OPEN FAU Online-Publikationssystem der Friedrich-Alexander-Universität Erlangen-Nürnberg
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Partial 3D-reconstruction of the colon from monoscopic colonoscopy videos using shape-from-motion and deep learning
No full textFor the image-based documentation of a colonoscopy procedure, a 3D-reconstuction of the hollow colon structure from endoscopic video streams is desirable. To obtain this reconstruction, 3D information about the colon has to be extracted from monocular colonoscopy image sequences. This information can be provided by estimating depth through shape-from-motion approaches, using the image information from two successive image frames and the exact knowledge of their disparity. Nevertheless, during a standard colonoscopy the spatial offset between successive frames is continuously changing. Thus, in this work deep convolutional neural networks (DCNNs) are applied in order to obtain piecewise depth maps and point clouds of the colon. These pieces can then be fused for a partial 3D reconstruction
Revision of the “Guideline of the German Medical Association on Quality Assurance in Medical Laboratory Examinations – Rili-BAEK”
No full textSurveillance of drug prescribing: why outliers miss their targets – a qualitative study
No full textBackground Rising costs are a challenge for healthcare systems. To keep expenditure for drugs under control, in many healthcare systems, drug prescribing is continuously monitored. The Bavarian Drug Agreement (German: Wirkstoffvereinbarung or WSV) for the ambulatory sector in Bavaria (the federal state of Germany) was developed for this purpose. Physicians must reach defined drug target quotas for prescribing generic drugs and certain recommended drugs specified and measured with defined daily doses (DDD). A subgroup of physicians, known as outliers, may miss their drug targets. The objective of this qualitative study was to understand the reasons physicians miss their targets. Methods We identified outliers based on drug prescribing data from the association of statutory health insurance (SHI)-accredited physicians (KV). Outliers were invited to participate in semi-structured interviews. Results Out of 401 outliers thus identified n = 26 physicians were interviewed. Their prescribing behaviours are affected by competing demands regarding drug decisions, such as saving staff time, costs, and discussions with patients. Often, their freedom to prescribe is limited by previous prescribers. Ease of administration of drugs not recommended also plays a role. Uncritical enthusiasm regarding the effectiveness and safety of drugs with recommendations, often reinforced by pharmaceutical marketing, leads to missed targets. Some physicians have coping strategies to avoid becoming outliers. Conclusions Investigating physicians not meeting their targets helps us understand beliefs and barriers for appropriate drug prescribing. Based on these kinds of findings, surveillance procedures can be improved, and physicians can receive support to meet targets in the future. Trial registration This trial has been registered in the German Register of Clinical Trials (DRKS: DRKS00016161; registration date 07. December 2018).Keypoints Regulating drug prescribing aims to control costs for healthcare systems. In fact, some physicians miss their drug targets. Reasons are individual, but mechanisms can be identified.Open Access funding enabled and organized by Projekt DEAL.Innovation Fund of the Federal Joint Committee (G-BA)Philipps-Universität Marburg (1009
A primer on current status and future opportunities of clinical optoacoustic imaging
Full text linkDespite its introduction in the 1970’s, it is only recent technology advances that have propelled growth in clinical optoacoustic (photoacoustic) imaging over the past decade. We analytically present the broad landscape of clinical optoacoustic applications in the context of these key technology advances, the unique contrast achieved, and the tissue biomarkers resolved. We then discuss current challenges and future opportunities to address the unmet clinical needs.Sanofi-Aventis Deutschlandhttp://dx.doi.org/10.13039/100019944Bayerische Forschungsstiftunghttp://dx.doi.org/10.13039/501100002745Horizon 2020 Framework Programmehttp://dx.doi.org/10.13039/10001066
Self-organised ordering of scandium into basal monolayers of aluminium nitride and its implication for the growth of well-crystallized (Al,Sc)N materials for electronic devices
Full text linkAluminium scandium nitride appears as a promising material for future wide band gap semiconductor devices, due to its large spontaneous polarisation effects. Extensive annealing experiments with regard to time and temperature of sputtered (Al,Sc)N thin films result in an ordering of such disordered and metastable materials of wurtzite structure into a so far unknown layered phase, once temperature allows sufficient cationic and anionic diffusion beyond ∼1200 °C. Detailed transmission electron microscopy investigations of this layered phase reveal the complete and self-organised ordering of scandium into octahedrally coordinated basal planes in between several AlN layers of wurtzite structure. The specific numbers of layers spread statistically around an average value depending on the chemical composition and formation temperature. Further investigations of the onset of such phase formation at 1400 °C demonstrate that partially disordered sequences of Sc-bearing basal planes can be thermodynamically interpreted as exsolution lamellae of monolayer thickness. These structural alterations may considerably affect the thermal stability and reliability of devices. The theoretically outstanding physical properties can be lost by the formation of octahedrally coordinated Sc in AlN during any appropriate high temperature deposition or annealing processes during device fabrication as well as due to migration effects during device operation.Extensive annealing of (Al,Sc)N sputter films reveals an ordering of scandium into basal monolayers. The structural characterisation, a thermodynamic formation model and its implications on (Al,Sc)N material growth and device stability is presented
Fortschrittliche Deep-Learning-Architekturen für Mensch-Radar-Interaktion in Innenräumen
No full textDue to the incredible progress in communication technologies and continual reduction in the size of electronic devices, sensors are progressively permeating everyday life. In indoor scenarios, human sensing has led to the creation of smart devices ranging from the usage of human presence or count to vital sensing information to regulate heating, ventilation, lighting, and entertainment systems for a better user experience while saving significant amounts of energy and CO2 emissions in the process. Additionally, sensors have extended in our daily lives to track and monitor our well-being in the form of sleep tracking, vital sensing, or activity monitoring. Deep Learning (DL) has propelled the maturity and robustness of these use cases and reduced the Go-To-Market time of resulting products.
