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Functionally impaired people have problems with choosing and finding the right clothing. So, they need help in their daily life to wash and manage the clothing. The goal of this work is to support the user by giving recommendations to choose the right clothing, to find the clothing and how to wash the clothing. The idea behind eKlarA is to generate a gateway based system that uses sensors to identify the clothing and their state in the clothing cycle. The clothing cycle consists of (one and more) closet, laundry basket and washing machine in one or several places. The gateway uses the information about the clothing, weather and calendar to support the user in the different steps of the clothing cycle. This allows to give more freedom to the functionally impaired people in their daily life.
How to protect the skin from getting sun burnt? The sun can damage your skin e.g. skin cancer. But the sun has a positive effect to the human. The time in sun and the intensity are key values between enjoy the sunbath and having a negative effect to the skin. A smart device like a UV flower could help you to enjoy the sunbath. It measures the UV index around you and gives this information to a smartphone app. The development steps of such a device are described in this paper. The UV flower is made of textile fabrics.
Telemedicine is becoming an increasingly important approach to diagnostic, treat or prevent diseases. However, the usage of Information Communication Technologies in healthcare results in a considerable amount of data that must be efficiently and securely transmitted. Many manufacturers provide telemedicine platforms without regarding interoperability, mobility and collaboration. This paper describes a collaborative mobile telemonitoring platform that can use the IEEE 11073 and HL7 communication standards or adapt proprietary protocols. The proposed platform also covers the security and modularity aspects. Furthermore this work introduces an Android-based prototype implementation
The main aim of presented in this manuscript research is to compare the results of objective and subjective measurement of sleep quality for older adults (65+) in the home environment. A total amount of 73 nights was evaluated in this study. Placing under the mattress device was used to obtain objective measurement data, and a common question on perceived sleep quality was asked to collect the subjective sleep quality level. The achieved results confirm the correlation between objective and subjective measurement of sleep quality with the average standard deviation equal to 2 of 10 possible quality points.
Type 1 diabetes is a chronic and a life threatening disease: an adjusted treatment and a proper management of the disease are crucial to prevent or delay the complications of diabetes. Although during the last decade the development of the artificial pancreas has presented great advances in diabetes care, the multiple daily injections therapy still represents the most widely used treatment option for type 1 diabetes. This work presents the proposal and first development stages of an application focused on guiding patients using the continuous glucose monitors and smart pens together with insulin and carbohydrates recommendations. Our proposal aims to develop a platform to integrate a series of innovative machine learning models and tools rigorously tested together with the use of the latest IoT devices to manage type 1 diabetes. The resulting system actually closes the loop, like the artificial pancreas, but in an intermittent way.
The best fully automated analysis process achieves even better classification results than the established manual process. The best algorithms for the three analysis steps are (i) SGLTR (Savitzky-Golay Laplace operator filter thresholding regions) and LM (Local Maxima) for automated peak identification, (ii) EM clustering (Expectation Maximization) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) for the clustering step and (iii) RF (Random Forest) for multivariate classification. Thus, automated methods can replace the manual steps in the analysis process to enable an unbiased high throughput use of the technology.
Purpose
Supporting the surgeon during surgery is one of the main goals of intelligent ORs. The OR-Pad project aims to optimize the information flow within the perioperative area. A shared information space should enable appropriate preparation and provision of relevant information at any time before, during, and after surgery.
Methods
Based on previous work on an interaction concept and system architecture for the sterile OR-Pad system, we designed a user interface for mobile and intraoperative (stationary) use, focusing on the most important functionalities like clear information provision to reduce information overload. The concepts were transferred into a high-fidelity prototype for demonstration purposes. The prototype was evaluated from different perspectives, including a usability study.
Results
The prototype’s central element is a timeline displaying all available case information chronologically, like radiological images, labor findings, or notes. This information space can be adapted for individual purposes (e.g., highlighting a tumor, filtering for own material). With the mobile and intraoperative mode of the system, relevant information can be added, preselected, viewed, and extended during the perioperative process. Overall, the evaluation showed good results and confirmed the vision of the information system.
Conclusion
The high-fidelity prototype of the information system OR-Pad focuses on supporting the surgeon via a timeline making all available case information accessible before, during, and after surgery. The information space can be personalized to enable targeted support. Further development is reasonable to optimize the approach and address missing or insufficient aspects, like the holding arm and sterility concept or new desired features.
This paper presents a new European initiative to support the sustainable empowerment of the ageing society. Empowerment in this context represents the capability to have a self-determined, autonomous and healthy life. The paper justifies the need of such an initiative and highlights the role that telemedicine and ambient assisted living can play in this environment.
Motivation
In order to enable context-aware behavior of surgical assistance systems, the acquisition of various information about the current intraoperative situation is crucial. To achieve this, the complex task of situation recognition can be delegated to a specialized system. Consequently, a standardized interface is required for the seamless transfer of the recognized contextual information to the assistance systems, enabling them to adapt accordingly.
Methods
Our group analyzed four medical interface standards to determine their suitability for exchanging intraoperative contextual information. The assessment was based on a harmonized data and service model derived from the requirements of expected context-aware use cases. The Digital Imaging and Communications in Medicine (DICOM) and IEEE 11073 for Service-oriented Device Connectivity (SDC) were identified as the most appropriate standards.
Results
We specified how DICOM Unified Procedure Steps (UPS), can be used to effectively communicate contextual information. We proposed the inclusion of attributes to formalize different granularity levels of the surgical workflow.
Conclusions
DICOM UPS SOP classes can be used for the exchange of intraoperative contextual information between a situation recognition system and surgical assistance systems. This can pave the way for vendor-independent context awareness in the OR, leading to targeted assistance of the surgical team and an improvement of the surgical workflow.
Documentation of clinical processes, especially in the perioperative are, is a base requirement for quality of service. Nonetheless, the documentation is a burden for the medical staff since it distracts from the clinical core process. An intuitive and user-friendly documentation system could increase documentation quality and reduce documentation workload. The optimal system solution would know what happened and the person documenting the step would need a single “confirm” button. In many cases, such a linear flow of activities is given as long as only one profession (e.g. anaestesiology, scrub nurse) is considered, but even in such cases, there might be derivations from the linear process flow and further interaction is required.
Background and purpose: Transapical aortic valve replacement (TAVR) is a recent minimally invasive surgical treatment technique for elderly and high-risk patients with severe aortic stenosis. In this paper,a simple and accurate image-based method is introduced to aid the intra-operative guidance of TAVR procedure under 2-D X-ray fluoroscopy.
Methods: The proposed method fuses a 3-D aortic mesh model and anatomical valve landmarks with live 2-D fluoroscopic images. The 3-D aortic mesh model and landmarks are reconstructed from interventional X-ray C-arm CT system, and a target area for valve implantation is automatically estimated using these aortic mesh models.Based on template-based tracking approach, the overlay of visualized 3-D aortic mesh model, land-marks and target area of implantation is updated onto fluoroscopic images by approximating the aortic root motion from a pigtail catheter motion without contrast agent. Also, a rigid intensity-based registration algorithm is used to track continuously the aortic root motion in the presence of contrast agent.Furthermore, a sensorless tracking of the aortic valve prosthesis is provided to guide the physician to perform the appropriate placement of prosthesis into the estimated target area of implantation.
Results: Retrospective experiments were carried out on fifteen patient datasets from the clinical routine of the TAVR. The maximum displacement errors were less than 2.0 mm for both the dynamic overlay of aortic mesh models and image-based tracking of the prosthesis, and within the clinically accepted ranges. Moreover, high success rates of the proposed method were obtained above 91.0% for all tested patient datasets.
Conclusion: The results showed that the proposed method for computer-aided TAVR is potentially a helpful tool for physicians by automatically defining the accurate placement position of the prosthesis during the surgical procedure.
Introduction: Telemedicine reduces greenhouse gas emissions (CO2eq); however, results of studies vary extremely in dependence of the setting. This is the first study to focus on effects of telemedicine on CO2 imprint of primary care.
Methods: We conducted a comprehensive retrospective study to analyze total CO2eq emissions of kilometers (km) saved by telemedical consultations. We categorized prevented and provoked patient journeys, including pharmacy visits. We calculated CO2eq emission savings through primary care telemedical consultations in comparison to those that would have occurred without telemedicine. We used the comprehensive footprint approach, including all telemedical cases and the CO2eq emissions by the telemedicine center infrastructure. In order to determine the net ratio of CO2eq emissions avoided by the telemedical center, we calculated the emissions associated with the provision of telemedical consultations (including also the total consumption of physicians’ workstations) and subtracted them from the total of avoided CO2eq emissions. Furthermore, we also considered patient cases in our calculation that needed to have an in-person visit after the telemedical consultation. We calculated the savings taking into account the source of the consumed energy (renewable or not).
Results: 433 890 telemedical consultations overall helped save 1 800 391 km in travel. On average, 1 telemedical consultation saved 4.15 km of individual transport and consumed 0.15 kWh. We detected savings in almost every cluster of patients. After subtracting the CO2eq emissions caused by the telemedical center, the data reveal savings of 247.1 net tons of CO2eq emissions in total and of 0.57 kg CO2eq per telemedical consultation. The comprehensive footprint approach thus indicated a reduced footprint due to telemedicine in primary care.
Discussion: Integrating a telemedical center into the health care system reduces the CO2 footprint of primary care medicine; this is true even in a densely populated country with little use of cars like Switzerland. The insight of this study complements previous studies that focused on narrower aspects of telemedical consultations.
This project aims to evaluate existing big data infrastructures for their applicability in the operating room to support medical staff with context-sensitive systems. Requirements for the system design were generated. The project compares different data mining technologies, interfaces, and software system infrastructures with a focus on their usefulness in the peri-operative setting. The lambda architecture was chosen for the proposed system design, which will provide data for both postoperative analysis and real-time support during surgery.
Size and function of bioartificial tissue models are still limited due to the lack of blood vessels and dynamic perfusion for nutrient supply. In this study, we evaluated the use of cytocompatible methacryl-modified gelatin for the fabrication of a hydrogel-based tube by dip-coating and subsequent photo-initiated cross-linking. The wall thickness of the tubes and the diameter were tuned by the degree of gelatin methacryl-modification and the number of dipping cycles. The dipping temperature of the gelatin solution was adjusted to achieve low viscous fluids of approximately 0.1 Pa s and was different for gelatin derivatives with different modification degrees. A versatile perfusion bioreactor for the supply of surrounding tissue models was developed, which can be adaped to several geometries and sizes of blood-vessel mimicking tubes. The manufactured bendable gelatin tubes were permeable for water and dissolved substances, like Nile Blue and serum albumin. As a proof of concept, human fibroblasts in a three-dimensional collagen tissue model were sucessfully supplied with nutrients via the central gelatin tube under dynamic conditions for 2 days. Moreover, the tubes could be used as scaffolds to build-up a functional and viable endothelial layer. Hence, the presented tools can contribute to solving current challenges in tissue engineering.
Workflow driven support systems in the peri-operative area have the potential to optimize clinical processes and to allow new situation-adaptive support systems. We started to develop a workflow management system supporting all involved actors in the operating theatre with the goal to synchronize the tasks of the different stakeholders by giving relevant information to the right team members. Using the OMG standards BPMN, CMMN and DMN gives us the opportunity to bring established methods from other industries into the medical field. The system shows each addressed actor their information in the right place at the right time to make sure every member can execute their task in time to ensure a smooth workflow. The system has the overall view of all tasks. Accordingly, a workflow management system including the Camunda BPM workflow engine to run the models, and a middleware to connect different systems to the workflow engine and some graphical user interfaces to show necessary information or to interact with the system are used. The complete pipeline is implemented with a RESTful web service. The system is designed to include different systems like hospital information system (HIS) via the RESTful web service very easily and without loss of data. The first prototype is implemented and will be expanded.
