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Aims
Human-computer interactions (HCI) may have a relevant impact on the performance of Artificial Intelligence (AI). Studies show that although endoscopists assessing Barrett’s esophagus (BE) with AI improve their performance significantly, they do not achieve the level of the stand-alone performance of AI. One aspect of HCI is the impact of AI on the degree of certainty and confidence displayed by the endoscopist. Indirectly, diagnostic confidence when using AI may be linked to trust and acceptance of AI. In a BE video study, we aimed to understand the impact of AI on the diagnostic confidence of endoscopists and the possible correlation with diagnostic performance.
Methods
22 endoscopists from 12 centers with varying levels of BE experience reviewed ninety-six standardized endoscopy videos. Endoscopists were categorized into experts and non-experts and randomly assigned to assess the videos with and without AI. Participants were randomized in two arms: Arm A assessed videos first without AI and then with AI, while Arm B assessed videos in the opposite order. Evaluators were tasked with identifying BE-related neoplasia and rating their confidence with and without AI on a scale from 0 to 9.
Results
The utilization of AI in Arm A (without AI first, with AI second) significantly elevated confidence levels for experts and non-experts (7.1 to 8.0 and 6.1 to 6.6, respectively). Only non-experts benefitted from AI with a significant increase in accuracy (68.6% to 75.5%). Interestingly, while the confidence levels of experts without AI were higher than those of non-experts with AI, there was no significant difference in accuracy between these two groups (71.3% vs. 75.5%). In Arm B (with AI first, without AI second), experts and non-experts experienced a significant reduction in confidence (7.6 to 7.1 and 6.4 to 6.2, respectively), while maintaining consistent accuracy levels (71.8% to 71.8% and 67.5% to 67.1%, respectively).
Conclusions
AI significantly enhanced confidence levels for both expert and non-expert endoscopists. Endoscopists felt significantly more uncertain in their assessments without AI. Furthermore, experts with or without AI consistently displayed higher confidence levels than non-experts with AI, irrespective of comparable outcomes. These findings underscore the possible role of AI in improving diagnostic confidence during endoscopic assessment.
Einleitung
Third space Endoskopieprozeduren wie die endoskopische Submukosadissektion (ESD) und die perorale endoskopische Myotomie (POEM) sind technisch anspruchsvoll und gehen mit untersucherabhängigen Komplikationen wie Blutungen und Perforationen einher. Grund hierfür ist die unabsichtliche Durchschneidung von submukosalen Blutgefäßen ohne präemptive Koagulation.
Ziele
Die Forschungsfrage, ob ein KI-Algorithmus die intraprozedurale Gefäßerkennung bei ESD und POEM unterstützen und damit Komplikationen wie Blutungen verhindern könnte, erscheint in Anbetracht des erfolgreichen Einsatzes von KI bei der Erkennung von Kolonpolypen interessant.
Methoden
Auf 5470 Einzelbildern von 59 third space Endoscopievideos wurden submukosale Blutgefäße annotiert. Zusammen mit weiteren 179.681 nicht-annotierten Bildern wurde ein DeepLabv3+neuronales Netzwerk mit dem ECMT-Verfahren für semi-supervised learning trainiert, um Blutgefäße in Echtzeit erkennen zu können. Für die Evaluation wurde ein Videotest mit 101 Videoclips aus 15 vom Trainingsdatensatz separaten Prozeduren mit 200 vordefinierten Gefäßen erstellt. Die Gefäßdetektionsrate, -zeit und -dauer, definiert als der Prozentsatz an Einzelbildern eines Videos bezogen auf den Goldstandard, auf denen ein definiertes Gefäß erkannt wurde, wurden erhoben. Acht erfahrene Endoskopiker wurden mithilfe dieses Videotests im Hinblick auf Gefäßdetektion getestet, wobei eine Hälfte der Videos nativ, die andere Hälfte nach Markierung durch den KI-Algorithmus angesehen wurde.
Ergebnisse
Der mittlere Dice Score des Algorithmus für Blutgefäße war 68%. Die mittlere Gefäßdetektionsrate im Videotest lag bei 94% (96% für ESD; 74% für POEM). Die mediane Gefäßdetektionszeit des Algorithmus lag bei 0,32 Sekunden (0,3 Sekunden für ESD; 0,62 Sekunden für POEM). Die mittlere Gefäßdetektionsdauer lag bei 59,1% (60,6% für ESD; 44,8% für POEM) des Goldstandards. Alle Endoskopiker hatten mit KI-Unterstützung eine höhere Gefäßdetektionsrate als ohne KI. Die mittlere Gefäßdetektionsrate ohne KI lag bei 56,4%, mit KI bei 71,2% (p<0.001).
Schlussfolgerung
KI-Unterstützung war mit einer statistisch signifikant höheren Gefäßdetektionsrate vergesellschaftet. Die mediane Gefäßdetektionszeit von deutlich unter einer Sekunde sowie eine Gefäßdetektionsdauer von größer 50% des Goldstandards wurden für den klinischen Einsatz als ausreichend erachtet. In prospektiven Anwendungsstudien sollte der KI-Algorithmus auf klinische Relevanz getestet werden.
Einleitung Die Endoskopische Retrograde Cholangiopankreatikographie (ERCP) ist der Goldstandard in der Diagnostik und Therapie von Erkrankungen des pankreatobiliären Trakts. Jedoch ist sie technisch sehr anspruchsvoll und weist eine vergleichsweise hohe Komplikationsrate auf.
Ziele
In der vorliegenden Machbarkeitsstudie soll geprüft werden, ob mithilfe eines Deep-learning-Algorithmus die Papille und das Ostium zuverlässig detektiert werden können und somit für Endoskopiker mit geringer Erfahrung ein geeignetes Hilfsmittel, insbesondere für die Ausbildungssituation, darstellen könnten.
Methodik
Wir betrachteten insgesamt 606 Bilddatensätze von 65 Patienten. In diesen wurde sowohl die Papilla duodeni major als auch das Ostium segmentiert. Anschließend wurde eine neuronales Netz mittels eines Deep-learning-Algorithmus trainiert. Außerdem erfolgte eine 5-fache Kreuzvaldierung.
