TY - JOUR A1 - Henning, Thorsten A1 - Wagner, Philipp A1 - Gedat, Egbert A1 - Kochlik, Bastian A1 - Kusch, Paula A1 - Sowoidnich, Kay A1 - Vastag, Marko A1 - Gleim, Jeannine A1 - Braune, Marcel A1 - Maiwald, Martin A1 - Sumpf, Bernd A1 - Grune, Tilman A1 - Weber, Daniela T1 - Evaluation of Modern Approaches for the Assessment of Dietary Carotenoids as Markers for Fruit and Vegetable Consumption JF - Nutrients N2 - The assessment of dietary carotenoids via blood measurements has been widely used as a marker for fruit and vegetable consumption. In the present study, modern, non-invasive approaches to assess dietary carotenoids, such as skin measurements and an app-based short dietary record (ASDR), were compared with conventional methods such as plasma status and handwritten 3-day dietary records. In an 8-week observational study, 21 healthy participants aged 50–65 years recorded their daily consumption of carotenoid-rich fruits and vegetables via a specially developed ASDR. Anthropometry, blood samplings and assessment of skin carotenoids via Raman and reflection spectroscopy were performed at baseline, after four weeks and at the end of the study. App-based intake data showed good correlations with plasma α-carotene (r = 0.74, p < 0.0001), β-carotene (r = 0.71, p < 0.0001), and total plasma carotenoids (r = 0.65, p < 0.0001); weak correlations with plasma lutein/zeaxanthin and β-cryptoxanthin (both r = 0.34, p < 0.05); and no correlation with plasma lycopene. Skin measurements via reflection and Raman spectroscopy correlated well with total plasma carotenoids (r = 0.81 and 0.72, respectively; both p < 0.0001), α-carotene (r = 0.75–0.62, p < 0.0001), and β-carotene (r = 0.79–0.71, p < 0.0001); moderately with plasma lutein/zeaxanthin (both r = 0.51, p < 0.0001); weakly with plasma β-cryptoxanthin (r = 0.40–0.31, p < 0.05); and showed no correlation with plasma lycopene. Skin measurements could provide a more convenient and noninvasive approach of estimating a person’s fruit and vegetable consumption compared to traditional methods, especially in studies that do not intend blood sampling. ASDR records might function as a suitable, convenient tool for dietary assessment in nutritional intervention studies. KW - carotenoid KW - vitamin KW - e-health KW - app KW - dietary record KW - blood KW - skin measurement KW - spectroscopy KW - Raman Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-17280 SN - 2072-6643 VL - 15 IS - 7 PB - MDPI ER - TY - GEN A1 - Bezruk, Valeriy A1 - Gedat, Egbert A1 - Holzheimer, Katja A1 - Vandenhouten, Ralf T1 - Automated recognition of sleep stages by electroencephalograms T2 - Wissenschaftliche Beiträge 2015 N2 - The assessment of different sleep stages and their disorders in diseases is an important part of telematic medicine. With an electroencephalogram, the different stages of sleep can be monitored and classified with respect to brain activity. By means of modern data management such as the patient monitor ixTrend, for example, the data can be recorded for long sleep phases and evaluated by a computer using appropriate software, such as Dataplore. Here, a new mathematical model for the automated classification of sleep stages is introduced. The statistical method of autocorrelation, applied to six known sleep stages, was extended by one new class for unknown signals. Due to this new class, it is not necessary to sort all recorded EEG signals into one of the known classes, thereby, minimising the probability of errors. Further, the dependence of the error probability on the duration of the analysed EEG signal was assessed. A minimal error probability of pmin = 0.15 was detected. Exemplary data for one patient are reported. N2 - Die Beurteilung der verschiedenen Schlafphasen und deren Störungen bei Erkrankungen spielt eine wichtige Rolle in der telematischen Medizin. Mit einem Elektroenzephalogramm können die Schlafphasen überwacht und in verschiedene Klassen der Hirnaktivität eingeteilt werden. Durch moderne Methoden des Datenmanagements, wie etwa den Patientenmonitor ixTrend, können die Daten über längere Schlafphasen aufgezeichnet und am Computer mit geeigneter Software, etwa Dataplore, analysiert werden. Hier wird ein neues mathematisches Modell zur automatischen Klassifizierung der Schlafphasen vorgestellt. Die statistische Methode der Autokorrelation, angewendet auf sechs bekannte Schlafphasen, wurde erweitert um eine neue Klasse nicht zuordenbarer Muster. Durch diese neue Klasse wird es vermieden, jedes aufgezeichnete EEG-Muster in eine der bekannten Klassen hineinzuzwingen. Dadurch wird der Fehler bei der Zuordnung verringert. Weiter wird der Einfluss der Länge des analysierten EEG auf die Fehlerquote der Zuordnung untersucht. Es zeigte sich eine minimale Fehlerquote von pmin = 0,15. Es werden beispielhafte Daten eines Patienten gezeigt. Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-3552 SN - 0949-8214 VL - 19 SP - 39 EP - 43 ER - TY - JOUR A1 - Stumper, Nele A1 - Berger, Jörn A1 - Klotsche, Jens A1 - Gedat, Egbert A1 - Hoff, Paula A1 - Schmittat, Gabriela A1 - Burmester, Gerd-Rüdiger A1 - Krönke, Gerhard A1 - Backhaus, Marina A1 - Haugen, Ida Kristin A1 - Ohrndorf, Sarah T1 - To optimise the diagnostic process of rheumatic diseases affecting the hands using fluorescence optical imaging (FOI) JF - RMD Open N2 - Background Accurate and rapid diagnosis of rheumatic diseases is essential for further treatment decision. Different rheumatic diseases present characteristic patterns (image features) in fluorescence optical imaging (FOI). We developed an atlas of FOI image features and tested its ability to differentiate various rheumatic diseases. Methods FOI images from patients with rheumatoid arthritis (RA), psoriatic arthritis (PsA), connective tissue diseases (CTD) and osteoarthritis (OA) were analysed by two readers blinded for diagnosis and calibrated against each other, using the prima vista mode (PVM) and an automated 5-phase model. Twenty-six different reoccurring typical signal enhancement patterns (features) indicating inflamed joints, nail or skin were defined and all FOI images were scored accordingly. The feature frequency in each patient cohort and phase (PVM, 5-phase) was counted. Contingency tables were created with categorical variable counts and diagnosis using common formulae. Findings Four hundred thirty-eight patients with RA (n=117), PsA (n=110), CTD (n=121) and OA (n=90) were included. Once the data had been categorised, a two-step diagnostic pathway was developed: in the first step, OA was best distinguished from the other diseases with high specificity by five patterns (specificity >0.9, diagnostic OR between 2.34 and 8.24). In a second step, the remaining autoimmune diseases were differentiated from each other by a certain number of features (five for RA, 12 for PsA and four for CTD). Interpretation This was the first study to show that feature analysis in FOI helps to differentiate typical rheumatic diseases from each other, potentially simplifying and speeding up the diagnostic process. Therefore, FOI could be considered an additional component of a wider range of imaging techniques used in rheumatology. Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-20698 VL - 11 IS - 3 PB - EULAR ER - TY - JOUR A1 - Gedat, Egbert A1 - Berger, Jörn A1 - Kiesel, Denise A1 - Failli, Vieri A1 - Briel, Andreas A1 - Welker, Pia T1 - Features Found in Indocyanine Green-Based Fluorescence Optical Imaging of Inflammatory Diseases of the Hands JF - Diagnostics N2 - Rheumatologists in Europe and the USA increasingly rely on fluorescence optical imaging (FOI, Xiralite) for the diagnosis of inflammatory diseases. Those include rheumatoid arthritis, psoriatic arthritis, and osteoarthritis, among others. Indocyanine green (ICG)-based FOI allows visualization of impaired microcirculation caused by inflammation in both hands in one examination. Thousands of patients are now documented and most literature focuses on inflammatory arthritides, which affect synovial joints and their related structures, making it a powerful tool in the diagnostic process of early undifferentiated arthritis and rheumatoid arthritis. However, it has become gradually clear that this technique has the potential to go even further than that. FOI allows visualization of other types of tissues. This means that FOI can also support the diagnostic process of vasculopathies, myositis, collagenoses, and other connective tissue diseases. This work summarizes the most prominent imaging features found in FOI examinations of inflammatory diseases, outlines the underlying anatomical structures, and introduces a nomenclature for the features and, thus, supports the idea that this tool is a useful part of the imaging repertoire in