Propofol is an intravenous anesthetic. Currently, it is not possible to routinely measure blood concentration of the drug in real time. However, multi-capillary column ion-mobility spectrometry of exhaled gas can estimate blood propofol concentration.Unfortunately, adhesion of volatile propofol on plastic materials complicates measurements. Therefore, it is necessary to consider the extent to which volatile propofol adheres to various plastics used in sampling tubing. Perfluoralkoxy (PFA), polytetrafluorethylene (PTFE), polyurethane (PUR), silicone, and Tygon tubing were investigated in an experimental setting using a calibration gas generator (HovaCAL). Propofol gas was measured for one hour at 26 °C, 50 °C, and 90 °C tubing temperature. Test tubing segments were then flushed with N2 to quantify desorption. PUR and Tygon sample tubing absorbed all volatile propofol. The silicone tubing reached the maximum propofol concentration after 119 min which was 29 min after propofol gas exposure stopped. The use of PFAor PTFE tubing produced comparable and reasonably accurate propofol measurements. The desaturation time for the PFA was 10 min shorter at 26 °C than for PTFE. PFA tubing thus seems most suitable for measurement of volatile propofol,with PTFE as an alternative.
Propofol in exhaled breath can be measured and may provide a real-time estimate of plasma concentration. However, propofol is absorbed in plastic tubing, thus estimates may fail to reflect lung/blood concentration if expired gas is not extracted directly from the endotracheal tube.We evaluated exhaled propofol in five ventilated ICU patients who were sedated with propofol. Exhaled propofol was measured once per minute using ion mobility spectrometry. Exhaled air was sampled directly from the endotracheal tube and at the ventilator end of the expiratory side of the anesthetic circuit. The circuit was disconnected from the patient and propofol was washed out with a separate clean ventilator. Propofol molecules, which discharged from the expiratory portion of the breathing circuit, were measured for up to 60 h.We also determined whether propofol passes through the plastic of breathing circuits. A total of 984 data pairs (presented as median values, with 95% confidence interval), consisting of both concentrations were collected. The concentration of propofol sampled near the patient was always substantially higher, at 10.4 [10.25–10.55] versus 5.73 [5.66–5.88] ppb (p<0.001). The reduction in concentration over the breathing circuit tubing was 4.58 [4.48–4.68] ppb, 3.46 [3.21–3.73] in the first hour, 4.05 [3.77–4.34] in the second hour, and 4.01 [3.36–4.40] in the third hour. Out-gassing propofol from the breathing circuit remained at 2.8 ppb after 60 h of washing out. Diffusion through the plastic was not observed. Volatile propofol binds or adsorbs to the plastic of a breathing circuit with saturation kinetics. The bond is reversible so propofol can be washed out from the plastic. Our data confirm earlier findings that accurate measurements of volatile propofol require exhaled air to be sampled as close as possible to the patient.
The workshop aims to discuss leading edge contributions to the interdisciplinary research area of ambient intelligence (AmI) applied to the domains of telemedicine and driving assistance. AmI refers to human centered environments attributed with sensors. The development of AmI in the two application domains of the workshop shares several commonalities: the extensive usage of networked devices and sensors, the design of artificial intelligence algorithms for diagnosis, including recommendation systems and qualitative reasoning or the application of mobile and wireless communication to their distributed systems. Together with the presentation of common aspects of Ambient Intelligence, a further goal of the workshop is to stimulate synergies among both application domains and present examples. The telemedicine domain can benefit from methodologies in designing complex devices, real-time conform system design, audiovisual or computer vision system design used in automotive driving assistance. Furthermore, the automotive domain can benefit from the usercentric view, biometric sensor data design, multi-user data bases for aggregation and diagnosis using big data like used in telemedicine. The German Government supports these research lines in its Hightec-Strategie under the domains “Health and Nutrition” and “Climate and Energy”. In Spain the term “Spanish Program for R&D Challenged Oriented Society – Challenge in energy safe, efficient and clean & Challenge in sustainable transport, smart and integrated” is used. Scientific contributions to the event are peer-reviewed by a suited program committee having members from Germany and Spain. The same committee is serving the JARCA workshop (Jornadas sobre Sistemas cualitativos y sus Aplicaciones en Diagnosis, Robótica e Inteligencia Ambiental - Conference on Qualitative Systems and their Applications in Diagnoses, Robotics and Ambient Intelligence) since 15 years. This workshop is sponsored by the German Academic Exchange Service (DAAD) under contract number 57070010.
Fragestellung: Das klinische Standardverfahren und Referenz der Schlafmessung und der Klassifizierung der einzelnen Schlafstadien ist die Polysomnographie (PSG). Alternative Ansätze zu diesem aufwändigen Verfahren könnten einige Vorteile bieten, wenn die Messungen auf eine komfortablere Weise durchgeführt werden. Das Hauptziel dieser Forschung Studie ist es, einen Algorithmus für die automatische Klassifizierung von Schlafstadien zu entwickeln, der ausschließlich Bewegungs- und Atmungssignale verwendet [1].
Patienten und Methoden: Nach der Analyse der aktuellen Forschungsarbeiten haben wir multinomiale logistische Regression als Grundlage für den Ansatz gewählt [2]. Um die Genauigkeit der Auswertung zu erhöhen, wurden vier Features entwickelt, die aus Bewegungs- und Atemsignalen abgeleitet wurden. Für die Auswertung wurden die nächtlichen Aufzeichnungen von 35 Personen verwendet, die von der Charité-Universitätsmedizin Berlin zur Verfügung gestellt wurden. Das Durchschnittsalter der Teilnehmer betrug 38,6 +/– 14,5 Jahre und der BMI lag bei durchschnittlich 24,4 +/– 4,9 kg/m2. Da der Algorithmus mit drei Stadien arbeitet, wurden die Stadien N1, N2 und N3 zum NREM-Stadium zusammengeführt. Der verfügbare Datensatz wurde strikt aufgeteilt: in einen Trainingsdatensatz von etwa 100 h und in einen Testdatensatz mit etwa 160 h nächtlicher Aufzeichnungen. Beide Datensätze wiesen ein ähnliches Verhältnis zwischen Männern und Frauen auf, und der durchschnittliche BMI wies keine signifikante Abweichung auf.
Ergebnisse: Der Algorithmus wurde implementiert und lieferte erfolgreiche Ergebnisse: die Genauigkeit der Erkennung von Wach-/NREM-/REM-Phasen liegt bei 73 %, mit einem Cohen’s Kappa von 0,44 für die analysierten 19.324 Schlafepochen von jeweils 30 s. Die beobachtete gewisse Überschätzung der NREM-Phase lässt sich teilweise durch ihre Prävalenz in einem typischen Schlafmuster erklären. Selbst die Verwendung eines ausbalancierten Trainingsdatensatzes konnte dieses Problem nicht vollständig lösen.
Schlussfolgerungen: Die erreichten Ergebnisse haben die Tauglichkeit des Ansatzes prinzipiell bestätigt. Dieser hat den Vorteil, dass nur Bewegungs- und Atemsignale verwendet werden, die mit weniger Aufwand und komfortabler für Benutzer aufgezeichnet werden können als z. B. Herz- oder EEG-Signale. Daher stellt das neue System eine deutliche Verbesserung im Vergleich zu bestehenden Ansätzen dar. Die Zusammenführung der beschriebenen algorithmischen Software mit dem in [1] beschriebenen Hardwaresystem zur Messung von Atem- und Körperbewegungssignalen zu einem autonomen, berührungslosen System zur kontinuierlichen Schlafüberwachung ist eine mögliche Richtung zukünftiger Arbeiten.
Informationstechnische Systeme, die den Arbeitsablauf im klinischen Bereich unterstützen, sind aktuell auf organisatorische Abläufe beschränkt. Diese Arbeit stellt einen ersten Ansatz vor, wie solch ein System in den perioperativen Bereich eingebracht werden kann. Hierzu wurde eine Workflow Engine mit einer perioperativen Prozess-Visualisierung verknüpft. Das System wurde nach Modell-View-Controller-Prinzip implementiert. Als "Controller" kommt die Workflow Engine zum Einsatz; also "Modell" ein Prozessmodell, mit den erforderlichen klinischen Daten. Der "View" wurde durch eine abgekoppelte Anwendung realisiert, welche auf Web-Technologien basiert. Drei Visualisierungen, die Workflow Engine sowie die Anbindung beider über eine Datenbankschnittstelle, wurden erfolgreich umgesetzt. Bei den drei Visualisierungen wurden jeweils eine Ansicht für den OP-Koordinator, den Springer und eine Ansicht für die Übersicht einer OP erstellt.
In dieser Arbeit werden Anforderungen an ein digitales Referenzmodell der Cell and Gene Therapy (CGT) Supply Chain mittels systematischer Literaturrecherche unter partieller Anwendung der Preferred-Reporting-Items-for-Systematic-Reviews-and-Meta-Analyses(PRISMA)-2020-Methode erarbeitet und erläutert. Die Ergebnisse der Literaturrecherche untermauern, dass die CGT Supply Chain standardisierte und automatisierte Prozesse benötigt, gewissen Transportanforderungen gerecht werden sowie eine lückenlose Rückverfolgbarkeit gewährleisten können muss. Die Anforderungen an das Referenzmodell lehnen sich z. T. an die Anforderungen des klassischen Supply-Chain-Operations-Reference(SCOR)-Modells an, bedürfen jedoch einer Veränderung und Weiterentwicklung unter Beachtung der Besonderheiten der CGT Supply Chain. Auf Basis eines Referenzmodells für die CGT Supply Chain, das die aus dieser Arbeit identifizierten Anforderungen beachtet, kann eine übergeordnete Managementplattform aufgebaut werden. Mit der digitalen Abbildung und Vernetzung aller Aktivitäten ist der Grundstein für die Integration in ein Enterprise-Resource-Planning(ERP)-System zum effektiven Data und Process Mining gelegt. Durch eine zunehmend bessere Datenqualität und -quantität entlang der Prozesse der CGT Supply Chain lassen sich verstärkt Informationen über die Prozesse selbst generieren, aus denen weitere Verbesserungsansätze hervorgehen. Eine CGT-Managementplattform bildet demnach die Grundlage für alle Prozesse innerhalb der CGT Supply Chain für einen kontinuierlichen Verbesserungsprozess.
Purpose: Medical processes can be modeled using different methods and notations.Currently used modeling systems like Business Process Model and Notation (BPMN) are not capable of describing the highly flexible and variable medical processes in sufficient detail.
Methods: We combined two modeling systems, Business Process Management (BPM) and Adaptive Case Management (ACM), to be able to model non-deterministic medical processes. We used the new Standards Case Management Model and Notation (CMMN) and Decision Management Notation (DMN).
Results: First, we explain how CMMN, DMN and BPMN could be used to model non-deterministic medical processes. We applied this methodology to model 79 cataract operations provided by University Hospital Leipzig, Germany, and four cataract operations provided by University Eye Hospital Tuebingen, Germany. Our model consists of 85 tasks and about 20 decisions in BPMN. We were able to expand the system with more complex situations that might appear during an intervention.