Ergebnisse
Bei einer 5-fachen Kreuzvaldierung auf den 606 gelabelten Daten konnte für die Klasse Papille eine F1-Wert von 0,7908, eine Sensitivität von 0,7943 und eine Spezifität von 0,9785 erreicht werden, für die Klasse Ostium eine F1-Wert von 0,5538, eine Sensitivität von 0,5094 und eine Spezifität von 0,9970 (vgl. [Tab. 1]). Unabhängig von der Klasse zeigte sich gemittelt (Klasse Papille und Klasse Ostium) ein F1-Wert von 0,6673, eine Sensitivität von 0,6519 und eine Spezifität von 0,9877 (vgl. [Tab. 2]).
Schlussfolgerung
In vorliegende Machbarkeitsstudie konnte das neuronale Netz die Papilla duodeni major mit einer hohen Sensitivität und sehr hohen Spezifität identifizieren. Bei der Detektion des Ostiums war die Sensitivität deutlich geringer. Zukünftig soll das das neuronale Netz mit mehr Daten trainiert werden. Außerdem ist geplant, den Algorithmus auch auf Videos anzuwenden. Somit könnte langfristig ein geeignetes Hilfsmittel für die ERCP etabliert werden.
Developing new deep-drawn sheet metal parts is a challenging task due to conflicting demands for low costs, durability, and crash properties. Ensuring manufacturability throughout geometrical changes adds to the complexity, leading engineers to rely on experience-driven iterative design changes that compromise requirements and lack reproducibility. Finite Element (FE) simulation models are employed to ensure manufacturability, albeit at the expense of high computational costs and delays in part development.
To improve efficiency, a Machine learning (ML)-centered approach was proposed to ensure manufacturability. However, the limited availability of data raises uncertainty about whether a low- or high-dimensional ML approach is most suitable for drawability assessment.
This work compares the accuracy of a low-dimensional, feature-based Linear Support Vector surrogate and an adapted high-dimensional PointNet model under different dataset sizes. The dataset is composed of parametrically generated, U-shaped structural sheet metal parts.
We use a one-step simulation scheme and evaluate results with a Forming Limit Diagram (FLD) to label drawability. Results show the point of transition to be at about 500 training samples, from which onwards Deep learning is advantageous.
Moreover, the generalizability of these models is tested on a second dataset with topologically similar components. This is to assess the potential for a geometrically more comprehensive evaluation. We discuss several influences on model performances and outline future potentials.
Einleitung
Die Differenzierung zwischen nicht dysplastischem Barrett-Ösophagus (NDBE) und mit Barrett-Ösophagus assoziierten Neoplasien (BERN) während der endoskopischen Inspektion erfordert viel Expertise. Die frühe Diagnosestellung ist wichtig für die weitere Prognose des Barrett-Karzinoms. In Deutschland werden Patient:innen mit einem Barrett-Ösophagus (BE) in der Regel im niedergelassenen Sektor überwacht.
Ziele
Ziel ist es, den Einfluss von einem auf Künstlicher Intelligenz (KI) basierenden klinischen Entscheidungsunterstützungssystems (CDSS) auf die Performance von niedergelassenen Gastroenterolog:innen (NG) bei der Evaluation von Barrett-Ösophagus (BE) zu untersuchen.
Methodik
Es erfolgte die prospektive Sammlung von 96 unveränderten hochauflösenden Videos mit Fällen von Patient:innen mit histologisch bestätigtem NDBE und BERN. Alle eingeschlossenen Fälle enthielten mindestens zwei der folgenden Darstellungsmethoden: HD-Weißlichtendoskopie, Narrow Band Imaging oder Texture and Color Enhancement Imaging. Sechs NG von sechs unterschiedlichen Praxen wurden als Proband:innen eingeschlossen. Es erfolgte eine permutierte Block-Randomisierung der Videofälle in entweder Gruppe A oder Gruppe B. Gruppe A implizierte eine Evaluation des Falls durch Proband:innen zunächst ohne KI und anschließend mit KI als CDSS. In Gruppe B erfolgte die Evaluation in umgekehrter Reihenfolge. Anschließend erfolgte eine zufällige Wiedergabe der so entstandenen Subgruppen im Rahmen des Tests.
Ergebnis
In diesem Test konnte ein von uns entwickeltes KI-System (Barrett-Ampel) eine Sensitivität von 92,2%, eine Spezifität von 68,9% und eine Accuracy von 81,3% erreichen. Mit der Hilfe von KI verbesserte sich die Sensitivität der NG von 64,1% auf 71,2% (p<0,001) und die Accuracy von 66,3% auf 70,8% (p=0,006) signifikant. Eine signifikante Verbesserung dieser Parameter zeigte sich ebenfalls, wenn die Proband:innen die Fälle zunächst ohne KI evaluierten (Gruppe A). Wurde der Fall jedoch als Erstes mit der Hilfe von KI evaluiert (Gruppe B), blieb die Performance nahezu konstant.
Schlussfolgerung
Es konnte ein performantes KI-System zur Evaluation von BE entwickelt werden. NG verbessern sich bei der Evaluation von BE durch den Einsatz von KI.
Increasing the lifetime of titanium implants through a diffusion-controlled surface treatment
(2023)
Aims
VA is an endoscopic finding of celiac disease (CD), which can easily be missed if pretest probability is low. In this study, we aimed to develop an artificial intelligence (AI) algorithm for the detection of villous atrophy on endoscopic images.
Methods
858 images from 182 patients with VA and 846 images from 323 patients with normal duodenal mucosa were used for training and internal validation of an AI algorithm (ResNet18). A separate dataset was used for external validation, as well as determination of detection performance of experts, trainees and trainees with AI support. According to the AI consultation distribution, images were stratified into “easy” and “difficult”.
Results
Internal validation showed 82%, 85% and 84% for sensitivity, specificity and accuracy. External validation showed 90%, 76% and 84%. The algorithm was significantly more sensitive and accurate than trainees, trainees with AI support and experts in endoscopy. AI support in trainees was associated with significantly improved performance. While all endoscopists showed significantly lower detection for “difficult” images, AI performance remained stable.
Conclusions
The algorithm outperformed trainees and experts in sensitivity and accuracy for VA detection. The significant improvement with AI support suggests a potential clinical benefit. Stable performance of the algorithm in “easy” and “difficult” test images may indicate an advantage in macroscopically challenging cases.
Aims
Evaluation of the add-on effect an artificial intelligence (AI) based clinical decision support system has on the performance of endoscopists with different degrees of expertise in the field of Barrett's esophagus (BE) and Barrett's esophagus-related neoplasia (BERN).
