuer- und Regelung der Herstellanlage bedeutet. Ausgehend von der Massenbilanzierung einer Wirbelschichtgranulierung wird der Einfluss verschiedener Prozess- und Materialparameter auf ein Standardgranulat untersucht. Die Ergebnisse der unterschiedlichen Versuchsreihen bestätigen einerseits die Reproduzierbarkeit von Granulateigenschaften basierend auf den Berechnungen der kritischen Sprührate. Andererseits zeigen sie den Einfluss verschiedener Prozess- und Materialparameter auf die Qualität des Endproduktes. Hieraus können wichtige Erkenntnisse für eine automatische Steuer- und Regelung der Herstellanlage abgeleitet und entsprechende Sollanforderungen für jeden einzelnen Prozessparameter sowie die überwachenden Sensoren definiert werden. Die Berechnungen zur Machbarkeit eines Granulatansatzes sind eine wertvolle Entscheidungsgrundlage hinsichtlich der Planung einer Granulatherstellung und dienen auch für eine Ansatzvergrößerung als Kalkulationsbasis. Ebenso kann die Algorithmenabfolge der „kritischen Sprührate“ zusammen mit den Formeln der „Berechnungen zur Machbarkeit“ für die Anpassung der Prozessparameter an die jahres- und tageszeitlichen Schwankungen der Außenluft herangezogen werden. Wie theoretische Studien zum „Ausgleich der Außenluftbedingungen“ aufzeigen, ist es mit Hilfe dieser Algorithmen möglich, die freie Feuchte während der Sprühphase auf ein definiertes Niveau zu bringen und dort zu halten. Dieser Anteil an überschüssigem Wasser ist primär für das Kornwachstum und somit für die Reproduktion von Granulaten verantwortlich. Die vorliegende Arbeit stellt mit ihren theoretischen Ansätzen einen entscheidenden Schritt hin zu einer automatisierten Wirbelschichtanlage dar. Sie zeigt Ansatzpunkte für ein mögliches Vorgehen auf und liefert Hinweise für die Anforderungen an Messsensoren sowie Steuer- und Regeleinheiten. N2 - The humidity of the inlet air is one of the largest problems within granulation pro-cesses in the pharmaceutical industry. Is it not possible to condition the inlet air in reference to its absolute moisture, there often only remains the alternative to stop production. One topic of the existent dissertation deals with the question wether it is possible to produce granulates of comparable characteristics independent from the conditions of external air like temperature, pressure and moisture. On the other hand it is examined which influence several process and material parameters or rather their variations have on the final product and what that means for an automation of the process or the specification of production plant control and regulation. Based on the mass balance of a fluidized bed granulation the effects of different process and material parameters on a standard granulate are proved. On the one hand the re-sults of the different test series attest the reproducibility of granulate characteristics based on the calculations of the critical spray rate. Otherwise they point out the in-fluence of several process and material parameters on the quality of the end product. Based on these results important perceptions for an automated control system and regulation of the production plant can be deflected and adequate requirements for every single process parameter as well as for supervising sensors can be defined. The calculation of the practicability of batches is a significant foundation for a deci-cion in reference to batch planning and provides a basis for scaling up processes. Apart from that the algorithms are able to adapt the process parameters to the daily and annually variations of the inlet air. Theoretical studies to “adjustment of inlet air conditions” show, that thanks to the calculations it is possible to bring the free mois-ture to a defined level during spraying. This part of excessive water is responsible for agglomeration and therefore for the reproduction of granulates. The existing disser-tation represents an important step to an automated fluidised bed granulator. It shows approaches for possible proceeding and provides precious indications for re-quirements of measuring sensors as well as for control systems and regulation units. KW - Wirbelschichtverfahren KW - Granulieren KW - Luftfeuchtigkeit KW - Wirbelschichtgranulation KW - Automatisierung KW - Außenluftfeuchte KW - fluidised bed granulation KW - automation KW - air humidity Y1 - 2005 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-15166 ER - TY - JOUR A1 - Haeusner, Sebastian A1 - Herbst, Laura A1 - Bittorf, Patrick A1 - Schwarz, Thomas A1 - Henze, Chris A1 - Mauermann, Marc A1 - Ochs, Jelena A1 - Schmitt, Robert A1 - Blache, Ulrich A1 - Wixmerten, Anke A1 - Miot, Sylvie A1 - Martin, Ivan A1 - Pullig, Oliver T1 - From Single Batch to Mass Production–Automated Platform Design Concept for a Phase II