Conclusion: An effective modeling of the cataract intervention is possible using the combination of BPM and ACM. The combination gives the possibility to depict complex processes with complex decisions. This combination allows a significant advantage for modeling perioperative processes.
In this paper a method for the generation of gSPM with ontology-based generalization was presented. The resulting gSPM was modeled with BPMN/BPMNsix in an efficient way and could be executed with BPMN workflow engines. In the next step the implementation of resource concepts, anatomical structures, and transition probabilities for workflow execution will be realized.
Artefaktkorrektur und verfeinerte Metriken für ein EEG-basiertes System zur Müdigkeitserkennung
(2019)
Fragestellung: Müdigkeit ist ein oft unterschätztes, aber dennoch großes Problem im Straßenverkehr. Von rund 2,5 Mio. Verkehrsunfällen 2015 in Deutschland, waren 2898 Unfälle, mit insgesamt 59 Toten (~1,7 % der Todesfälle), auf Übermüdung zurückzuführen. Schätzungen gehen von einer Dunkelziffer von bis zu 20 % aus. In einer ersten eigenen Studie wurde überprüft, ob ein mobiles EEG in einem Fahrsimulator Müdigkeitszustände zuverlässig erkennen kann. Die Erkennungsrate lag lediglich bei 61 %. Ziel dieser Arbeit ist, das verwendete Messsystem zu verbessern. Dazu wird die Genauigkeit durch eine Artefaktkorrektur und mit Hilfe von verfeinerten Qualitätsmetriken erhöht. Eine erkannte Übermüdung wird dem Fahrer dann in angemessener Weise angezeigt, so dass er entsprechend reagieren kann.
Patienten und Methoden: Die Independent Component Analysis (ICA) ist ein multivariates Verfahren, um mehrere Zufallsvariablen zu analysieren. Für die Entscheidung, ob ein Fahrer gerade müde oder wach ist, wird der erstellte Merkmalsvektor für jede Sequenz mit ICA klassifiziert. Dafür wird ein trainierter Machine-Learning-Algorithmus eingesetzt, der in der Lage ist, auch unbekannte Datensätze in Klassen einzuteilen. Um die benötigten Frequenzwerte zu erhalten, wurde für jeden EEG-Kanal eine Fourier Transformation durchgeführt. Der erstellte Merkmalsvektor wird im nächsten Schritt durch ein Künstliches Neuronales Netz klassifiziert. Für das Training werden vorab erstellte Merkmalsvektoren mit den Klassen „Wach“ und „Müde“ versehen. Diese Daten werden zufällig gemischt und im Verhältnis 2:1 in eine Trainings- und Testmenge geteilt. Das Experiment wurde mit acht Personen mit jeweils zweimal 45 min Testfahrt durchgeführt.
Ergebnisse: Der komplette Datensatz besteht aus 150.000 Signalwerten, welche zu ca. 7000 Sequenzen zusammengefasst werden. Durch die Anwendung der Qualitätsmetrik bleiben 4370 Sequenzen für das Training übrig. Bei invaliden Sequenzen aufgrund von EEG-Artefakten gibt es deutliche Unterschiede. Im „Wach“ Zustand werden dreimal so viele Sequenzen verworfen als im „Müde“ Zustand. Insgesamt werden bei wachen Probanden im Schnitt ca. 50 % der Sequenzen verworfen, bei Müden lediglich 25 %. Im Durchschnitt erreicht das System eine Erkennungsrate von 73 % für beide Zustände. Vergleicht man nun das Verhältnis von „Wach“ und „Müde“ und lässt „Leichte Müdigkeit“ außen vor, liegen die Ergebnisse bei über 90 %.
Schlussfolgerungen: Die Ergebnisse zeigen, dass die Aufmerksamkeit während des Experiments abnimmt bzw. die Müdigkeit zunimmt. Dies verdeutlichen zum einen subjektive und objektive Beobachtungen von Müdigkeitsanzeichen. Zum anderen lassen sich messbare und klassifizierbare Unterschiede im EEG Signal nachweisen. Die als Merkmale eingesetzten Theta-Wellen zeigten eine niedrigere Amplitude gegen Ende des Experiments. Die Erweiterung der binären Klassifizierung führt zu einer weiteren Stabilisierung der Ergebnisse. Artefaktkorrektur und Qualitätsmetriken steigern die Güte der Daten weiter. Die entwickelte Anwendung zur Müdigkeitserkennung ermittelt messbare Zeichen von Müdigkeit und kann eine gute Entscheidung über die Fahrtauglichkeit treffen.
In order to ensure sufficient recovery of the human body and brain, healthy sleep is indispensable. For this purpose, appropriate therapy should be initiated at an early stage in the case of sleep disorders. For some sleep disorders (e.g., insomnia), a sleep diary is essential for diagnosis and therapy monitoring. However, subjective measurement with a sleep diary has several disadvantages, requiring regular action from the user and leading to decreased comfort and potential data loss. To automate sleep monitoring and increase user comfort, one could consider replacing a sleep diary with an automatic measurement, such as a smartwatch, which would not disturb sleep. To obtain accurate results on the evaluation of the possibility of such a replacement, a field study was conducted with a total of 166 overnight recordings, followed by an analysis of the results. In this evaluation, objective sleep measurement with a Samsung Galaxy Watch 4 was compared to a subjective approach with a sleep diary, which is a standard method in sleep medicine. The focus was on comparing four relevant sleep characteristics: falling asleep time, waking up time, total sleep time (TST), and sleep efficiency (SE). After evaluating the results, it was concluded that a smartwatch could replace subjective measurement to determine falling asleep and waking up time, considering some level of inaccuracy. In the case of SE, substitution was also proved to be possible. However, some individual recordings showed a higher discrepancy in results between the two approaches. For its part, the evaluation of the TST measurement currently does not allow us to recommend substituting the measurement method for this sleep parameter. The appropriateness of replacing sleep diary measurement with a smartwatch depends on the acceptable levels of discrepancy. We propose four levels of similarity of results, defining ranges of absolute differences between objective and subjective measurements. By considering the values in the provided table and knowing the required accuracy, it is possible to determine the suitability of substitution in each individual case. The introduction of a “similarity level” parameter increases the adaptability and reusability of study findings in individual practical cases.
Physicians in interventional radiology are exposed to high physical stress. To avoid negative long-term effects resulting from unergonomic working conditions, we demonstrated the feasibility of a system that gives feedback about unergonomic
situations arising during the intervention based on the Azure Kinect camera. The overall feasibility of the approach could be shown.
Checklists are a valuable tool to ensure process quality and quality of care. To ensure proper integration in clinical processes, it would be desirable to generate checklists directly from formal process descriptions. Those checklists could also be used for user interaction in context-aware surgical assist systems. We built a tool to automatically convert Business Process Model and Notation (BPMN) process models to checklists displayed as HTML websites. Gateways representing decisions are mapped to checklist items that trigger dynamic content loading based on the placed checkmark. The usability of the resulting system was positively evaluated regarding comprehensibility and end-user friendliness.
In Zusammenarbeit mit dem Medizinproduktehersteller ulrich medical wird eine User Experience und Usability Studie an der Software der im Moment eingesetzten Kontrastmittelinjektoren durchgeführt. Das Unternehmen möchte eine neue Variante eines Kontrastmittelinjektors entwickeln, der als Basis eine verbesserte Version dieser Softwares enthält. Benutzerstudien können mit den unterschiedlichsten Methoden durchgeführt werden. Das geeignete Vorgehen muss definiert und die Testpersonen in Bezug zur eingesetzten Methode ermittelt werden. Bei Medizinprodukten muss zusätzlich auf strikte Auflagen in Normen und Gesetzen geachtet werden. Die Grundlage zur Methodenauswahl bildet eine Recherche zu Usability und User Experience Vorgaben für Medizinprodukte. Die Studie wird anhand quantitativer Daten eines Usability Tests im Labor, Fragebögen zur User Experience und qualitativen Post Test- Interviews evaluiert. In erster Linie dient diese Studie der Ermittlung von möglichen Verbesserungen, welche in der darauf folgenden Masterthesis vertieft und umgesetzt werden.
This paper contributes to the automatic detection of perioperative workflow by developing a binary endoscope localization. Automated situation recognition in the context of an intelligent operating room requires the automatic conversion of low level cues into more abstract high level information. Imagery from a laparoscope delivers rich content that is easy to obtain but hard to process. We introduce a system which detects if the endoscope's distal tip is inside or outsiede the patient based on the endoscope video. This information can be used as one parameter in a situation recognition pipeline. Our localization performs in real-time at a video resolution of 1280x720 and 5-fold cross validation yields mean F1-scores of up to 0,94 on videos of 7 laparoscopies.
Background: One of the most promising health care development areas is introducing telemedicine services and creating solutions based on blockchain technology. The study of systems combining both these domains indicates the ongoing expansion of digital technologies in this market segment.
Objective: This paper aims to review the feasibility of blockchain technology for telemedicine.
Methods: The authors identified relevant studies via systematic searches of databases including PubMed, Scopus, Web of Science, IEEE Xplore, and Google Scholar. The suitability of each for inclusion in this review was assessed independently. Owing to the lack of publications, available blockchain-based tokens were discovered via conventional web search engines (Google, Yahoo, and Yandex).
Results: Of the 40 discovered projects, only 18 met the selection criteria. The 5 most prevalent features of the available solutions (N=18) were medical data access (14/18, 78%), medical service processing (14/18, 78%), diagnostic support (10/18, 56%), payment transactions (10/18, 56%), and fundraising for telemedical instrument development (5/18, 28%).
Conclusions: These different features (eg, medical data access, medical service processing, epidemiology reporting, diagnostic support, and treatment support) allow us to discuss the possibilities for integration of blockchain technology into telemedicine and health care on different levels. In this area, a wide range of tasks can be identified that could be accomplished based on digital technologies using blockchains.
Propofol is a commonly used intravenous general anesthetic. Multi-capillary column (MCC) coupled ion-mobility spectrometry (IMS) can be used to quantify exhaled propofol, and thus estimate plasma drug concentration. Here, we present results of the calibration and analytical validation of a MCC/IMS pre-market prototype for propofol quantification in exhaled air.
In the period from the 1950s to 2013, the American Food and Drug Administration (FDA) approved 1346 new molecular entities (NMEs) or new biologics entities (NBEs). On average, the approval rate was 20 NMEs per year. In the past 40 years, the number of new drugs launched into the market increased slightly from 15 NMEs in the 1970s to 25–30 NMEs since the 1990s. The highest number of new drugs approved by FDA was in 1996 and 1997, which might be related to the enactment of the Prescription Drug User Fee Act (PDUFA) in 1993.
An ongoing challenge in our days is to lower the impact on the quality of life caused by dysfunctionality through individual support. With the background of an aging society and continuous increases in costs for care, a holistic solution is needed. This solution must integrate individual needs and preferences, locally available possibilities, regional conditions, professional and informal caregivers and provide the flexibility to implement future requirements. The proposed model is a result of a common initiative to overcome the major obstacles and to center a solution on individual needs caused by dysfunctionality.