Methods
The support system is based on a multi-task deep learning model trained to solve a segmentation and several classification tasks. The training approach represents an extension of the ECMT semi-supervised learning algorithm. The complete system evaluates a decision tree between estimated motion, classification, segmentation, and temporal constraints, to decide when and how the prediction is highlighted to the observer. In our current study, ninety-six video cases of patients with BE and BERN were prospectively collected and assessed by Barrett's specialists and non-specialists. All video cases were evaluated twice – with and without AI assistance. The order of appearance, either with or without AI support, was assigned randomly. Participants were asked to detect and characterize regions of dysplasia or early neoplasia within the video sequences.
Results
Standalone sensitivity, specificity, and accuracy of the AI system were 92.16%, 68.89%, and 81.25%, respectively. Mean sensitivity, specificity, and accuracy of expert endoscopists without AI support were 83,33%, 58,20%, and 71,48 %, respectively. Gastroenterologists without Barrett's expertise but with AI support had a comparable performance with a mean sensitivity, specificity, and accuracy of 76,63%, 65,35%, and 71,36%, respectively.
Conclusions
Non-Barrett's experts with AI support had a similar performance as experts in a video-based study.
Aims
AI has proven great potential in assisting endoscopists in diagnostics, however its role in therapeutic endoscopy remains unclear. Endoscopic submucosal dissection (ESD) is a technically demanding intervention with a slow learning curve and relevant risks like bleeding and perforation. Therefore, we aimed to develop an algorithm for the real-time detection and delineation of relevant structures during third-space endoscopy.
Methods
5470 still images from 59 full length videos (47 ESD, 12 POEM) were annotated. 179681 additional unlabeled images were added to the training dataset. Consequently, a DeepLabv3+ neural network architecture was trained with the ECMT semi-supervised algorithm (under review elsewhere). Evaluation of vessel detection was performed on a dataset of 101 standardized video clips from 15 separate third-space endoscopy videos with 200 predefined blood vessels.
Results
Internal validation yielded an overall mean Dice score of 85% (68% for blood vessels, 86% for submucosal layer, 88% for muscle layer). On the video test data, the overall vessel detection rate (VDR) was 94% (96% for ESD, 74% for POEM). The median overall vessel detection time (VDT) was 0.32 sec (0.3 sec for ESD, 0.62 sec for POEM).
Conclusions
Evaluation of the developed algorithm on a video test dataset showed high VDR and quick VDT, especially for ESD. Further research will focus on a possible clinical benefit of the AI application for VDR and VDT during third-space endoscopy.
Introduction
Shear induced multimerisation of von-Willebrand-factor (vWF) is supposed to play an important role in coagulation inside extracorporeal membrane oxygenators. However, there is no proof that links observed vWF structures to computed or measured flow conditions.
Methods
The structures of multimeric vWF fibers, observed in clinically used membrane oxygenators is examined using immunofluorescence microscopy (IFM) using Carstairs’ staining method (positive ethics committee vote). The flow around the membrane fibres inside the oxygenator is investigated in terms of shear rate, wall shear velocity and streamlines by using CFD (RANS, Carreau-Yasuda viscosity, geometry remodelled after high-resolution µCT-scans). By interpreting the histological and numerical results in this common context, indications for shear induced coagulation mechanisms can be identified.
Results
The fibre structures of multimeric vWF build regular but not exactly symmetric formations around the contact face (CF) between the crosswise stacked oxygenator fibres (OF), see fig.1B, vWF marked red. Annular around the CF arranged, cells are likely to be found, see fig.1B, nuclei marked blue.
The computed streamlines around the OF show attached flow around the circular fibres. However, the irregular arrangement of real OF produce considerable cross flow between the interconnected neighbouring channels, in contrast to previous 2D-simulations. Thus, the CF are washed around closely by blood, also from neighbouring channels. The wall shear velocity streamlines form regular, slightly asymmetric shapes around the contact faces. The occurring maximum shear rates are in the range of 1,000 1/s.
Discussion
The shapes of vWF structures found in clinically used oxygenators match the computational results in terms of wall shear velocity and streamlines well. The accumulation of cells close to the CF can also be explained by fluid mechanics, as there are small shear gradients and slow velocities. However, occurring shear rates between OFs are too low to trigger multimerisation of vWF. That raises the question where in the circuit the actual activation of vWF is started and how, at least partly chained, vWF multimeres are attracted towards the OF surface. A next step will be the investigation of the actual shear rate triggered (or mediated) multimerisation of vWF. Towards this end, microfluidic experiments with shear triggered coagulation will be performed. Also of big interest is the computation of the flow situation in the oxygenator in proximity to chaining threads, which have been ignored in computations so far. However, first a realistic representation of the effective viscosity in computations is needed, which is not available yet.
One major goal of green-field factory planning is to decide on space requirements in the plant. In this phase, detailed information about the supply chain network (e.g. which suppliers deliver which parts) is often unavailable. Nevertheless, typical decisions in supply chain management, like the definition of replenishment processes and quantities or make-or-buy decisions, impact space requirements in the factory and should therefore be considered in the factory planning. This research article proposes a simulation approach for a factory simulation in which raw material replenishment is integrated to evaluate the space requirements for raw materials.
Feasibility of detecting thrombotic deposits in membrane oxygenators using micro computed tomography
(2019)
To detect convective gas flow inside the large airways during high frequency oscillated ventilation (HFOV) the fluorinated contrast gas Heptafluoropropane was used for 19F-MRI. In a first study the comparison between constant flow measurements and Computational Fluid Dynamics (CFD) simulations provided a good agreement. In a following experiment oscillated flow was applied to a lung phantom consisting of ventilation bag and long pipe. The pressure wave inside the pipe was explored point-by-point and corresponding velocities were determined. With these experiments it could be shown for the first time that flow measurement during HFOV using fluorinated contrast gas is feasible.