Clinical Trial Tissue Engineered Cartilage Product JF - Frontiers in Medicine N2 - Advanced Therapy Medicinal Products (ATMP) provide promising treatment options particularly for unmet clinical needs, such as progressive and chronic diseases where currently no satisfying treatment exists. Especially from the ATMP subclass of Tissue Engineered Products (TEPs), only a few have yet been translated from an academic setting to clinic and beyond. A reason for low numbers of TEPs in current clinical trials and one main key hurdle for TEPs is the cost and labor-intensive manufacturing process. Manual production steps require experienced personnel, are challenging to standardize and to scale up. Automated manufacturing has the potential to overcome these challenges, toward an increasing cost-effectiveness. One major obstacle for automation is the control and risk prevention of cross contaminations, especially when handling parallel production lines of different patient material. These critical steps necessitate validated effective and efficient cleaning procedures in an automated system. In this perspective, possible technologies, concepts and solutions to existing ATMP manufacturing hurdles are discussed on the example of a late clinical phase II trial TEP. In compliance to Good Manufacturing Practice (GMP) guidelines, we propose a dual arm robot based isolator approach. Our novel concept enables complete process automation for adherent cell culture, and the translation of all manual process steps with standard laboratory equipment. Moreover, we discuss novel solutions for automated cleaning, without the need for human intervention. Consequently, our automation concept offers the unique chance to scale up production while becoming more cost-effective, which will ultimately increase TEP availability to a broader number of patients. KW - ATMP KW - tissue engineering KW - GMP KW - manufacturing KW - autologous KW - cartilage regeneration KW - automation & robotics KW - automation Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-244631 SN - 2296-858X VL - 8 ER - TY - JOUR A1 - Krenzer, Adrian A1 - Makowski, Kevin A1 - Hekalo, Amar A1 - Fitting, Daniel A1 - Troya, Joel A1 - Zoller, Wolfram G. A1 - Hann, Alexander A1 - Puppe, Frank T1 - Fast machine learning annotation in the medical domain: a semi-automated video annotation tool for gastroenterologists JF - BioMedical Engineering OnLine N2 - Background Machine learning, especially deep learning, is becoming more and more relevant in research and development in the medical domain. For all the supervised deep learning applications, data is the most critical factor in securing successful implementation and sustaining the progress of the machine learning model. Especially gastroenterological data, which often involves endoscopic videos, are cumbersome to annotate. Domain experts are needed to interpret and annotate the videos. To support those domain experts, we generated a framework. With this framework, instead of annotating every frame in the video sequence, experts are just performing key annotations at the beginning and the end of sequences with pathologies, e.g., visible polyps. Subsequently, non-expert annotators supported by machine learning add the missing annotations for the frames in-between. Methods In our framework, an expert reviews the video and annotates a few video frames to verify the object’s annotations for the non-expert. In a second step, a non-expert has visual confirmation of the given object and can annotate all following and preceding frames with AI assistance. After the expert has finished, relevant frames will be selected and passed on to an AI model. This information allows the AI model to detect and mark the desired object on all following and preceding frames with an annotation. Therefore, the non-expert can adjust and modify the AI predictions and export the results, which can then be used to train the AI model. Results Using this framework, we were able to reduce workload of domain experts on average by a factor of 20 on our data. This is primarily due to the structure of the framework, which is designed to minimize the workload of the domain expert. Pairing this framework with a state-of-the-art semi-automated AI model enhances the annotation speed further. Through a prospective study with 10 participants, we show that semi-automated annotation using our tool doubles the annotation speed of non-expert annotators compared to a well-known state-of-the-art annotation tool. Conclusion In summary, we introduce a framework for fast expert annotation for gastroenterologists, which reduces the workload of the domain expert considerably while maintaining a very high annotation quality. The framework incorporates a semi-automated annotation system