New drugs serving unmet medical needs are one of the key value drivers of research-based pharmaceutical companies. The efficiency of research and development (R&D), defined as the successful approval and launch of new medicines (output) in the rate of the monetary investments required for R&D (input), has declined since decades. We aimed to identify, analyze and describe the factors that impact the R&D efficiency. Based on publicly available information, we reviewed the R&D models of major research-based pharmaceutical companies and analyzed the key challenges and success factors of a sustainable R&D output. We calculated that the R&D efficiencies of major research-based pharmaceutical companies were in the range of USD 3.2–32.3 billion (2006–2014). As these numbers challenge the model of an innovation-driven pharmaceutical industry, we analyzed the concepts that companies are following to increase their R&D efficiencies: (A) Activities to reduce portfolio and project risk, (B) activities to reduce R&D costs, and (C) activities to increase the innovation potential. While category A comprises measures such as portfolio management and licensing, measures grouped in category B are outsourcing and risk-sharing in late-stage development. Companies made diverse steps to increase their innovation potential and open innovation, exemplified by open source, innovation centers, or crowdsourcing, plays a key role in doing so. In conclusion, research-based pharmaceutical companies need to be aware of the key factors, which impact the rate of innovation, R&D cost and probability of success. Depending on their company strategy and their R&D set-up they can opt for one of the following open innovators: knowledge creator, knowledge integrator or knowledge leverager.
To analyze the humans’ sleep it is necessary as to identify the sleep stages, occurring during the sleep, their durations and sleep cycles. The gold standard procedure for this approach is polysomnography (PSG), which classify the sleep stages based on Rechtschaffen and Kales (R-K) method. This method aside the advantages as high accuracy has however some disadvantages, among others time-consuming and uncomfortable for the patient procedure. Therefore, the development of further methods for the sleep classification in addition to PSG is a promising topic for the investigation and this work has as its aim the presentation of possible ways and goals for this development.
The potentials and opportunities created by digitized healthcare can be further customized through smart data processing and analysis using accurate patient information. This development and the associated new treatment concepts basing on digital smart sensors can lead to an increase in motivation by applying gamification approaches. This effect can also be used in the field of medical treatment, e.g. with the help of a digital spirometer combined with an app. In one of our exemplary applications, we show how to control an airplane within an app by breathing respectively inhaling and exhaling. Using this biofeedback within a game allows us to increase the motivation and fun for children that need to perform necessary exercises.
The metric and qualitative analysis of models of the upper and lower dental arches is an important aspect of orthodontic treatment planning. Currently available eLearning systems for dental education only allow access to digital learning materials, and do not interactively support the learning progress. Moreover, to date no study compared the efficiency of learning methods based on physical or digital study models. For this pilot study, 18 dental students were separated into two groups to investigate whether the learning success in study model analysis with an interactive elearning system is higher based on digital models or on conventional plaster models. The results show that with the digital method less time is needed per model analysis. Moreover, the digital approach leads to higher total scores than that based on plaster models. We conclude that interactive eLearning using digital dental arch models is a promising tool for dental education.
Background/Aim: The aim of this study was the development of a new osteoconductivity index to determine the bone healing capacities of bone substitute materials (BSM) on the basis of 3D microcomputed tomographic (μ-CT) data. Materials and Methods: Sinus biopsies were used for the comparative analysis of the integration behavior of two xenogeneic BSM (cerabone® and Bio Oss®). 3D μ-CT and data sets from histomorphometrical measurements based on 2D histological slices were used to measure the bone-material-contact and the tissue distribution within the biopsies. The tissue reactions to both BSM were microscopically analyzed. Results: The 3D and 2D results of the osteoconductivity measurements showed comparable material-bone contacts for both BSM, but the 2D data were significantly lower. The same results were found when tissue distribution was measured in both groups. The histopathological analysis showed comparative tissue reactions in both BSM. Conclusion: Osteoconductivity index is a reliable measurement parameter for determining the healing capacities of BSM. The observed differences between both measurement methods could be assigned to the resolution capacity of μ-CT data that did not allow for a precise interface distinction between both BSM and bone tissue. Histomorphometrical data based on histological slides still allow for a more exact evaluation.
Completely defined co-culture of adipogenic differentiated ASCs and microvascular endothelial cells
(2018)
Vascularized adipose tissue models are in high demand as alternatives to animal models to elucidate the mechanisms of widespread diseases, screen for new drugs or assess drug safety levels. Animal-derived sera such as fetal bovine serum (FBS), which are commonly used in these models, are associated with ethical concerns, risk of contaminations and inconsistencies of their composition and impact on cells. In this study, we developed a serum-free, defined co culture medium and implemented it in an adipocyte/endothelial cell (EC) co culture model.
Human adipose-derived stem cells were differentiated under defined conditions (diffASCs) and, like human microvascular ECs (mvECs), cultured in a defined co culture medium in mono-, indirect or direct co-culture for 14 days. The defined co-culture medium was superior when compared to mono-culture media and facilitated the functional maintenance and maturation of diffASCs including perilipin A expression, lipid accumulation, and also glycerol and leptin release. The medium also allowed mvEC maintenance, confirmed by the expression of CD31 and von Willebrand factor (vWF), and by acetylated low density lipoprotein (acLDL) uptake. Thereby, mvECs showed strong dependence on EC-specific factors. Additionally, mvECs formed vascular structures in direct co-culture with diffASCs.
The completely defined co-culture system allows for the serum-free culture of adipocyte/EC co-cultures and thereby represents a valuable and ethically acceptable tool for the culture and study of vascularized adipose tissue models.
Background aims: In vitro engineered adipose tissue is in great demand to treat lost or damaged soft tissue or to screen for new drugs, among other applications.However, today most attempts depend on the use of animal-derived sera. To pave the way for the application of adipose tissue-engineered
products in clinical trials or as reliable and robust in vitro test systems, sera should be completely excluded from the production process. In this study, we aimed to develop an in vitro adipose tissue model in the absence of sera and maintain its function long-term.
Methods: Human adipose tissue-derived stem cells were expanded and characterized in a xeno- and serum-free environment. Adipogenic differentiation was induced using a completely defined medium. Developed adipocytes were maintained in a completely defined maturation medium for additional 28 days. In addition to cell-viability and adherence, adipocyte-specific markers such as perilipin A expression of leptin release were evaluated.
Results: The defined differentiation medium enhanced cell adherence and lipid
accumulation at a significant level compared with the corresponding negative control. The defined maturation medium also significantly supported cell adherence and functional adipocyte maturation during the long-term culture period.
Conclusions: The process described here enables functional adipocyte generation and maintenance without the addition fo unknown or unimal-derived constituents, achieving an important milestone in the introduction of adipose tissue engineered products into clinical trials or in vitro screening.
In der Kryochirurgie wird Kälte verwendet, um tumoröses Gewebe abzutöten. Dazu werden Kryosonden in den Tumor gestochen und stark abgekühlt. Hierbei gibt es verschiedene Herausforderungen, welchen computergestützt begegnet werden kann. Diese Arbeit gibt die Ergebnisse einer Literaturrecherche zu den Herausforderungen wieder. Die vorgestellten Arbeiten beschäftigten sich mit der Simulation des im Tumor entstehenden Eisballs, dem korrekten Positionieren der Kryosonden im Tumor, dem Überwachen des Eingriffs sowie dem Entwickeln von Simulationen für Trainingszwecke. Dabei zeigt sich, dass der Einsatz von computergestützten Lösungen die Kryochirurgie für Operateur und Patient verbessern kann.
Context-aware systems to support actors in the operating room depending on the status of the intervention require knowledge about the current situation in the intra-operative area. In literature, solutions to achieve situation awareness already exist for specific use cases, but applicability and transferability to other conditions are less addressed. It is assumed that a unified solution that can be adapted to different processes and sensors would allow for greater flexibility, applicability, and thus transferability to different applications. To enable a flexible and intervention-independent system, this work proposes a concept for an adaptable situation recognition system. The system consists of four layers with several modular components for different functionalities. The feasibility is demonstrated via prototypical implementation and functional evaluation of a first basic framework prototype. Further development goal is the stepwise extension of the prototype.
Besides the optimisation of the car, energy-efficiency and safety can also be increased by optimising the driving behaviour. Based on this fact, a driving system is in development whose goal is to educate the driver in energy-efficient and safe driving. It monitors the driver, the car and the environment and gives energy-efficiency and safety relevant recommendations. However, the driving system tries not to distract or bother the driver by giving recommendations for example during stressful driving situations or when the driver is not interested in that recommendation. Therefore, the driving system monitors the stress level of the driver as well as the reaction of the driver to a given recommendation and decides whether to give a recommendation or not. This allows to suppress recommendations when needed and, thus, to increase the road safety and the user acceptance of the driving system.
In networked operating room environments, there is an emerging trend towards standardized non-proprietary communication protocols which allow to build new integration solutions and flexible human-machine interaction concepts. The most prominent endeavor is the IEEE 11073 SDC protocol. For some uses cases, it would be helpful if not just medical devices could be controlled based on SDC, but also building automation systems like light, shutters, air condition, etc. For those systems, the KNX protocol is widely used. We build an SDC-to-KNX gateway which allows to use the SDC protocol for sending commands to connected KNX devices. The first prototype system was successfully implemented at the demonstration operating room at Reutlingen University. This is a first step toward the integration of a broader variety of KNX devices.
Radiofrequency ablation is an ablation technique to treat tumors with focused heat. Computer tomography, ultrasound and magnetic resonance imaging (MRI) are imaging modalities which can be used for image-guided procedures. MRI offers several advantages in comparison to the other imaging modalities, such as radiation-free fluoroscopic imaging, temperature mapping, a high-soft-tissue contrast and free selection of imaging planes. This work addresses the application of 3Dcontrollers for controlling interventional, fluoroscopic MR sequences at the scenario of MR guided radiofrequency ablation of hepatic malignancies. During this procedure, the interventionalist can monitor the targeting of the tumor with near-real time fluoroscopic sequences. In general, adjustments of the imaging planes are necessary during tumor targeting, which is performed by an assistant in the control room. Therefore, communication between the interventionalist in the scanner room and the assistant in the control room is essential. However, verbal communication is impaired due to the loud scanning noises. Alternatively, non-verbal communication between the two persons is possible, however limited to a few gestures and susceptible to misunderstandings. This work is analyzing different 3D-controllers to enable control of interventional MR sequences during MR-guided procedures directly by the interventionalist. Leap Motion, Wii Remote, SpaceNavigator, Phantom Omni and Foot Switch were selected. For that a simulation was built in C++ with VTK to feign the real scenario for test purposes. Previous results showed that Leap Motion is not suitable for the application while Wii Remote and Foot Switch are possible input devices. Final evaluation showed a generally time reduction with the use of 3D-controllers. Best results were reached with Wii Remote in 34 seconds. Handholding input devices like Wii Remote have further potential to integrate them in real environment to reduce intervention time.
This paper investigates the possibility to effectively monitor and control the respiratory action using a very simple and non invasive technique based on a single lightweight reduced-size wireless surface electromyography (sEMG) sensor placed below the sternum. The captured sEMG signal, due to the critical sensor position, is characterized by a low energy level and it is affected by motion artifacts and cardiac noise. In this work we present a preliminary study performed on adults for assessing the correlation of the spirometry signal and the sEMG signal after the removal of the superimposed heart signal. This study and the related findings could be useful in respiratory monitoring of preterm infants.
The scoring of sleep stages is one of the essential tasks in sleep analysis. Since a manual procedure requires considerable human and financial resources, and incorporates some subjectivity, an automated approach could result in several advantages. There have been many developments in this area, and in order to provide a comprehensive overview, it is essential to review relevant recent works and summarise the characteristics of the approaches, which is the main aim of this article. To achieve it, we examined articles published between 2018 and 2022 that dealt with the automated scoring of sleep stages. In the final selection for in-depth analysis, 125 articles were included after reviewing a total of 515 publications. The results revealed that automatic scoring demonstrates good quality (with Cohen's kappa up to over 0.80 and accuracy up to over 90%) in analysing EEG/EEG + EOG + EMG signals. At the same time, it should be noted that there has been no breakthrough in the quality of results using these signals in recent years. Systems involving other signals that could potentially be acquired more conveniently for the user (e.g. respiratory, cardiac or movement signals) remain more challenging in the implementation with a high level of reliability but have considerable innovation capability. In general, automatic sleep stage scoring has excellent potential to assist medical professionals while providing an objective assessment.