The special wing geometry of dragonflies consisting of veins and a membrane forming a corrugated profile leads to special aerodynamic characteristics. To capture the governing flow regimes of a dragonfly wing in detail, a realistic wing model has to be investigated. Therefore, this study aimed to analyze the aerodynamic characteristics of a 3D dragonfly wing reconstructed from a high-resolution micro-CT scan. Afterwards, a spatially high discretized mesh was generated using the mesh generator CENTAUR™ 14.5.0.2 (CentaurSoft, Austin, TX, US) to finally conduct Computational Fluid Dynamics (CFD) investigations in Fluent® 2020 R2 (ANSYS, Inc., Canonsburg, PA, US). Due to the small dimensions of the wing membrane, only the vein structure of a Camacinia Gigantea was captured at a micro-CT voxel size of 7 microns. The membrane was adapted and connected to the vein structure using a Boolean union operation. Occurring nconsistencies after combining the veins and the membrane were corrected using an adapted pymesh script [1]. As an initial study, only one quarter of the wing (outer wing section) was investigated to reduce the required computational effort. The resulting hybrid mesh consisting of 10 pseudo-structured prism layers along the wing surface and tetrahedra in the farfield area has 43 mio. nodes. The flow around the wing was considered to be incompressible and laminar using transient calculations. When the flow passes the vein structures, steady vortices occur in the corrugation valleys leading to recirculation zones. Therefore, the dragonfly wing resembles the profile of an airfoil. This leads to comparable lift coefficients of dragonfly wings and airfoil profiles at significantly reduced structural weight. The reconstructed geometry also included naturally occurring triangular prismlike serrated structures at the leading edge of the wing, which have comparable effects to micro vortex generators and might stabilize the recirculation zones. Further work aims to investigate the aerodynamic properties of a complete dragonfly wing during wing flapping.
Air leakage past High-Volume-Low-Pressure (HVLP) endotracheal tube (ETT) cuffs creates a potential infection risk for health care professionals during ventilation of patients suffering from contagious airborne diseases. However, unlike silent aspiration, a phenomenon where fluids enter the airways of intubated patients, the aspect of aerosol emergence through cuff folds -what we called accordingly “silent breathing” (SB)- has not been investigated in detail so far.
This study investigates air leakage past HVLP cuffs with varying cuff pressures under realistic artificial breathing scenarios experimentally and in addition numerically. The focus was laid on the parametric investigation of the occurrence and furthermore on different influencing factors of silent breathing. The morphology of the folds responsible for the leakage was captured using high-resolution 3D microcomputed tomography (μCT). For the numerical investigations (Com-putational Fluid Dynamics - CFD), the commercial CFD Software package FLUENT 2021 R2 (ANSYS, Inc., Canonsburg, PA, US), as well as the DLR in-house research code THETA has been used.
High Spatial Resolution Tomo-PIV of the Nasopharynx Focussing on the Physiological Breathing Cycle
(2022)
Investigations of complex patient-specific flow in the nasopharynx requires high resolution numerical calculations validated by reliable experiments. When building the validation base and the benchmark
of computational fluid dynamics, an experimental setup of the nasal airways was developed. The applied optical measurement technique of tomo-PIV supplies information on the governing flow field in three dimensions. This paper presents tomo-PIV measurements of the highly complex patient-specific geometry of the human trachea. A computertomographic scan of a person’s head builds the basis of the experimental silicone model of the nasal airways. An optimised approach for precise refractive index matching avoids optical distortions even in highly complex non-free-of-sight 3D geometries. A linear-motor-driven pump generates breathing scenarios, based on measured breathing cycles. Adjusting of the CCD cameras‘ double-frame-rate PIV-Δt enables the detailed analysis of flow structures during different cycle phases. Merging regions of interest enables high spatial resolution acquisition of the flow field.
Surgical smoke has been a little discussed topic in the context of the current pandemic. Surgical smoke is generated during the cauterization of tissue with heat-generating devices and consists of 95% water
vapor and 5% cellular debris in the form of particulate matter. In-vivo investigations are performed during tracheotomies where surgical smoke is produced during tissue electrocautery. Furthermore, in-vitro parametric studies to investigate the particle number and size distribution and the spatial distribution of surgical smoke with laser light sheet technique are conducted. The higher the power of the high-frequency-device the larger the particles in size and the higher the resulting particle counts. The images taken show the densest smoke at 40W with artificial saliva. The resulting characteristic size distribution, which may include viruses and bacterial components, confirms that the risk arising from surgical smoke should be considered. Furthermore, the experiments will provide the database for further numerical investigations.
Modelling blood flow an shear induced coagulation in membraene oxygenators (MO) is challenging. The relevant geometry of oxygenator fibers (OF) and chaining threads is complex and spans several length scales. In relevant scales and regimes blood shows several significant non-Newtonian effects. Existing models are only capable of accounting for some, but not all relevant effects. Additionally, coagulation processes are influencing fluid properties and geometry significantly. Due to the enormous size of the discretised geometries highly detailed viscosity and coagulation properties of blodd flow in MOs. First step is to find a gemoetry dependent viscosity representation on basis of parametric micro channel experiments with anti-coagulated blood. Next step is a statistic coagulation model, based on micro channel experiments with human (re-calcified citrated) whole blood an evaluation of clinically used osygenators. Since shear rate dependent (i.e. viscosity dependet) coagulation in return influences the viscosity, a combined model with suitable implementation in a RANS framework is necessary. Towards this end, micro channel experiments with new and used single OFs triggering coagulation are performed. Structures of multimeric von Willebrand fibers (vWF), as indicator for shear induced coagulation, are compared to computed and measured flow conditions, using immunofluorescence microscopy, RANS-computations and µPIV, respectively. Preliminary examinations in clinically used MOs show good agreement between occurring structures of vWF, cell depositions and computed flow patterns (geometry form µCT-Scans). However, computed shear rates might be to low to actually trigger activation of vWF. The complex geometry of MOs results in huge meshes, which makes RANS with statistical modelling of viscosity and coagulation a reasonable approach. Towards this end, experimental data on micro channel level with evaluation on real application level is crucial. Especially regarding clotting processes, micro fluidic experiments are powerful research tool.
We present two methods that combine image reconstruction and edge detection in computed tomography (CT) scans. Our first method is as an extension of the prominent filtered backprojection algorithm. In our second method we employ ℓ1-regularization for stable calculation of the gradient. As opposed to the first method, we show that this approach is able to compensate for undersampled CT data.