rheumatology clinical practice, particularly where other imaging methods are not easily available. KW - optical diagnosis imaging KW - fluorescence optical imaging KW - indocyanine green fluorescence KW - image feature KW - image analysis KW - rheumatoid disease Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-16359 SN - 2075-4418 VL - 12 IS - 8 PB - MDPI ER - TY - JOUR A1 - Rothe, Felix A1 - Berger, Jörn A1 - Welker, Pia A1 - Fiebelkorn, Richard A1 - Kupper, Stefan A1 - Kiesel, Denise A1 - Gedat, Egbert A1 - Ohrndorf, Sarah T1 - Fluorescence optical imaging feature selection with machine learning for differential diagnosis of selected rheumatic diseases JF - Frontiers in Medicine N2 - Background and objective: Accurate and fast diagnosis of rheumatic diseases affecting the hands is essential for further treatment decisions. Fluorescence optical imaging (FOI) visualizes inflammation-induced impaired microcirculation by increasing signal intensity, resulting in different image features. This analysis aimed to find specific image features in FOI that might be important for accurately diagnosing different rheumatic diseases. Patients and methods: FOI images of the hands of patients with different types of rheumatic diseases, such as rheumatoid arthritis (RA), osteoarthritis (OA), and connective tissue diseases (CTD), were assessed in a reading of 20 different image features in three phases of the contrast agent dynamics, yielding 60 different features for each patient. The readings were analyzed for mutual differential diagnosis of the three diseases (One-vs-One) and each disease in all data (One-vs-Rest). In the first step, statistical tools and machine-learning-based methods were applied to reveal the importance rankings of the features, that is, to find features that contribute most to the model-based classification. In the second step machine learning with a stepwise increasing number of features was applied, sequentially adding at each step the most crucial remaining feature to extract a minimized subset that yields the highest diagnostic accuracy. Results: In total, n = 605 FOI of both hands were analyzed (n = 235 with RA, n = 229 with OA, and n = 141 with CTD). All classification problems showed maximum accuracy with a reduced set of image features. For RA-vs.-OA, five features were needed for high accuracy. For RA-vs.-CTD ten, OA-vs.-CTD sixteen, RA-vs.-Rest five, OA-vs.-Rest eleven, and CTD-vs-Rest fifteen, features were needed, respectively. For all problems, the final importance ranking of the features with respect to the contrast agent dynamics was determined. Conclusions: With the presented investigations, the set of features in FOI examinations relevant to the differential diagnosis of the selected rheumatic diseases could be remarkably reduced, providing helpful information for the physician. KW - rheumatoid arthritis KW - osteoarthritis KW - connective tissue disease KW - gradient boosting machine KW - forward selection algorithm Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-17922 SN - 2296-858X VL - 10 PB - Frontiers ER - TY - CHAP A1 - Pulwer, Silvio A1 - Fiebelkorn, Richard A1 - Zesch, Christoph A1 - Steglich, Patrick A1 - Villringer, Claus A1 - Villasmunta, Francesco A1 - Gedat, Egbert A1 - Handrich, Jan A1 - Schrader, Sigurd A1 - Vandenhouten, Ralf T1 - Endoscopic orientation by multimodal data fusion T2 - Proc. SPIE 10931, MOEMS and Miniaturized Systems XVIII N2 - To improve the feasibility of endoscopic inspection processes we developed a system that provides online information about position, orientation and viewing direction of endoscopes, to support the analysis of endoscopic images and to ease the operational handling of the equipment. The setup is based on an industrial endoscope consisting of a camera, various MEMS and multimodal data fusion. The software contains algorithms for feature and geometric structure recognition as well as Kalman filters. To track the distal end of the endoscope and to generate 3D point cloud data in real time the optical and photometrical characteristics of the system are registered and the movement of the endoscope is reconstructed by using image processing techniques. KW - endoscope KW - orientation KW - Kalman filter KW - visual odometry KW - mems KW - gyroscope KW - accelerometer Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-11420 SN - 1996-756X