Purpose: Gliomas are the most common and aggressive type of brain tumors due to their infiltrative nature and rapid progression. The process of distinguishing tumor boundaries from healthy cells is still a challenging task in the clinical routine. Fluid attenuated inversion recovery (FLAIR) MRI modality can provide the physician with information about tumor infiltration. Therefore, this paper proposes a new generic deep learning architecture, namely DeepSeg, for fully automated detection and segmentation of the brain lesion using FLAIR MRI data.
Methods: The developed DeepSeg is a modular decoupling framework. It consists of two connected core parts based on an encoding and decoding relationship. The encoder part is a convolutional neural network (CNN) responsible for spatial information extraction. The resulting semantic map is inserted into the decoder part to get the full-resolution probability map. Based on modified U-Net architecture, different CNN models such as residual neural network (ResNet), dense convolutional network (DenseNet), and NASNet have been utilized in this study.
Results: The proposed deep learning architectures have been successfully tested and evaluated on-line based on MRI datasets of brain tumor segmentation (BraTS 2019) challenge, including s336 cases as training data and 125 cases for validation data. The dice and Hausdorff distance scores of obtained segmentation results are about 0.81 to 0.84 and 9.8 to 19.7 correspondingly.
Conclusion: This study showed successful feasibility and comparative performance of applying different deep learning models in a new DeepSeg framework for automated brain tumor segmentation in FLAIR MR images. The proposed DeepSeg is open source and freely available at https://github.com/razeineldin/DeepSeg/.
Delphi Markets
(2023)
Delphi markets refer to approaches and implementations of integrating prediction markets and Delphi studies (Real-time Delphi). The combination of the two methods for producing forecasts can potentially compensate for each other´s weaknesses. For example, prediction markets can be used to select participants with expertise and also motivate long-term participation through their gamified approach and incentive mechanisms. In this paper, two potentials for prediction markets and four potentials for Delphi studies, which are made possible by integration, are derived theoretically. Subsequently, three different integration approaches are presented, on the basis of which the integration on user, market and Delphi question-level is exemplified and it is shown that, depending on the approach, not all potentials can be achieved. At the end, recommendations for the use of Delphi markets are derived, existing limitations for Delphi markets as well as future developments are pointed out.
There have been substantial research efforts for algorithms to improve continuous and automated assessment of various health-related questions in recent years. This paper addresses the deployment gap between those improving algorithms and their usability in care and mobile health applications. In practice, most algorithms require significant and founded technical knowledge to be deployed at home or support healthcare professionals. Therefore, the digital participation of persons in need of health care professionals lacks a usable interface to use the current technological advances. In this paper, we propose applying algorithms taken from research as web-based microservices following the common approach of a RESTful service to bridge the gap and make algorithms accessible to caregivers and patients without technical knowledge and extended hardware capabilities. We address implementation details, interpretation and realization of guidelines, and privacy concerns using our self-implemented example. Also, we address further usability guidelines and our approach to those.
Plasma polymerization is used for the modification and control of surface properties of a highly transparent, thermoplastic elastomeric silicone copolymer, GENIOMER® 80 (G80). PEG-like diglyme plasma polymer films were deposited with ether retentions varying between 20% and 70% as measured by X-ray photoelectron spectroscopy analysis which did not affect the transparency of the substrate. Films with ether retentions of greater than 70% inhibit protein binding (bovine serum albumin and fibrinogen) and cell proliferation. A short oxygen plasma pretreatment enhances the adhesion and stability of the film as shown by protein binding and cell adhesion experiments. The transparency of the material and the stability of the coating makes this material a versatile bulk material for technical (e.g., lab-on-a-chip) and biomedical (e.g., intraocular lens) applications. The G80/plasma polymer composite is stable against vigorous washing and storage over 5 months and, therefore, offers an attractive alternative to poly(dimethylsiloxane).
The aim of this work was to investigate the mean fill weight control of a continuous capsule-filling process, whether it is possible to derive controller settings from an appendant process model. To that end, a system composed out of fully automated capsule filler and an online gravimetric scale was used to control the filled weight. This setup allows to examine challenges associated with continuous manufacturing processes, such as variations in the amount of active pharmaceutical ingredient (API) in the mixture due to fluctuations of the feeders or due to altered excipient batch qualities. Two types of controllers were investigated: a feedback control and a combination of feedback and feedforward control. Although both of those are common in the industry, determining the optimal parameter settings remains an issue. In this study, we developed a method to derive the control parameters based on process models in order to obtain optimal control for each filled product. Determined via rapid automated process development (RAPD), this method is an effective and fast way of determining control parameters. The method allowed us to optimize the weight control for three pharmaceutical excipients. By conducting experiments, we verified the feasibility of the proposed method and studied the dynamics of the controlled system. Our work provides important basic data on how capsule filler can be implemented into continuous manufacturing systems.
An operating room is a stressful work environment. Nevertheless, all involved persons have to work safely as there is no space for mistakes. To ensure a high level of concentration and seamless interaction, all involved persons have to know their own tasks and the tasks of their colleagues. The entire team must work synchronously at all times. To optimize the overall workflow, a task manager supporting the team was developed. In parallel, a common conceptual design of a business process visualization was developed, which makes all relevant information accessible in real-time during a surgery. In this context an overview of all processes in the operating room was created and different concepts for the graphical representation of these user-dependent processes were developed. This paper describes the concept of the task manager as well as the general concept in the field of surgery.
An operation room is a stressful work environment. Nevertheless, all involved persons have to work safely as there is no space for making mistakes. To ensure a high level of concentration and seamless interaction, all involved persons have to know their own tasks and tasks of their colleagues. The entire team must work synchronously at all times. However, the operation room (OR) is a noisy environment and the actors have to set their focus on their work. To optimize the overall workflow, a task manager supporting the team was developed. Each actor is equipped with a client terminal showing a summary of their own tasks. Moreover, a big screen displays all tasks of all actors. The architecture is a distributed system based on a communication framework that supports the interaction of all clients with the task manager. A prototype of the task manager and several clients have been developed and implemented. The system represents a proof-of-concept for further development. This paper describes the concept of the task manager.
The focus of the developed maturity model was set on processes. The concept of the widespread CMM and its practices has been transferred to the perioperative domain and the concept of the new maturity model. Additional optimization goals and technological as well as networking-specific aspects enable a process- and object-focused view of the maturity model in order to ensure broad coverage of different subareas. The evaluation showed that the model is applicable to the perioperative field. Adjustments and extensions of the maturity model are future steps to improve the rating and classification of the new maturity model.
Recent advances in artificial intelligence have enabled promising applications in neurosurgery that can enhance patient outcomes and minimize risks. This paper presents a novel system that utilizes AI to aid neurosurgeons in precisely identifying and localizing brain tumors. The system was trained on a dataset of brain MRI scans and utilized deep learning algorithms for segmentation and classification. Evaluation of the system on a separate set of brain MRI scans demonstrated an average Dice similarity coefficient of 0.87. The system was also evaluated through a user experience test involving the Department of Neurosurgery at the University Hospital Ulm, with results showing significant improvements in accuracy, efficiency, and reduced cognitive load and stress levels. Additionally, the system has demonstrated adaptability to various surgical scenarios and provides personalized guidance to users. These findings indicate the potential for AI to enhance the quality of neurosurgical interventions and improve patient outcomes. Future work will explore integrating this system with robotic surgical tools for minimally invasive surgeries.
Health monitoring in a home environment can have broader use since it may provide continuous control of health parameters with relatively minor intrusiveness into regular life. This work aims to verify if it is possible to replace the typical in some sleep medicine areas subjective questioning by an objective measurement using electronic devices. For this purpose, a study was conducted with ten subjects, in which objective and subjective measurement of relevant sleep parameters took place. The results of both measurement methods were evaluated and analyzed. The results showed that while for some measures, such as Total Time in Bed, there is a high agreement between objective and subjective measurements, for others, such as sleep quality, there are significant differences. For this reason, currently, a combination of both measurement methods may be beneficial and provide the most detailed results, while a partial replacement can already reduce the number of questions at the subjective measurement by measurement through electronic devices.
Die rasante Entwicklung der Sensortechnik im Endverbraucherbereich lässt einen klinischen Nutzen der verfügbaren dezentral erhobenen Daten aus dem Patientenalltag zur Überwachung des individuellen Gesundheitszustands vermuten. Zur Überprüfung dieser Vermutung ist die Bereitstellung einer entsprechenden Plattform in den klinischen Alltag erforderlich. Hierzu wird die bwHealthApp entwickelt, mit der sowohl die aktuelle Bandbreite als auch die Evolution der Sensortechnik auf die klinische Anwendung abbildbar ist. Mit dem flexiblen Entwurf lässt sich der klinische Nutzen für die personalisierte Medizin evaluieren. Außerdem bietet die bwHealthApp einen an Machbarkeit orientierten Diskussionsbeitrag zu offenen rechtlichen, regulatorischen und ethischen Fragestellungen der Digitalisierung in der Medizin in Deutschland.
Salivary gland tumors (SGTs) are a relevant, highly diverse subgroup of head and neck tumors whose entity determination can be difficult. Confocal Raman imaging in combination with multivariate data analysis may possibly support their correct classification. For the analysis of the translational potential of Raman imaging in SGT determination, a multi-stage evaluation process is necessary. By measuring a sample set of Warthin tumor, pleomorphic adenoma and non-tumor salivary gland tissue, Raman data were obtained and a thorough Raman band analysis was performed. This evaluation revealed highly overlapping Raman patterns with only minor spectral differences. Consequently, a principal component analysis (PCA) was calculated and further combined with a discriminant analysis (DA) to enable the best possible distinction. The PCA-DA model was characterized by accuracy, sensitivity, selectivity and precision values above 90% and validated by predicting model-unknown Raman spectra, of which 93% were classified correctly. Thus, we state our PCA-DA to be suitable for parotid tumor and non-salivary salivary gland tissue discrimination and prediction. For evaluation of the translational potential, further validation steps are necessary.
In 2017, Philips' goal was to use innovation to improve the lives of three billion people a year by 2025. To achieve that, the company was shifting from selling medical products in a transactional manner to providing integrated healthcare solutions based on digital health technology. Based on our interviews with 23 executives at Philips, the case examines the two directions of the transformation required by this shift: externally, Philips worked on transforming how healthcare was conducted. Healthcare professionals would have to change the way they worked and reimbursement schemes needed to change to incentivize payers, providers, and patients in vastly different ways. Internally, Philips needed to redesign how its employees worked. The company componentized its business, introduced digital platforms, and co-created integrated solutions with the various stakeholders of the healthcare industry. In other words: Philips was transforming itself in order the reinvent healthcare in the digital age.