The most dominant human sensing sensors are vision sensors which raise privacy concerns and suffer from limited illuminations or occlusions. Contrary to that, radar systems are privacy-preserving, independent of illumination conditions, and allow extraction of information such as range, velocity, and angle of a target.
Most works in radar-based human sensing are limited to automotive use cases and use traditional signal processing or conventional DL architectures designed for vision systems.
This work explores the use of low-cost millimeter-wave frequency continuous wave radar sensors for human sensing in complex indoor environments, ranging from presence sensing, people counting, and people activity recognition to vital signs monitoring by leveraging advanced deep learning techniques. The limitations of using conventional approaches due to multi-path reflections, clutter, and ghost targets are identified and addressed by using new training schemes, architectures, and loss functions that result in robust and superior performance compared to previous approaches while being widely acceptable due to small computation footprint of the proposed methodologies.Der rasante Fortschritt in der Kommunikationstechnologie und die kontinuierliche Miniaturisie-
rung elektronischer Geräte haben dazu geführt, dass Sensoren zunehmend Einzug in unseren
Alltag halten. Insbesondere in Innenräumen hat die Erfassung menschlicher Bewegungen zur
Entwicklung intelligenter Geräte geführt, die von der Nutzung von Informationen über die
Anwesenheit von Menschen bis hin zur Steuerung von Heizung, Lüftung, Beleuchtung und
Unterhaltungssystemen reichen, um eine bessere Benutzererfahrung zu bieten und dabei
signifikante Energiemengen sowie CO2-Emissionen einzusparen. Darüber hinaus erlauben es
Sensoren, eine kontinuierliche Überwach-ung unseres Wohlbefindens in Form von Schlaftracking,
Vitalmessungen oder Aktivitätsüberwachung durchzuführen. Deep Learning (DL) ermöglicht eine
Verbesserung der Reife und Robustheit dieser Anwendungsfälle und beschleunigt zudem die
Markteinführung der resultierenden Produkte. Die am häufigsten von Menschen für sich
verwendeten Sensoren sind Bildgebungs-Sensoren, die Bedenken hinsichtlich der Privatsphäre
aufwerfen und unter begrenzten Beleuchtungs- oder Verdeckungsbedingungen leiden. Im
Gegensatz dazu gewährleisten Radarsysteme den Datenschutz, sind unabhängig von
Lichtverhältnissen und ermöglichen die Extraktion von Informationen wie Entfernung,
Geschwin-digkeit und Winkel eines Ziels. Bisherige Arbeiten im Bereich der radarbasierten
Erfassung beschränken sich oftmals auf Anwendungsfälle im Automobilbereich und nutzen
traditionelle Signalverarbeitung oder konventionelle DL-Architekturen, die für
Bildverarbeitungssysteme entwickelt wurden. Die vorliegende Arbeit untersucht den Einsatz von
kostengünstigen Millimeterwellen-Dauerstrichradar-sensoren zur Personenerkennung in
komplexen Innenraumumgebungen, von der Präsenzerkennung, der Personenzählung und der
Personenaktivität bis hin zur Überwachung von Vitalfunktionen durch den Einsatz fortschrittlicher
Deep-Learning-Techniken. Die Einschränkungen herkömmlicher Ansätze aufgrund von Mehrwege-
Reflexionen, Störungen und unerwünschten Zielen werden durch den Einsatz neuer
Trainingstechniken, Architekturen und Verlustfunktionen identifiziert und behoben, was zu einer
robusten und überlegenen Leistung im Vergleich zu früheren Ansätzen führt. Zudem sind die
vorgeschlagenen Methoden aufgrund ihres geringen Rechenaufwands von Vorteil
Effective Thermal Conductivity of Nanofluids Containing Silicon Dioxide, Titanium Dioxide, Copper Oxide, Polystyrene, or Polymethylmethacrylate Nanoparticles Dispersed in Water, Ethylene Glycol, or Glycerol
No full textThe present study represents a continuation of our investigations on the effective thermal conductivity λeff of nanofluids by systematically varying the types of base fluids and particles. For the spherical nanoparticles with mean diameters between (20 and 175) nm, the metal oxides silicon dioxide (SiO2), titanium dioxide (TiO2), and copper oxide (CuO) as well as the polymers polystyrene (PS) and polymethylmethacrylate (PMMA) were selected to cover a broad range for the particle thermal conductivity λp from about (0.1 to 30) W⋅m–1⋅K–1. The corresponding polar base fluids water, ethylene glycol, and glycerol allow to not only vary their thermal conductivity λbf by a factor of more than two, but also their dynamic viscosity by about three orders of magnitude. For the measurement of λeff