Patients with juvenile retinal dystrophy often report that they are unaware of their central scotoma, suggesting the presence of perceptual filling-in. We used functional Magnetic Resonance Imaging (fMRI) to determine possible neural correlates of perceptual filling-in in patients with retinal distrophy and clinically established central scotoma in both eyes. The data of 5 patients (Stargardt disease, cone-rod dystrophy; mean age 45 yrs; scotoma diameter 10-20°) and of 5 normally sighted controls were analyzed. Fixation behaviour and perimetry were measured with a Nidek microperimeter. Magnetic resonance imaging was performed using a Siemens 3T Allegra scanner. We stimulated the central visual field (30 deg) with a vertically oriented, low spatial frequency (1 c/deg) high-contrast sinewave grating that was either a) continuous, or b) was interrupted by a central grey disk. The disk was either slightly larger than the scotoma (detectable on 75% of trials) or slightly smaller (detectable on 25% of trials). To control for attention, an eccentric fixation task was performed during scanning. Data were analyzed using SPM8 (GLM with ROI analysis to obtain percent signal change for foveal projection zone). Results: for all patients, the BOLD signal in the foveal projection area was significantly higher for the small disk (i.e., condition leading to complete filling-in) than for the large disk (i.e., no filling-in). This effect was absent in the control subjects. Our findings support the existence of an active neural process that leads to filling-in in patients with central visual field scotomata.
Welche Freizeitaktivitäten bedingen sportliche Aktivitäten in einer mediatisierten Gesellschaft?
(2010)
Psychologische Fertigkeiten im Hörgeschädigten-Spitzensport: Analyse des Einsatzes bei Kaderathleten
(2010)
Additive Fertigung hat sich in zahlreichen industriellen Anwendungen etabliert und bildet eine wichtige Schlüsseltechnologie. Im Gegensatz zu metallischen Werkstoffen, haben additiv gefertigte Bauteile aus Kunststoffen geringere Festigkeit und Steifigkeit, sodass sich ihre Verwendung als lasttragende Strukturen schwierig gestaltet. Insbesondere der Einsatz von endlosen Verstärkungsfasern kann die mechanischen Eigenschaften additiv gefertigter Strukturen signifikant verbessern und die Fertigung hochbelastbarer Faserverbundstrukturen im 3D-Druckverfahren ermöglichen. Daher gilt es aktuell notwendige Anlagen und Prozessketten für den Fertigungsprozess aber auch Vorgehensweisen für die belastungsoptimierte Auslegung der Faserverläufe innerhalb des Bauteils zu entwickeln, um so das Themengebiet „Endlosfaserverstärkter 3D-Druck“ weiter voranzutreiben. Für mehr räumliche Freiheit bei der Positionierung der Druckbahnen können industrieroboterbasierte Systeme eingesetzt werden, um so das Potenzial gerichteter Bauweise von Faserverbundstrukturen auch im additiven Fertigungsprozess vollumfänglich ausschöpfen zu können. Dabei ermöglicht ihr Einsatz auch eine räumliche Ablage der Faserverstärkung, wobei für die Materialablage ein geeigneter 3D-Druckkopf erforderlich ist. Für die Implementierung der Faserverstärkung ist zudem eine dem Lastfall entsprechende Auslegung des Bauteils sowie die Ermittlung einer sinnvollen Faserpositionierung innerhalb des Bauteils erforderlich, wobei unterschiedliche Variablen aus den Bereichen Material, Struktur und Fertigungsprozess berücksichtigt werden müssen. Im Rahmen des Vortrages werden die Herausforderungen der Technologieentwicklung des endlosfaserverstärkten 3D-Drucks aufgegriffen sowie auf eine belastungsorientierte Faserpositionierung näher eingegangen. Aktuelle Erkenntnisse werden diskutiert sowie eine Entwurfsmethodik für die Prozesspfadgenerierung vorgeschlagen.
Die RegioStadtbahn Neckar-Alb ist ein großes Verkehrsprojekt in der Region Neckar-Alb mit einem künftigen Schienennetz von 198 Kilometern, davon 38 Kilometer neu oder reaktiviert sowie einem Investitionsvolumen von über 1 Milliarde Euro. Ein erstes Modul geht 2022/23 in Betrieb, während die Innenstadtstrecke durch Tübingen durch einen Bürgerentscheid
gestoppt wurde, vielleicht aber nur vorübergehend. Der Vortrag skizziert das Gesamtprojekt und stellt den Wahlkampf zum Bürgerentscheid vor.
Regensburg ist die größte Stadt im süddeutschen Raum, die ihren öffentlichen Personennahverkehr bisher ohne schienengebundenes Verkehrsmittel organisiert. In Beschlüssen des Stadtrates wie dem Leitbild für Energie und Klima, dem aktuellen Stadtentwicklungsplan
2040 und insbesondere mit einem Grundsatzbeschluss aus dem Jahr 2018 ist die Bedeutung des Stadtbahn-Projektes für eine wachsende Stadt und ein nachhaltig ökologisches öffentliches Verkehrssystem dokumentiert. Der Vortrag informiert über die Organisation der Planung, über besondere Herausforderungen und wichtige Meilensteine sowie die erzielten Fortschritte im Projekt.
Die anstehenden Verkehrsaufgaben werden mit ihren besonderen Herausforderungen an das künftige Stadtbahnsystem in Regensburg dargestellt. Diese haben unmittelbare Auswirkungen auf die Rahmenbedingungen, die das künftige Stadtbahnfahrzeug mit seinen Abmessungen zu erfüllen hat. Anhand verschiedener Fahrzeugkonzepte wird der Weg zum Wunschfahrzeugtyp hergeleitet.
Regensburg steht vor der Herausforderung, den Ansprüchen einer wachsenden Stadt und der nicht zuletzt durch die Entwicklungen im Energiesektor beeinflusste Mobilitätswende mit einem attraktiven und
zukunftsfähigen Mobilitätsangebot gerecht zu werden. Die Entscheidung für ein höherwertiges ÖPNV-System - die Stadtbahn - wurde 2018 getroffen. Das Stadtwerk Regensburg.Mobilität GmbH (SMO) mit der Aufgabe betraut, ein funktionsfähiges ÖPNV-Netz zu konzipieren, das die Stadtbahn durch optimierten Stadtbuseinsatz unter den gegebenen infrastrukturellen Rahmenbedingungen ergänzt. Der Vortrag wird die Strategie vorgestellt, mit der die SMO dieses Ziel erreichen wird.