Mammographie-Geräte werden in der Diagnostik von Mammakarzinomen eingesetzt. Die ursprüngliche Technik wurde in den letzten Jahren von analogen Röntgenfilmen zu digital integrierten Systemen weiterentwickelt. Durch die Tomosynthese, bei der in einem Schnittbildverfahren mehrere Schichten des Organismus untersucht werden können, können auch überlagerte Strukturen sichtbar gemacht werden. Um als adäquate Grundlage zur Diagnostik von malignen Tumoren dienen zu können, müssen einige qualitative Anforderungen erfüllt werden. Bisher gibt es wenig Literatur, die Anforderungen und den Aufbau solcher Geräte systematisch beschreiben. Im Rahmen dieser Arbeit werden auf Basis der Literatur und bestehender Systeme die qualitativen Anforderungen identifiziert. Der prinzipielle Aufbau solcher Systeme wird anhand der einzelnen Systembausteine in der semiformalen Notationssprache SysML gezeigt. Die grundlegende Funktionsweise eines tomosynthesefähigen Mammographie Gerätes wird in dieser Arbeit zusammenfassend und anhand der einzelnen Systembausteine beschrieben. Diese Arbeit dient der Vermittlung eines umfassenden Verständnisses für die digitale Mammographie, um als Grundlage für die Dokumentation von qualitativen Anforderungen dienen zu können.
The field of breath analysis has developed to be of growing interest in medical diagnosis and patient monitoring. The main advantages are that it’s noninvasive, painless and repeatable in flexible cycles. Even though breath analysis is being researched for a couple of decades there are still many unanswered questions. Human breath contains volatile organic compounds which are emitted from inside the body. Some of these compounds can be assigned to specific sources, such as inflammation or cancer, but also to non health related origins. This paper gives an overview of breath analysis for the purpose of disease diagnosis and health monitoring. Therefore, literature regarding breath analysis in the medical field has been analyzed, from its early stages to the present. As a result, this paper gives an outline of the topic of breath analysis.
Im Fokus der Arbeit steht die Unterstützung der Stentgraftauswahl bei endovaskulärer Versorgung eines infrarenalen Aortenaneurysmas. Im Rahmen der Arbeit wurde eine Methode zur Auswertung von Ergebnissen einer Finite Elemente-Analyse zum Stentgraftverhalten konzipiert, implementiert und im Rahmen einer deutschlandweiten Benutzerstudie mit 16 Chirurgen diskutiert. Die entwickelte Mensch-Maschine-Schnittstelle ermöglicht dem Gefäßmediziner eine interaktive Analyse berechneter Fixierungskräfte und Kontaktzustände mehrerer Stentgrafts im Kontext mit dem zu behandelnden Aortenabschnitt. Die entwickelte Methode ermöglicht eine tiefergehende Auseinandersetzung der Mediziner mit numerischen Simulationen und Stentgraftbewertungsgrößen. Hierdurch konnte im Rahmen der Benutzerstudie das Einsatzpotenzial numerischer Simulationen zur Unterstützung der Stentgraftauswahl ermittelt und eine Anforderungsspezifikation an ein System zur simulationsbasierten Stentgraftplanung definiert werden. Im Ergebnis wurde als wesentliches Einsatzpotenzial die Festlegung eines Mindestmaßes an Überdimensionierung, die Optimierung der Schenkellänge von bifurkativen Stentgrafts sowie der Vergleich unterschiedlicher Stentgraftdesigns ermittelt. Zu den wesentlichen Funktionen eines Systems zur simulationsbasierten Stentgraftauswahl gehören eine Übersichtskarte zu farbkodiertem Migrationsrisiko pro Stentgraft und Landungszone, die Visualisierung des Abdichtungszustandes der Stentkomponenten sowie die Darstellung von Stentgraft- und Gefäßdeformationen im 3D-Modell.
It is well established that the mechanical environment influences cell functions in health and disease. Here, we address how the mechanical environment influences tumor growth, in particular, the shape of solid tumors. In an in vitro tumor model, which isolates mechanical interactions between cancer tumor cells and a hydrogel, we find that tumors grow as ellipsoids, resembling the same, oft-reported observation of in vivo tumors. Specifically, an oblate ellipsoidal tumor shape robustly occurs when the tumors grow in hydrogels that are stiffer than the tumors, but when they grow in more compliant hydrogels they remain closer to spherical in shape. Using large scale, nonlinear elasticity computations we Show that the oblate ellipsoidal shape minimizes the elastic free energy of the tumor-hydrogel system. Having eliminated a number of other candidate explanations, we hypothesize that minimization of the elastic free energy is the reason for predominance of the experimentally observed ellipsoidal shape. This result may hold significance for explaining the shape progressio.
Information and communication technologies support telemedicine to lower health access barriers and to provide better health care. While the potential in Active Assisted Living (AAL) is increasing, it is difficult to evaluate its benefits for the user, and it requires coordinated actions to launch it. The European Commission’s action plan 2012–2020 provides a roadmap to patient empowerment and healthcare, to link up devices and technologies, and to invest in research towards the personalized medicine of the future. As a quickly developing area in medicine, telemonitoring is a demanding field in research and development. Telemonitoring is an essential component of personalized medicine, where health providers can obtain precise information on outcare or chronic patients to improve diagnosis and therapy and also help healthy persons with prevention support. Telemonitoring combines mobile and wearable devices with the personal AAL home environment, a private or (partly) supervised home, most often called ’smart home’. The focus of this workshop is on new hardware and software solutions specifically designed to be applicable in AAL environments to empower patients. This workshop presents system-oriented solutions covering wearable and AAL-embedded devices, computer science infrastructure both at the users’ and the medical premises, to handle the data and decision support systems to support diagnose and treatment.
This paper presents a generic method to enhance performance and incorporate temporal information for cardiorespiratory-based sleep stage classification with a limited feature set and limited data. The classification algorithm relies on random forests and a feature set extracted from long-time home monitoring for sleep analysis. Employing temporal feature stacking, the system could be significantly improved in terms of Cohen’s κ and accuracy. The detection performance could be improved for three classes of sleep stages (Wake, REM, Non-REM sleep), four classes (Wake, Non-REM-Light sleep, Non-REM Deep sleep, REM sleep), and five classes (Wake, N1, N2, N3/4, REM sleep) from a κ of 0.44 to 0.58, 0.33 to 0.51, and 0.28 to 0.44 respectively by stacking features before and after the epoch to be classified. Further analysis was done for the optimal length and combination method for this stacking approach. Overall, three methods and a variable duration between 30 s and 30 min have been analyzed. Overnight recordings of 36 healthy subjects from the Interdisciplinary Center for Sleep Medicine at Charité-Universitätsmedizin Berlin and Leave-One-Out-Cross-Validation on a patient-level have been used to validate the method.
Glioblastomas are the most aggressive fast-growing primary brain cancer which originate in the glial cells of the brain. Accurate identification of the malignant brain tumor and its sub-regions is still one of the most challenging problems in medical image segmentation. The Brain Tumor Segmentation Challenge (BraTS) has been a popular benchmark for automatic brain glioblastomas segmentation algorithms since its initiation. In this year, BraTS 2021 challenge provides the largest multi-parametric (mpMRI) dataset of 2,000 pre-operative patients. In this paper, we propose a new aggregation of two deep learning frameworksnamely, DeepSeg and nnU-Net for automatic glioblastoma recognition in pre-operative mpMRI. Our ensemble method obtains Dice similarity scores of 92.00, 87.33, and 84.10 and Hausdorff Distances of 3.81, 8.91, and 16.02 for the enhancing tumor, tumor core, and whole tumor regions, respectively, on the BraTS 2021 validation set, ranking us among the top ten teams. These experimental findings provide evidence that it can be readily applied clinically and thereby aiding in the brain cancer prognosis, therapy planning, and therapy response monitoring. A docker image for reproducing our segmentation results is available online at (https://hub.docker.com/r/razeineldin/deepseg21).
The scoring of sleep stages is an essential part of sleep studies. The main objective of this research is to provide an algorithm for the automatic classification of sleep stages using signals that may be obtained in a non-obtrusive way. After reviewing the relevant research, the authors selected a multinomial logistic regression as the basis for their approach. Several parameters were derived from movement and breathing signals, and their combinations were investigated to develop an accurate and stable algorithm. The algorithm was implemented to produce successful results: the accuracy of the recognition of Wake/NREM/REM stages is equal to 73%, with Cohen's kappa of 0.44 for the analyzed 19324 sleep epochs of 30 seconds each. This approach has the advantage of using the only movement and breathing signals, which can be recorded with less effort than heart or brainwave signals, and requiring only four derived parameters for the calculations. Therefore, the new system is a significant improvement for non-obtrusive sleep stage identification compared to existing approaches.
Die prä-, intra- und postoperative Entitäts- und Dignitätsbestimmung von Speicheldrüsen-tumoren (ST) allein anhand von histomorphologischen Kriterien ist häufig mit großen Unsicherheiten verbunden.
Die Spektren der Raman-Spektroskopie (RS) und der Infrarot-Spektroskopie (IS) enthalten Informationen zu der molekularen Zusammensetzung des untersuchten Gewebes. Ziel der Arbeit war die Etablierung eines Gewebe-Aufarbeitungs-Workflows und die Analyse des Einflusses der Fixierung auf die spektrale Bioinformation. Zudem wird ein Überblick über den Einsatz der RS und IS im Kopf-Hals Bereich gegeben.
Es wurden 10 mm dicke, konsekutive kryo-, formalin- und paraffinfixierte ST-Gewebeschnitte von Zystadenolymphomen (n=5) und pleomorphen Adenomen (n=4) mit der RS und IS untersucht und die Daten multivariat ausgewertet. Die Messungen erfolgten in Korrelation zur Histomorphologie über einen korrespondierenden HE-Schnitt sowohl im Tumorgewebe als auch im gesunden Speicheldrüsengewebe.
In der Mittelwertspektrenanalyse zeigte sich eine deutliche Paraffin-Signatur, Formalin-Fixierung hatte keinen wesentlichen Einfluss. Dies konnte durch die Hauptkomponentenanalyse (PCA) bestätigt werden. Eine Diskriminierung von Tumor- und Nicht-Tumorgewebe durch die PCA und gekoppelte Diskriminanzanalyse war ebenfalls mit beiden spektroskopischen Methoden mit einer hohen Sensitivität möglich.
Für eine Translation von spektralen Verfahren ist das Wissen über Einflussfaktoren auf die spektrale Bioinformation der Gewebeaufarbeitung und -fixierung unabdingbar. Die Integration spektraler Verfahren additiv in bestehende Arbeitsabläufe ist möglich. Der Einfluss der Formalinfixierung auf die spektrale Bioinformation ist gering. Die bioinformatische Analyse der umfangreichen Datensätze ist herausfordernd.
IZKF Würzburg
Recognition of sleep and wake states is one of the relevant parts of sleep analysis. Performing this measurement in a contactless way increases comfort for the users. We present an approach evaluating only movement and respiratory signals to achieve recognition, which can be measured non-obtrusively. The algorithm is based on multinomial logistic regression and analyses features extracted out of mentioned above signals. These features were identified and developed after performing fundamental research on characteristics of vital signals during sleep. The achieved accuracy of 87% with the Cohen’s kappa of 0.40 demonstrates the appropriateness of a chosen method and encourages continuing research on this topic.
Evaluation of a contactless accelerometer sensor system for heart rate monitoring during sleep
(2024)
The monitoring of a patient's heart rate (HR) is critical in the diagnosis of diseases. In the detection of sleep disorders, it also plays an important role. Several techniques have been proposed, including using sensors to record physiological signals that are automatically examined and analysed. This work aims to evaluate using a contactless HR monitoring system based on an accelerometer sensor during sleep. For this purpose, the oscillations caused by chest movements during heart contractions are recorded by an installation mounted under the bed mattress. The processing algorithm presented in this paper filters the signals and determines the HR. As a result, an average error of about 5 bpm has been documented, i.e., the system can be considered to be used for the forecasted domain.