utilizing trained object detection models. The software and framework are open-source. KW - object detection KW - machine learning KW - deep learning KW - annotation KW - endoscopy KW - gastroenterology KW - automation Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-300231 VL - 21 IS - 1 ER - TY - CHAP ED - Ludwigs, Markus ED - Muriel Ciceri, José Hernán ED - Velling, Annika T1 - Digitalization as a challenge for justice and administration T1 - La digitalización como reto para la justicia y la administración T1 - Digitalisierung als Herausforderung für Justiz und Verwaltung N2 - Digitalization is one of the global challenges for justice and This volume documents the presentations of a multilingual online conference on "Digitalization as a challenge for justice and administration" held in March 2022. The contributions of the international team of authors provide insights into central issues of this highly relevant subject from African, Japanese, U.S., Swiss, Latin American and German perspectives. The result is a multifaceted picture of digitalization in the context of public, private and even criminal law. N2 - Este volumen documenta las presentaciones de una conferencia multilingüe en línea sobre "La digitalización como reto para la justicia y la administración" celebrada en marzo de 2022. Las contribuciones del equipo internacional de autores ofrecen una visión de las cuestiones centrales de este tema de gran actualidad desde las perspectivas africana, japonesa, estadounidense, suiza, latinoamericana y alemana. El resultado es una imagen multifacética de la digitalización en el contexto del derecho público, privado y penal. N2 - Der vorliegende Tagungsband dokumentiert die Vorträge einer im März 2022 durchgeführten multilingualen Online-Konferenz zur "Digitalisierung als Herausforderung für Justiz und Verwaltung". Die Beiträge des internationalen Autorenteams vermitteln Einblicke in zentrale Fragestellungen der hochaktuellen Thematik aus afrikanischer, japanischer, US-amerikanischer, schweizerischer, lateinamerikanischer und deutscher Perspektive. Dabei ergibt sich ein facettenreiches Bild zur Digitalisierung im öffentlich-rechtlichen, privatrechtlichen und auch strafrechtlichen Kontext. T3 - Abhandlungen zum Öffentlichen Recht - 1 KW - Digitalisierung KW - Verwaltung KW - Justiz KW - Internationales Recht KW - künstliche Intelligenz KW - Automatisierung KW - Regulierung KW - E-Government KW - automation KW - regulation Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-301717 SN - 978-3-95826-200-3 SN - 978-3-95826-201-0 SN - 2941-2854 SN - 2941-2862 N1 - Parallel erschienen als Druckausgabe bei Würzburg University Press, ISBN 978-3-95826-200-3, 25,80 EUR. PB - Würzburg University Press CY - Würzburg ER - TY - CHAP A1 - Förstner, Konrad A1 - Hagedorn, Gregor A1 - Koltzenburg, Claudia A1 - Kubke, Fabiana A1 - Mietchen, Daniel T1 - Collaborative platforms for streamlining workflows in Open Science T2 - Proceedings of the 6th Open Knowledge Conference N2 - Despite the internet's dynamic and collaborative nature, scientists continue to produce grant proposals, lab notebooks, data files, conclusions etc. that stay in static formats or are not published online and therefore not always easily accessible to the interested public. Because of limited adoption of tools that seamlessly integrate all aspects of a research project (conception, data generation, data evaluation, peerreviewing and publishing of conclusions), much effort is later spent on reproducing or reformatting individual entities before they can be repurposed independently or as parts of articles. We propose that workflows - performed both individually and collaboratively - could potentially become more efficient if all steps of the research cycle were coherently represented online and the underlying data were formatted, annotated and licensed for reuse. Such a system would accelerate the process of taking projects from conception to publication stages and allow for continuous updating of the data sets and their interpretation as well as their integration into other independent projects. A major advantage of such work ows is the increased transparency, both with respect to the scientific process as to the contribution of each participant. The latter point is important from a perspective of motivation, as it enables the allocation of reputation, which creates incentives for scientists to contribute to projects. Such work ow platforms offering possibilities to fine-tune the accessibility of their content could gradually pave the path from the current static mode of research presentation into a more coherent practice of open science. KW - Open Science KW - Virtual Research Environment KW - collaboratories KW - workflow platform KW - automation Y1 - 2011 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-101678 ER - TY - JOUR A1 - Krenzer, Adrian A1 - Heil, Stefan A1 - Fitting, Daniel A1 - Matti, Safa A1 - Zoller, Wolfram G. A1 - Hann, Alexander A1 - Puppe, Frank T1 - Automated classification of polyps using deep learning architectures and few-shot learning JF - BMC Medical Imaging N2 - Background Colorectal cancer is a leading cause of cancer-related deaths worldwide. The best method to prevent CRC is a colonoscopy. However, not all colon polyps have the risk of becoming cancerous. Therefore, polyps are classified using different classification systems. After the classification, further treatment and procedures are based on the classification of the polyp. Nevertheless, classification is not easy. Therefore, we suggest two novel automated classifications system assisting gastroenterologists in classifying polyps based on the NICE and Paris classification. Methods We build two classification systems. One is classifying polyps based on their shape (Paris). The other classifies polyps based on their texture and surface patterns (NICE). A two-step process for the Paris classification is introduced: First, detecting and cropping the polyp on the image, and secondly, classifying the polyp based on the cropped area with a transformer network. For the NICE classification, we design a few-shot learning algorithm based on the Deep Metric Learning approach. The algorithm creates an embedding space for polyps, which allows classification from a few examples to account for the data scarcity of NICE annotated images in our database. Results For the Paris classification, we achieve an accuracy of 89.35 %, surpassing all papers in the literature and establishing a new state-of-the-art and baseline accuracy for other publications on a public data set. For the NICE classification, we achieve a competitive accuracy of 81.13 % and demonstrate thereby the viability of the few-shot learning paradigm in polyp classification in data-scarce environments. Additionally, we show different ablations of the algorithms. Finally, we further elaborate on the explainability of the system by showing heat maps of the neural network explaining neural activations. Conclusion Overall we introduce two polyp classification systems to assist gastroenterologists. We achieve state-of-the-art performance in the Paris classification and demonstrate the viability of the few-shot learning paradigm in the NICE classification, addressing the prevalent data scarcity issues faced in medical machine learning. KW - machine learning KW - deep learning KW - endoscopy KW - gastroenterology KW - automation KW - image classification KW - transformer KW - deep metric learning KW - few-shot learning Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-357465 VL - 23 ER - TY - JOUR A1 - Krenzer, Adrian A1 - Banck, Michael A1 - Makowski, Kevin A1 - Hekalo, Amar A1 - Fitting, Daniel A1 - Troya, Joel A1 - Sudarevic, Boban A1 - Zoller, Wolfgang G. A1 - Hann, Alexander A1 - Puppe, Frank T1 - A real-time polyp-detection system with clinical application in colonoscopy using deep convolutional neural networks JF - Journal of Imaging N2 - Colorectal cancer (CRC) is a leading cause of cancer-related deaths worldwide. The best method to prevent CRC is with a colonoscopy. During this procedure, the gastroenterologist searches for polyps. However, there is a potential risk of polyps being missed by the gastroenterologist. Automated detection of polyps helps to assist the gastroenterologist during a colonoscopy. There are already publications examining the problem of polyp detection in the literature. Nevertheless, most of these systems are only used in the research context and are not implemented for clinical application. Therefore, we introduce the first fully open-source automated polyp-detection system scoring best on current benchmark data and implementing it ready for clinical application. To create the polyp-detection system (ENDOMIND-Advanced), we combined our own collected data from different hospitals and practices in Germany with open-source datasets to create a dataset with over 500,000 annotated images. ENDOMIND-Advanced leverages a post-processing technique based on video detection to work in real-time with a stream of images. It is integrated into a prototype ready for application in clinical interventions. We achieve better performance compared to the best system in the literature and score a F1-score of 90.24% on the open-source CVC-VideoClinicDB benchmark. KW - machine learning KW - deep learning KW - endoscopy KW - gastroenterology KW - automation KW - object detection KW - video object detection KW - real-time Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-304454 SN - 2313-433X VL - 9 IS - 2 ER -