Seit Mitte der 1980er Jahre werden in Frankreich mit großem Erfolg neue oberirdische Straßenbahnsysteme realisiert, nachdem das Verkehrssystem Straßenbahn vormals landesweit nahezu vollständig verschwunden war. Kennzeichnend für die Projekte sind eine meist komplette Neuordnung städtischer Verkehrsstrukturen sowie eine integrierte Betrachtung von Stadtgestaltung und Verkehrsanlagenbau. Der Vortrag zeichnet die Entwicklungsgeschichte der neuen Systeme sowie deren Planungsleitbilder und Erfolgsfaktoren nach.
In practical use cases, simulation engineers are confronted with uncertainties in the simulation parameters. Normally, trust in a model is built from experience, practical assumptions, and parameter studies. This approach, though, is based on the assumption that few parameter combinations are sufficient to represent the whole design space. This lacks an appraisable mathematical basis.
To get insights into which parameters most strongly affect the results, a global sensitivity analysis can be conducted [2, 3]. The results are utilized to rank the most influential parameters and to filter less relevant ones. This gives feedback which improved set of input data will lead to more certainty in the simulation results. To enable this in the framework of multi-fidelity analysis and optimization, we compare here global sensitivities and uncertainties of the implicit One-Step approach (low-fidelity) with those of the explicit multi-step deep drawing approach (high-fidelity).
Clinical setting
Third space procedures such as endoscopic submucosal dissection (ESD) and peroral endoscopic myotomy (POEM) are complex minimally invasive techniques with an elevated risk for operator-dependent adverse events such as bleeding and perforation. This risk arises from accidental dissection into the muscle layer or through submucosal blood vessels as the submucosal cutting plane within the expanding resection site is not always apparent. Deep learning algorithms have shown considerable potential for the detection and characterization of gastrointestinal lesions. So-called AI – clinical decision support solutions (AI-CDSS) are commercially available for polyp detection during colonoscopy. Until now, these computer programs have concentrated on diagnostics whereas an AI-CDSS for interventional endoscopy has not yet been introduced. We aimed to develop an AI-CDSS („Smart ESD“) for real-time intra-procedural detection and delineation of blood vessels, tissue structures and endoscopic instruments during third-space endoscopic procedures.
Characteristics of Smart ESD
An AI-CDSS was invented that delineates blood vessels, tissue structures and endoscopic instruments during third-space endoscopy in real-time. The output can be displayed by an overlay over the endoscopic image with different modes of visualization, such as a color-coded semitransparent area overlay, or border tracing (demonstration video). Hereby the optimal layer for dissection can be visualized, which is close above or directly at the muscle layer, depending on the applied technique (ESD or POEM). Furthermore, relevant blood vessels (thickness> 1mm) are delineated. Spatial proximity between the electrosurgical knife and a blood vessel triggers a warning signal. By this guidance system, inadvertent dissection through blood vessels could be averted.
Technical specifications
A DeepLabv3+ neural network architecture with KSAC and a 101-layer ResNeSt backbone was used for the development of Smart ESD. It was trained and validated with 2565 annotated still images from 27 full length third-space endoscopic videos. The annotation classes were blood vessel, submucosal layer, muscle layer, electrosurgical knife and endoscopic instrument shaft. A test on a separate data set yielded an intersection over union (IoU) of 68%, a Dice Score of 80% and a pixel accuracy of 87%, demonstrating a high overlap between expert and AI segmentation. Further experiments on standardized video clips showed a mean vessel detection rate (VDR) of 85% with values of 92%, 70% and 95% for POEM, rectal ESD and esophageal ESD respectively. False positive measurements occurred 0.75 times per minute. 7 out of 9 vessels which caused intraprocedural bleeding were caught by the algorithm, as well as both vessels which required hemostasis via hemostatic forceps.
Future perspectives
Smart ESD performed well for vessel and tissue detection and delineation on still images, as well as on video clips. During a live demonstration in the endoscopy suite, clinical applicability of the innovation was examined. The lag time for processing of the live endoscopic image was too short to be visually detectable for the interventionist. Even though the algorithm could not be applied during actual dissection by the interventionist, Smart ESD appeared readily deployable during visual assessment by ESD experts. Therefore, we plan to conduct a clinical trial in order to obtain CE-certification of the algorithm. This new technology may improve procedural safety and speed, as well as training of modern minimally invasive endoscopic resection techniques.
Barrett-Ampel
(2022)
Hintergrund
Adenokarzinome des Ösophagus sind bis heute mit einer infausten Prognose vergesellschaftet (1). Obwohl Endoskopiker mit Barrett-Ösophagus als Präkanzerose konfrontiert werden, ist vor allem für nicht-Experten die Differenzierung zwischen Barrett-Ösophagus ohne Dysplasie und assoziierten Neoplasien mitunter schwierig. Existierende Biopsieprotokolle (z.B. Seattle Protokoll) sind oftmals unzuverlässig (2). Eine frühzeitige Diagnose des Adenokarzinoms ist allerdings von fundamentaler Bedeutung für die Prognose des Patienten.
Forschungsansatz
Auf der Grundlage dieser Problematik, entwickelten wir in Kooperation mit dem Forschungslabor „Regensburg Medical Image Computing (ReMIC)“ der OTH Regensburg ein auf künstlicher Intelligenz (KI) basiertes Entscheidungsunterstützungssystem (CDSS). Das auf einer DeepLabv3+ neuronalen Netzwerkarchitektur basierende CDSS differenziert mittels Mustererkennung Barrett- Ösophagus ohne Dysplasie von Barrett-Ösophagus mit Dysplasie bzw. Neoplasie („Klassifizierung“). Hierbei werden gemittelte Ausgabewahrscheinlichkeiten mit einem vom Benutzer definierten Schwellenwert verglichen. Für Vorhersagen, die den Schwellenwert überschreiten, berechnen wir die Kontur der Region und die Fläche. Sobald die vorhergesagte Läsion eine bestimmte Größe in der Eingabe überschreitet, heben wir sie und ihren Umriss hervor. So ermöglicht eine farbkodierte Visualisierung eine Abgrenzung zwischen Dysplasie bzw. Neoplasie und normalem Barrett-Epithel („Segmentierung“).