of the twelve different particle–fluid combinations, i.e., TiO2 or CuO with all three liquids as well as SiO2, PS, or PMMA with water or ethylene glycol, a steady-state guarded parallel-plate instrument (GPPI) associated with an expanded (k = 2) relative uncertainty between 0.022 and 0.032 was used at atmospheric pressure over a temperature range from (283 to 358) K at varying particle volume fractions up to 0.31. The results for the thermal-conductivity ratio λeff·λbf–1 are independent of temperature and show a moderate and relatively linear change as a function of the particle volume fraction. For similar ratios λp·λbf–1, the experimental data for λeff·λbf–1 are also very similar, which are above, close to, or below 1 if λp is larger than, comparable to, or smaller than λbf, respectively. For all nanofluids investigated, the Hamilton–Crosser model can describe the present measurement results and reliable experimental data reported in the literature for λeff·λbf–1 typically within ± 5 %. Overall, the measurement results from this work contribute to an extension of the database for λeff of nanofluids with respect to the investigated wide ranges of systems, temperature, and particle volume fraction.Open Access funding enabled and organized by Projekt DEAL.Deutsche Forschungsgemeinschafthttps://doi.org/10.13039/501100001659Friedrich-Alexander-Universität Erlangen-Nürnberg (1041
Latency Reversing Agents and the Road to a HIV Cure
No full textHIV-1 infection cannot be cured due to the presence of HIV-1 latently infected cells. These cells do not produce the virus, but they can resume virus production at any time in the absence of antiretroviral therapy. Therefore, people living with HIV (PLWH) need to take lifelong therapy. Strategies have been coined to eradicate the viral reservoir by reactivating HIV-1 latently infected cells and subsequently killing them. Various latency reversing agents (LRAs) that can reactivate HIV-1 in vitro and ex vivo have been identified. The most potent LRAs also strongly activate T cells and therefore cannot be applied in vivo. Many LRAs that reactivate HIV in the absence of general T cell activation have been identified and have been tested in clinical trials. Although some LRAs could reduce the reservoir size in clinical trials, so far, they have failed to eradicate the reservoir. More recently, immune modulators have been applied in PLWH, and the first results seem to indicate that these may reduce the reservoir and possibly improve immunological control after therapy interruption. Potentially, combinations of LRAs and immune modulators could reduce the reservoir size, and in the future, immunological control may enable PLWH to live without developing HIV-related disease in the absence of therapy.This research received no external funding
Linkage of Ifo Survey and Balance-Sheet Data: The EBDC Business Expectations Panel & the EBDC Business Investment Panel
No full textA concept and technical requirements for the Temi platform supporting care and nursing
No full textWith a growing number of people in need of care and the shortage of skilled workers, caregivers express the desire for improved support to reduce physical strain and psychological stress. One task for burden relieve is documentation of nursing actions and treatment processes, as this can be done in parallel with the action. Hence tools capturing information in real time could improve information sharing. Also, contactless assessment of patients’ vital signs and emotions is desirable. Thus, the idea is to enhance an existing mobile platform, which can follow the nurse, includes a voice-controlled assistant, and be able for wireless (e.g., via Bluetooth) assessment of vital data from patients in its direct vicinity. Using the Temi telepresence robot as an open technical platform, we provide and discus requirements for this platform to potentially aid and relief to medical staff within caring and elderly homes, with respect to voice-control, Bluetooth-based vital data collection, interfaces to a digital patient manager and automated emotion recognition. The presented concept and the rating of the associated technologies leads to the conclusion that - if adequately implemented and installed - the caregivers could potentially receive noticeable relief from them. Nevertheless, as next steps, the examined technologies and ideas must be completely implemented, thoroughly fused with each other, and evaluated to provide a real support system for clinical care and assisted documentation