Die digitale Transformation und gesellschaftliche Entwicklungen verändern die Arbeitswelt nicht erst seit der Corona-Pandemie. Kommunikation, Kreativität und agile Vorgehensweisen in der Arbeitsorganisation rücken in den Vordergrund und werden gerade in Krisenzeiten zu wichtigen Stärken von Unternehmen. Der Grad der Selbstorganisation von Teams steigt und erfordert mehr individuelle Selbstorganisation der Beschäftigten. Dies birgt neben vielen Chancen auch Gesundheitsrisiken.
Der Report beleuchtet die agile Organisation und weitere moderne Organisationsmodelle wie die Soziokratie, die Holokratie und die evolutionäre Organisation unter dem Gesundheitsaspekt. All diese Organisationsmodelle sind gekennzeichnet durch die Abflachung von Hierarchien, eine stärkere Sinnorientierung, mehr Flexibilität sowie die Integration von Leistungspotentialen der Beschäftigten. Die Gemeinsamkeiten, aber auch Unterschiede und Konfliktpotenziale werden ausführlich erklärt.
Mit diesem Hintergrundwissen können Beratende Gesundheitsthemen besser in Phasen gesundheitlicher Belastungen einbringen und richtig adressieren. Der iga.Report 44 gibt einen Überblick zum noch jungen Stand der Forschung und liefert zahlreiche Ansatzpunkte für die Präventionsarbeit und die Betriebliche Gesundheitsförderung in einer neuen Arbeitswelt.
The analysis of exhaled metabolites has become a promising field of research in recent decades. Several volatile organic compounds reflecting metabolic disturbance and nutrition status have even been reported. These are particularly important for long-term measurements, as needed in medical research for detection of disease progression and therapeutic efficacy. In this context, it has become urgent to investigate the effect of fasting and glucose treatment for breath analysis. In the present study, we used amodel of ventilated rats that fasted for 12 h prior to the experiment. Ten rats per group were randomly assigned for continuous intravenous infusion without glucose or an infusion including 25 mg glucose per 100 g per hour during an observation period of 12 h. Exhaled gas was analysed using multicapillary column ion-mobility spectrometry. Analytes were identified by the BS-MCC/IMS database (version 1209; B & S Analytik, Dortmund, Germany). Glucose infusion led to a significant increase in blood glucose levels (p<0.05 at 4 h and thereafter) and cardiac output (p<0.05 at 4 h and thereafter). During the observation period, 39 peaks were found collectively. There were significant differences between groups in the concentration of ten volatile organic compounds: p<0.001 at 4 h and thereafter for isoprene, cyclohexanone, acetone, p-cymol, 2-hexanone, phenylacetylene, and one unknown compound, and p<0.001 at 8 h and thereafter for 1-pentanol, 1-propanol, and 2-heptanol. Our results indicate that for long-term measurement, fasting and the withholding of glucose could contribute to changes of volatile metabolites in exhaled air.
Background: Conventional methods for lung cancer detection including computed tomography (CT) and bronchoscopy are expensive and invasive. Thus, there is still a need for an optimal lung cancer detection technique. Methods: The exhaled breath of 50 patients with lung cancer histologically proven by bronchoscopic biopsy samples (32 adenocarcinomas, 10 squamous cell carcinomas, 8 small cell carcinomas), were analyzed using ion mobility spectrometry (IMS) and compared with 39 healthy volunteers. As a secondary assessment, we compared adenocarcinoma patients with and without epidermal growth factor receptor (EGFR) mutation. Results: A decision tree algorithm could separate patients with lung cancer including adenocarcinoma, squamous cell carcinoma and small cell carcinoma. One hundred-fifteen separated volatile organic compound (VOC) peaks were analyzed. Peak-2 noted as n-Dodecane using the IMS database was able to separate values with a sensitivity of 70.0% and a specificity of 89.7%. Incorporating a decision tree algorithm starting with n-Dodecane, a sensitivity of 76% and specificity of 100% was achieved. Comparing VOC peaks between adenocarcinoma and healthy subjects, n-Dodecane was able to separate values with a sensitivity of 81.3% and a specificity of 89.7%. Fourteen patients positive for EGFR mutation displayed a significantly higher n-Dodecane than for the 14 patients negative for EGFR (p<0.01), with a sensitivity of 85.7% and a specificity of 78.6%. Conclusion: In this prospective study, VOC peak patterns using a decision tree algorithm were useful in the detection of lung cancer. Moreover, n-Dodecane analysis from adenocarcinoma patients might be useful to discriminate the EGFR mutation.
Purpose
Artificial intelligence (AI), in particular deep neural networks, has achieved remarkable results for medical image analysis in several applications. Yet the lack of explainability of deep neural models is considered the principal restriction before applying these methods in clinical practice.
Methods
In this study, we propose a NeuroXAI framework for explainable AI of deep learning networks to increase the trust of medical experts. NeuroXAI implements seven state-of-the-art explanation methods providing visualization maps to help make deep learning models transparent.
Results
NeuroXAI has been applied to two applications of the most widely investigated problems in brain imaging analysis, i.e., image classification and segmentation using magnetic resonance (MR) modality. Visual attention maps of multiple XAI methods have been generated and compared for both applications. Another experiment demonstrated that NeuroXAI can provide information flow visualization on internal layers of a segmentation CNN.
Conclusion
Due to its open architecture, ease of implementation, and scalability to new XAI methods, NeuroXAI could be utilized to assist radiologists and medical professionals in the detection and diagnosis of brain tumors in the clinical routine of cancer patients. The code of NeuroXAI is publicly accessible at https://github.com/razeineldin/NeuroXAI.
Purpose
Artificial intelligence (AI), in particular deep learning (DL), has achieved remarkable results for medical image analysis in several applications. Yet the lack of human-like explanations of such systems is considered the principal restriction before utilizing these methods in clinical practice (Yang, Ye, & Xia, 2022).
Methods
Explainable Artificial Intelligence (XAI) provides a human-explainable and interpretable description of the “black-box” nature of DL (Gulum, Trombley, & Kantardzic, 2021). An effective XAI diagnosis generator, namely NeuroXAI (refer to Fig. 1), has been developed to extract 3D explanations from convolutional neural networks (CNN) models of brain gliomas (Zeineldin et al., 2022). By providing visual justification maps, NeuroXAI can help make DL models transparent and thus increase the trust of medical experts.
Results
NeuroXAI has been applied to two applications of the most widely investigated problems in brain imaging analysis, i.e. image classification and segmentation using magnetic resonance imaging (MRI). Visual attention maps of multiple XAI methods have been generated and compared for both applications, which could help to provide transparency about the performance of DL systems.
Conclusion
NeuroXAI helps to understand the prediction process of 3D CNN networks for brain glioma using human-understandable explanations. Results revealed that the investigated DL models behave in a logical human-like manner and can improve the analytical process of the MRI images systematically. Due to its open architecture, ease of implementation, and scalability to new XAI methods, NeuroXAI could be utilized to assist medical professionals in the detection and diagnosis of brain tumors. NeuroXAI code is publicly accessible at https://github.com/razeineldin/NeuroXAI
Rational behavior is a standard assumption in science. Indeed, rationality is required for environmental action towards net-zero emissions or public health interventions during the SARS-CoV-2 pandemic. Yet, little is known about the elements of rationality. This paper explores a dualism of rationality comprised of optimality and consistency. By designing a new guessing game, we experimentally uncover and disentangle two building blocks of human rationality: the notions of optimality and consistency. We find evidence that rationality is largely associated to optimality and weakly to consistency. Remarkably, under uncertainty, rationality gradually shifts to a heuristic notion. Our findings provide insights to better understand human decision making.
From raw ion mobility measurements to disease classification : a comparison of analysis processes
(2015)
Ion mobility spectrometry (IMS) is a technology for the detection of volatile compounds in the air of exhaled breath that is increasingly used in medical applications. One major goal is to classify patients into disease groups, for example diseased versus healthy, from simple breath samples. Raw IMS measurements are data matrices in which peak regions representing the compounds have to be identified and quantified. A typical analysis process consists of pre-processing and peak detection in single experiments, peak clustering to obtain consensus peaks across several experiments, and classification of samples based on the resulting multivariate peak intensities. Recently several automated algorithms for peak detection and peak clustering have been introduced, in order to overcome the current need for human-based analysis that is slow, subjective and sometimes not reproducible. We present an unbiased comparison of a multitude of combinations of peak processing and multivariate classification algorithms on a disease dataset. The specific combination of the algorithms for the different analysis steps determines the classification accuracy, with the encouraging result that certain fully-automated combinations perform even better than current manual approaches.
Knee osteoarthritis is a common complication and can lead to total loss of joint function in patients. Treatment by either partial or total knee replacement with appropriate UHMWPE based implantsis highly invasive, may cause complications and may show unsatisfying results. Alternatively, treatment may be done by insertion of an elastic interpositional knee spacer with optimized material characteristics.
We report the development of high performance polyurethane-based polymers modified with bioactive molecules for fabrication of such knee spacers. In order to tailor mechanical and tribological properties and to improve resist to enzymatic degradation we propose a core-shell model for the spacer with specifically adapted properties.
Gamification is one of the recognized methods of motivating people in various life processes, and it has spread to many spheres of life, including healthcare. This article proposes a system design for long-term care patients using the method mentioned. The proposed system aims to increase patient engagement in the treatment and rehabilitation process via gamification. Literature research on available and earlier proposed systems was conducted to develop a suited system design. The primary target group includes bedridden patients and a sedentary lifestyle (predominantly lying in bed). One of the main criteria for selecting a suitable option was its contactless realization for the mentioned target groups in long-term care cases. As a result, we developed the system design for hardware and software that could prevent bedsores and other health problems from occurring because of low activity. The proposed design can be tested in hospitals, nursing homes, and rehabilitation centers.
Gender Marketing gewinnt sowohl in der Marketing-Theorie als auch in der Unternehmenspraxis zunehmend an Bedeutung. Der Unterschied zwischen den Geschlechtern zeigt sich nicht nur in unterschiedlichen Fähigkeiten und Einstellungen, sondern auch in verschiedenen Bedürfnissen und im Kaufverhalten. Viele Produkte werden von Männern für Männer entwickelt. Produkte, die sich speziell an Frauen richten, werden häufig gemäß dem Motto „pink it and shrink it“ auf den Markt gebracht. Eine erfolgreiche Umsetzung von Gender-Aspekten ist für Unternehmen eine wichtige Marketing-Herausforderung für die Zukunft.
Medical applications are becoming increasingly important in the current development of health care and therefore a crucial part of the medical industry. An essential component is the development of user interfaces for mobile medical applications. The conceptual process is crucial for the further development of the main development process. Inconsistency or errors in the conceptual phase, have a serious impact on all areas and could prevent the certification for market approval.
This paper presents a guide to support developer with this process. It was developed based on a requirement analysis of the legal requirements to publish a medical device.