In einer Studie an Bildern in „Weißlicht“ (WL) und „Narrow Band Imaging“ (NBI) demonstrierten wir eine Sensitivität von mehr als 90% und eine Spezifität von mehr als 80% (3). In einem nächsten Schritt, differenzierte unser KI-Algorithmus Barrett- Metaplasien von assoziierten Neoplasien anhand von zufällig abgegriffenen Bildern in Echtzeit mit einer Accuracy von 89.9% (4). Darauf folgend, entwickelten wir unser System dahingehend weiter, dass unser Algorithmus nun auch dazu in der Lage ist, Untersuchungsvideos in WL, NBI und „Texture and Color Enhancement Imaging“ (TXI) in Echtzeit zu analysieren (5).
Aktuell führen wir eine Studie in einem randomisiert-kontrollierten Ansatz an unveränderten Untersuchungsvideos in WL, NBI und TXI durch.
Ausblick
Um Patienten mit aus Barrett-Metaplasien resultierenden Neoplasien frühestmöglich an „High-Volume“-Zentren überweisen zu können, soll unser KI-Algorithmus zukünftig vor allem Endoskopiker ohne extensive Erfahrung bei der Beurteilung von Barrett- Ösophagus in der Krebsfrüherkennung unterstützen.
Einleitung
Übermäßige Bewegung im Bild kann die Performance von auf künstlicher Intelligenz (KI) basierenden klinischen Entscheidungsunterstützungssystemen (CDSS) reduzieren. Optical Flow (OF) ist eine Methode zur Lokalisierung und Quantifizierung von Bewegungen zwischen aufeinanderfolgenden Bildern.
Ziel
Ziel ist es, die Mensch-Computer-Interaktion (HCI) zu verbessern und Endoskopiker die unser KI-System „Barrett-Ampel“ zur Unterstützung bei der Beurteilung von Barrett-Ösophagus (BE) verwenden, ein Echtzeit-Feedback zur aktuellen Datenqualität anzubieten.
Methodik
Dazu wurden unveränderte Videos in „Weißlicht“ (WL), „Narrow Band Imaging“ (NBI) und „Texture and Color Enhancement Imaging“ (TXI) von acht endoskopischen Untersuchungen von histologisch gesichertem BE und mit Barrett-Ösophagus assoziierten Neoplasien (BERN) durch unseren KI-Algorithmus analysiert. Der zur Bewertung der Bildqualität verwendete OF beinhaltete die mittlere Magnitude und die Entropie des Histogramms der Winkel. Frames wurden automatisch extrahiert, wenn die vordefinierten Schwellenwerte von 3,0 für die mittlere Magnitude und 9,0 für die Entropie des Histogramms der Winkel überschritten wurden. Experten sahen sich zunächst die Videos ohne KI-Unterstützung an und bewerteten, ob Störfaktoren die Sicherheit mit der eine Diagnose im vorliegenden Fall gestellt werden kann negativ beeinflussen. Anschließend überprüften sie die extrahierten Frames.
Ergebnis
Gleichmäßige Bewegung in eine Richtung, wie etwa beim Vorschieben des Endoskops, spiegelte sich, bei insignifikant veränderter Entropie, in einer Erhöhung der Magnitude wider. Chaotische Bewegung, zum Beispiel während dem Spülen, war mit erhöhter Entropie assoziiert. Insgesamt war eine unruhige endoskopische Darstellung, Flüssigkeit sowie übermäßige Ösophagusmotilität mit erhöhtem OF assoziiert und korrelierte mit der Meinung der Experten über die Qualität der Videos. Der OF und die subjektive Wahrnehmung der Experten über die Verwertbarkeit der vorliegenden Bildsequenzen korrelierten direkt proportional. Wenn die vordefinierten Schwellenwerte des OF überschritten wurden, war die damit verbundene Bildqualität in 94% der Fälle für eine definitive Interpretation auch für Experten unzureichend.
Schlussfolgerung
OF hat das Potenzial Endoskopiker ein Echtzeit-Feedback über die Qualität des Dateninputs zu bieten und so nicht nur die HCI zu verbessern, sondern auch die optimale Performance von KI-Algorithmen zu ermöglichen.
The primary consumers of plant exudates – in exact fungi and bacteria, are representative for the soil succession level from bare soil, which is bacterial dominant to old growth forest constitute by fungal dominance. In a specified level of soil succession, a special kind of plant family benefits on the Fungal to Bacteria Ratio. The ability to determine this ratio in situ without complex chemical applications is part of the project Electronical Laboratory for Intelligent Soil Examination (ELISE). Several mechanical and optical tests on soil samples are covered within this Project. To analyze the fungal to bacteria ratio, samples are prepared automatically – in a defined and reproductive procedure – to generate slides for shadowing microscopy. The samples are observed by a camera, which is attached to a transmitted light microscope. The automatic analysis, done with computer vision algorithms, aims to quantify bacterial and fungal biomass in the actual sample view. Moreover, the algorithm can classify organisms according to their color and shape.
To get a processable picture, several images from different focal levels must be taken through the sample thickness. Parts of each image, are in focus at the actual layer, are merged to a whole depth of field picture, by focus stacking.
This produced picture is used to classify, locate and quantify – in first step filamentous organisms e.g. fungal by image sematic segmentation. The result represents an image sized mask, which indicates the class of fungi with class equivalate values at the pixel positions – covered by the organism. This information is used to calculate the fungal mass per gram soil.
To quantify the bacterial biomass two approaches are implemented. For low density of bacterial existence, the individual bacteria is counted for a part of the field of view by an image detection algorithm to be extrapolate afterwards to the mass per gram soil. For high density of bacterial occurrence, specified regions of interest with only bacteria present are chosen. An image classification which has been pretrained by pictures of bacterial density patterns – previously determent by making the sample countable due to preforming sample dilutions, is done. The second option for high density bacterial count is, to automatically preform dilutions until the image detection is confidently countable.
To ensure a usable confidence score a statistical approach of many fields of view is taken.
The sample preparation for shadowing microscopy, to examinate biological soil properties, is time consuming, manual work. The outcome depends on subjective skills of the operator, furthermore the results are mostly not quantitative. The database on biological soil properties is mostly not sufficient for an integrated modelling on an multidisciplinary scale. This project combines three progressive approaches to develop a tool that is easy to use and gives in situ results that can be used for many purposes.
Background:
Brain lesions in language-related cortical areas remain a challenge in the clinical routine. In recent years the resting-state fMRI (rs-fMRI) was shown to be a feasible method for preoperative language assessment. The aim of this study was to examine whether language-related resting-state components, which have been obtained using a data-driven independent-component-based identification algorithm, can be supportive in determining language dominance in the left or right hemisphere.