ER - TY - GEN A1 - Gedat, Egbert A1 - Fechner, Pascal A1 - Fiebelkorn, Richard A1 - Vandenhouten, Jan A1 - Vandenhouten, Ralf T1 - Image recognition of multi-perspective data for intelligent analysis of gestures and actions T2 - Wissenschaftliche Beiträge 2018 N2 - The BERMUDA project started in January 2015 and was successfully completed after less than three years in August 2017. A technical set-up and an image processing and analysis software were developed to record and evaluate multi-perspective videos. Based on two cameras, positioned relatively far from one another with tilted axes, synchronized videos were recorded in the laboratory and in real life. The evaluation comprised the background elimination, the body part classification, the clustering, the assignment to persons and eventually the reconstruction of the skeletons. Based on the skeletons, machine learning techniques were developed to recognize the poses of the persons and next for the actions performed. It was, for example, possible to detect the action of a punch, which is relevant in security issues, with a precision of 51.3 % and a recall of 60.6 %. N2 - Das Projekt BERMUDA konnte im Januar 2015 begonnen und nach knapp drei Jahren im August 2017 erfolgreich abgeschlossen werden. Es wurden ein technischer Aufbau und eine Bildverarbeitungs- und Analysesoftware entwickelt, mit denen sich multiperspektivische Videos aufzeichnen und auswerten lassen. Basierend auf zwei in größerem Abstand gewinkelt positionierten Kameras wurden synchrone Videos sowohl im Labor als auch in realen Umgebungen aufgenommen. Die Auswertung umfasst die Hintergrundeliminierung, die Körperteilklassifikation, ein Clustering, die Zuordnung zu Personen und schließlich die Rekonstruktion der Skelette. Ausgehend von den Skeletten wurden Methoden des maschinellen Lernens zur Erkennung der Haltungen und darauf aufbauend zur Gestenerkennung entwickelt. Beispielhaft konnte die im Sicherheitskontext relevante Handlung des Schlagens mit einer Genauigkeit von 51,3 % und einer Trefferquote von 60,6 % erkannt werden. Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-10230 SN - 0949-8214 VL - 22 SP - 25 EP - 30 ER - TY - GEN A1 - Gedat, Egbert A1 - Fechner, Pascal A1 - Fiebelkorn, Richard A1 - Vandenhouten, Ralf T1 - Szenenanalyse und Unterscheidung der Skelette mehrerer Menschen in digitalen Bildern mit Graphentheorie durch eine k-kürzeste-disjunkte-Wege-Suche T2 - Wissenschaftliche Beiträge 2017 N2 - Das Erkennen von Personen auf Fotos und in Videos geschieht in aktuellen Klassifizierungsverfahren durch die Zuordnung eines Köperteils zu jedem Pixel. Anschließend werden die gefundenen Körperteile zu Skeletten zusammengefasst. Im Falle mehrerer abgebildeter Personen ergibt sich das Problem der Zuordnung der Körperteile zu den verschiedenen Skeletten. Es wurde in dieser Arbeit ein auf dem Suurballe-Algorithmus basierendes graphentheoretisches Verfahren entwickelt, das diese Aufgabe löst. Aufbauend auf im Wesentlichen abstandsabhängigen Kantengewichten wird eine k-kürzeste-disjunkte-Wege-Suche durchgeführt. Unter Einbezug von möglicherweise fehlenden Körperteilen durch Unsichtbar-Knoten und automatisches Aussortieren zu viel gefundener Körperteile werden so die k-optimalen Skelette gefunden. Die Methode wurde an idealisierten computergenerierten Bildern mit einer Trefferquote von 100 % gefundener Personen getestet. Ein Test mit realen Bilddaten lieferte eine Trefferquote von 71,7 %. N2 - Recognition of humans on photos and in videos is done per current classification procedures by assigning a body part to each pixel. The body parts thus allocated are subsequently ordered into skeletons. In the case the images indicate that multiple humans are present, the problem arises of assigning the body parts to different skeletons. A Suurballe algorithm-based method developed by us solves this task. By mainly relying on distance-related edge weights, a k shortest disjoint paths search is performed. Accounting for possibly missing body parts by invisible-nodes and automatically sorting out redundant body parts, the optimal k skeletons are found. The method was tested with computer-generated data, yielding a rate of 100 % persons detected. A test with real-world images yielded a recall ratio of 71.7 %. Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-9394 SN - 0949-8214 VL - 21 SP - 31 EP - 35 ER -