Haptisches Feedback ist nach zahlreichen Studien ein wichtiger Bestandteil in der medizinischen Robotik. Die meisten Systeme befinden sich jedoch noch im Forschungsstatus und verfolgen unterschiedliche Ansätze. In der Teleoperation wird mit sensorlosen und Sensor-Systemen geforscht. Sensoren bieten, im Gegensatz zu den Encodern in sensorlosen Systemen, genaue Messungen, sind allerdings teuer in der Anschaffung, schwer zu desinfizieren und müssen in OP-Besteck integriert werden. In Hands-On Systemen fühlt der Operateur im Gegensatz zu Teleoperationssystemen direkt die auftretenden Kräfte bei der Benutzung. Der Roboter bietet in diesen Systemen nur die benötigte Stabilität und Genauigkeit, gesteuert werden sie direkt durch den Menschen. Dagegen werden in Teleoperationssystemen gezielte Controller eingesetzt. Hier hat sich der für den OP entwickelte sigma.7 durchgesetzt. Gegenüber der für die Allgemeinheit entwickelten Konkurrenz bietet er haptisches Feedback in allen nötigen Freiheitsgraden und eine entsprechende Kraftrückkoppelung.
Die DGCH registriert vermehrt Klagen aus der klinischen Praxis hinsichtlich der nicht vollständigen Vernetzung bzw. Integration von Gerätesystemen im Chirurgischen OP. Die Anzahl, der Funktionsumfang und der Komplexitätsgrad der verwendeten Geräte nehmen ständig zu und machen die Bedienung immer aufwendiger und damit schwieriger und fehleranfälliger, sodass eine Verbesserung bei der Unterstützung im Ablauf wünschenswert ist. Die Sektion Computer- und telematikassistierte Chirurgie (CTAC) der DGCH hat es auf Veranlassung des Generalsekretärs deshalb übernommen, eine aktuelle Bestandsaufnahme vorzunehmen und mögliche Ansätze zur Verbesserung des derzeitigen Status zu bewerten.
In many cases continuous monitoring of vital signals is required and low intrusiveness is an important requirement. Incorporating monitoring systems in the hospital or home bed could have benefits for patients and caregivers. The objective of this work is the definition of a measurement protocol and the creation of a data set of measurements using commercial and low-cost prototypes devices to estimate heart rate and breathing rate. The experimental data will be used to compare results achieved by the devices and to develop algorithms for feature extraction of vital signals.
The ballistocardiography is a technique that measures the heart rate from the mechanical vibrations of the body due to the heart movement. In this work a novel noninvasive device placed under the mattress of a bed estimates the heart rate using the ballistocardiography. Different algorithms for heart rate estimation have been developed.
Accurate monitoring of a patient's heart rate is a key element in the medical observation and health monitoring. In particular, its importance extends to the identification of sleep-related disorders. Various methods have been established that involve sensor-based recording of physiological signals followed by automated examination and analysis. This study attempts to evaluate the efficacy of a non-invasive HR monitoring framework based on an accelerometer sensor specifically during sleep. To achieve this goal, the motion induced by thoracic movements during cardiac contractions is captured by a device installed under the mattress. Signal filtering techniques and heart rate estimation using the symlets6 wavelet are part of the implemented computational framework described in this article. Subsequent analysis indicates the potential applicability of this system in the prognostic domain, with an average error margin of approximately 3 beats per minute. The results obtained represent a promising advancement in non-invasive heart rate monitoring during sleep, with potential implications for improved diagnosis and management of cardiovascular and sleep-related disorders.
The goal of the presented project is to develop the concept of home e-health centers for barrier-free and cross-border telemedicine. AAL technologies are already present on the market but there is still a gap to close until they can be used for ordinary patient needs. The general idea needs to be accompanied by new services, which should be brought together in order to provide a full coverage of service for the users. Sleep and stress were chosen as predominant influence in the population. The executed scientific study of available home devices analyzing sleep has provided the necessary to select appropriate devices. The first choice for the project implementation is the device EMFIT QS+. This equipment provides a part of a complete system that a home telemedical hospital can provide at a level of precision and communication with internal and/or external health services.
In vivo, cells encounter different physical and chemical signals in the extracellular matrix (ECM) which regulate their behavior. Examples of these signals are micro- and nanometer-sized features, the rigidity, and the chemical composition of the ECM. The study of cell responses to such cues is important to understand complex cell functions, some diseases, and is basis for the development of new biomaterials for applications in medical implants or regenerative medicine. Therefore, the development of new methods for surface modifications with controlled physical and chemical features is crucial. In this work, we report a new combination of micelle nanolithography (BCML) and soft micro-lithography, for the production of polyethylene glycol (PEG) hydrogels, with a micro-grooved surface and decoration with hexagonally precisely arranged gold nanoparticles (AU NPs). The Au-NPs are used for binding adhesive ligands in a well-defined density. First tests were performed by culturing human fibroblasts on the gels. Adhesion and alignment of the cells along the parallel grooves of the surface were investigated. The substrates could provide a new platform for studying cell contact guidance by micro structures, and may enable a more precise control of cell behavior by nanometrically controlled surface functionalization.
Identifikation von Schlaf- und Wachzuständen durch die Auswertung von Atem- und Bewegungssignalen
(2021)
Measuring cardiorespiratory parameters in sleep, using non-contact sensors and the Ballistocardiography technique has received much attention due to the low-cost, unobtrusive, and non-invasive method. Designing a user-friendly, simple-to-use, and easy-to-deployment preserving less error-prone remains open and challenging due to the complex morphology of the signal. In this work, using four forcesensitive resistor sensors, we conducted a study by designing four distributions of sensors, in order to simplify the complexity of the system by identifying the region of interest for heartbeat and respiration measurement. The sensors are deployed under the mattress and attached to the bed frame without any interference with the subjects. The four distributions are combined in two linear horizontal, one linear vertical, and one square, covering the influencing region in cardiorespiratory activities. We recruited 4 subjects and acquired data in four regular sleeping positions, each for a duration of 80 seconds. The signal processing was performed using discrete wavelet transform bior 3.9 and smooth level of 4 as well as bandpass filtering. The results indicate that we have achieved the mean absolute error of 2.35 and 4.34 for respiration and heartbeat, respectively. The results recommend the efficiency of a triangleshaped structure of three sensors for measuring heartbeat and respiration parameters in all four regular sleeping positions.
Mobile monitoring of outpatients during cancer therapy becomes possible through technological advancements. This study leveraged a new remote patient monitoring app for in-between systemic therapy sessions. Patients’ evaluation showed that the handling is feasible. Clinical implementation must consider an adaptive development cycle for reliable operations.
Background: Internationally, teledermatology has proven to be a viable alternative to conventional physical referrals. Travel cost and referral times are reduced while patient safety is preserved. Especially patients from rural areas benefit from this healthcare innovation. Despite these established facts and positive experiences from EU neighboring countries like the Netherlands or the United Kingdom, Germany has not yet implemented store-and-forward teledermatology in routine care.
Methods: The TeleDerm study will implement and evaluate store-and-forward teledermatology in 50 general practitioner (GP) practices as an alternative to conventional referrals. TeleDerm aims to confirm that the possibility of store-and-forward teledermatology in GP practices is going to lead to a 15% (n = 260) reduction in referrals in the intervention arm. The study uses a cluster-randomized controlled trial design. Randomization is planned for the cluster “county”. The main observational unit is the GP practice. Poisson distribution of referrals is assumed. The evaluation of secondary outcomes like acceptance, enablers and barriers uses a mixed methods design with questionnaires and interviews.
Discussion: Due to the heterogeneity of GP practice organization, patient management software, information technology service providers, GP personal technical affinity and training, we expect several challenges in implementing teledermatology in German GP routine care. Therefore, we plan to recruit 30% more GPs than required by the power calculation. The implementation design and accompanying evaluation is expected to deliver vital insights into the specifics of implementing telemedicine in German routine care.
Background/Aim: The aim of this study was the conception, production, material analysis and cytocompatibility analysis of a new collagen foam for medical applications. Materials and Methods: After the innovative production of various collagen sponges from bovine sources, the foams were analyzed ex vivo in terms of their structure (including pore size) and in vitro in terms of cytocompatibility according to EN ISO 10993-5/-12. In vitro, the collagen foams were compared with the established soft and hard tissue materials cerabone and Jason membrane (both botiss biomaterials GmbH, Zossen, Germany). Results: Collagen foams with different compositions were successfully produced from bovine sources. Ex vivo, the foams showed a stable and long-lasting primary structure quality with a bubble area of 1,000 to 2,000 μm2. In vitro, all foams showed sufficient cytocompatibility. Conclusion: Collagen sponges represent a promising material for hard and soft tissue regeneration. Future studies could focus on integrating and investigating different additives in the foams.
The massive use of patient data for the training of artificial intelligence algorithms is common nowadays in medicine. In this scientific work, a statistical analysis of one of the most used datasets for the training of artificial intelligence models for the detection of sleep disorders is performed: sleep health heart study 2. This study focuses on determining whether the gender and age of the patients have a relevant influence to consider working with differentiated datasets based on these variables for the training of artificial intelligence models.
In the last decades, several driving systems were developed to improve the driving behaviour in energy efficiency or safety. However, these driving systems cover either the area of energy-efficiency or safety. Furthermore, they do not consider the stress level of the driver when showing a recommendation, although stress can lead to an unsafe or inefficient driving behaviour. In this paper, an approach is presented to consider the driver stress level in a driving system for safe and energy-efficient driving behaviour. The driving system tries to suppress a recommendation when the driver is in stress in order not to stress the driver additionally with recommendations in a stressful driving situation. This can lead to an increase in the road safety and in the user acceptance of the driving system, as the driver is not getting bothered or stressed by the driving system.
The evaluation of the approach showed, that the driving system
is able to show recommendations to the driver, while also reacting
to a high stress level by suppressing recommendations in
order not to stress the driver additionally.
Influence of the respirator on volatile organic compounds : an animal study in rats over 24 hours
(2015)
Long-term animal studies are needed to accomplish measurements of volatile organic compounds (VOCs) for medical diagnostics. In order to analyze the time course of VOCs, it is necessary to ventilate these animals. Therefore, a total of 10 male Sprague–Dawley rats were anaesthetized and ventilated with synthetic air via tracheotomy for 24 h. An ion mobility spectrometry coupled to multi-capillary columns (MCC–IMS) was used to analyze the expired air. To identify background contaminations produced by the respirator itself, six comparative measurements were conducted with ventilators only. Overall, a number of 37 peaks could be detected within the positive mode. According to the ratio peak intensity rat/ peak intensity ventilator blank, 22 peaks with a ratio >1.5 were defined as expired VOCs, 12 peaks with a ratio between 0.5 and 1.5 as unaffected VOCs, and three peaks with a ratio <0.5 as resorbed VOCs. The peak intensity of 12 expired VOCs changed significantly during the 24 h measurement. These results represent the basis for future intervention studies. Notably, online VOC analysis with MCC–IMS is possible over 24 h in ventilated rats and allows different experimental approaches.
This book contains the proceedings of the KES International conferences on Innovation in Medicine and Healthcare (KES-InMed-19) and Intelligent Interactive Multimedia Systems and Services (KES-IIMSS-19), held on 17–19 June 2019 and co-located in St. Julians, on the island of Malta, as part of the KES Smart Digital Futures 2019 multi theme conference.
The major areas covered by KES-InMed-19 include: Digital IT Architecture in Healthcare; Advanced ICT for Medical and Healthcare; Biomedical Engineering, Trends, Research and Technologies and Healthcare Support System. The major areas covered by KES-IIMSS-19 were: Interactive Technologies; Artificial Intelligence and Data Analytics; Intelligent Services and Architectures and Applications.
This book is of use to researchers in these vibrant areas, managers, industrialists and anyone wishing to gain an overview of the latest research in these fields.