Methods:
Twenty patients suffering from brain lesions close to supposed language relevant cortical areas were included. Rs-fMRI and task-based (tb-fMRI) were performed for the purpose of preoperative language assessment. Tb-fMRI included a verb generation task with an appropriate control condition (a syllable switching task) to decompose language critical and language supportive processes. Subsequently, the best fitting ICA component for the resting-state language network (RSLN) referential to general linear models (GLMs) of the tb-fMRI (including models with and without linguistic control conditions) was identified using an algorithm based on the Dice-index.
Results:
The RSLNs associated with GLMs using a linguistic control condition led to significantly higher laterality indices than GLM baseline contrasts. LIs derived from GLM contrasts with and without control conditions alone did not differ significantly.
Conclusion:
In general, the results suggest that determining language dominance in the human brain is feasible both with tb-fMRI and rs-fMRI, and in particular, the combination of both approaches yields a higher specificity in preoperative language assessment. Moreover, we can conclude that the choice of the language mapping paradigm is crucial for the mentioned benefits.
Ansätze zur Verbesserung der allgemeinen palliativpflegerischen Versorgung onkologischer Patienten
(2020)
Fragestellung
Der Großteil der onkologischen Palliativpatienten im Krankenhaus wird auf Allgemeinstationen im Rahmen der allgemeinen Palliativversorgung (APV) versorgt. Die Studie untersucht Defizite und mögliche Unterstützungsangebote für Pflegekräfte, die einen großen Teil der Versorgung leisten.
Studiendesign
Befragung mit halbstandardisiertem Fragebogen am Universitären Krebszentrum. Anonyme Befragung aller 379 Pflegekräfte, die auf Allgemeinstationen mit onkologischem Schwerpunkt arbeiten.
Methodik
Quantitative Auswertung der geschlossenen Fragen. Qualitative Inhaltsanalyse nach Mayring der offenen Fragen.
Ergebnis
Die Rücklaufquote betrug 28 % (n = 104), davon 90 % ohne Palliative Care Weiterbildung. Nur 34 % der teilnehmenden Pflegekräfte schätzen die APV auf ihrer Station als sehr gut oder gut ein. Als wesentliche Defizite werden neben Personal- und Zeitmangel (81 %) mehrheitlich (52 %) auch unzureichendes Fachwissen angegeben. 87 % wünschen sich fachliche Unterstützung in der palliativpflegerischen Routine-Versorgung. 77 % wäre es eine Hilfe, wenn palliative Pflegeexperten einmal pro Woche oder häufiger vor Ort unterstützen würden. Dabei werden weniger die Übernahme von Tätigkeiten (22 %), als vor allem Anleitung und Befähigung präferiert (Medikamentenmanagement 48 %, Angehörigenversorgung 64 %, Pflegeinterventionen 65 %).
Diskussion
Pflegende möchten ihre Rolle in der APV wahrnehmen und erachten dazu neben mehr Zeit-/Personalressource vor allem Wissenstransfer und Unterstützung durch Pflegeexperten als hilfreich. Dies kann durch sehr erfahrene und intuitiv handelnde Palliativkräfte (z. B. Kompetenzstufe 5, [1]) und durch Advanced Practice Nurse (APN, [2]) mit theorie- und wissenschaftsbasierter praxisnaher Pflege gelingen.
Field induced plasticity of magneto-sensitive elastomers for gripping technology applications
(2018)
In this work, a simple cost effective method to create tunable self-assembled three-dimensional nanostructure array-like nanoantennas on a tip of an optical quartz glass fiber is described. The structures are prepared using lithography-less dry etching. Gold nanoparticles are used as an etching mask using a thin metal-film thermal dewetting technique. The structures are applied as sensor for label-free analysis of organic substances in ppb range, such as drug residues in groundwater. The measurements are carried out by means of a Surface-enhanced Raman scattering (SERS) effect, an exchangeable sensor head, and a portable Raman device. This method allows in situ applications. Parathiocresol is used as a model substance to characterize the SERS cells. For metallization, gold and silver are compared. Reproducible SERS enhancement factors up to 10 7 are evaluated.
Introduction
We present a clinical case showing the real-time detection, characterization and delineation of an early Barrett’s cancer using AI.
Patients and methods
A 70-year old patient with a long-segment Barrett’s esophagus (C5M7) was assessed with an AI algorithm.
Results
The AI system detected a 10 mm focal lesion and AI characterization predicted cancer with a probability of >90%. After ESD resection, histopathology showed mucosal adenocarcinoma (T1a (m), R0) confirming AI diagnosis.
Conclusion
We demonstrate the real-time AI detection, characterization and delineation of a small and early mucosal Barrett’s cancer.
To investigate the role of heuristics in the domain of software engineering, an eye tracking study was conducted in which experts and novices were compared. The study focused on one of the most challenging parts in this domain: the generation of an object model for a software product based on a requirements specification. During their training, software engineers are taught different techniques to solve this task. One of these techniques is the noun/verb analysis.
However, it is still unclear to what extent novice and expert programmers are making use of it. Ideally, the noun/verb analysis works as a heuristic and helps programmers to make fast and accurate decisions. Participants in the study were 40 software programmers at four levels of expertise (novices, intermediates, experienced rogrammers, experts). They were presented with ten decision tasks. In each task, participants read a requirement specification and then had to choose one out of three presented class diagrams that they considered the best solution. During the task, their eye movements were recorded. Results show that all participants used the noun/verb analysis as a heuristic. Programmers with higher levels of expertise, however, outperformed programmers with lower levels of expertise. Interestingly, the more experienced programmers were not following the noun/verb analysis in a blindfolded way. They realised that the noun/verb analysis would produce diagrams, but a skilled software architect would not model them in this way. Instead they created their models in a way that they perceived as more logical and realistic
Second cancer risk after radiation of localized prostate cancer with and without flattening filter
(2017)
The partitioning hypervisor Jaihouse allows us to run safety critical and uncritical applications in parallel on a single SoC. We present our experiences when porting a safety and real-time critical existing application as a Jailhouse guest. It shows a novel and promising approach for implementing mixed-criticality applications with real-time requirement while not loosing the benefits of Linux. This is done by static partitioning of hardware resources; guests do not interfere. We will present a multicopter platform running the real-time critical flight stack in an isolated Jailhouse guest. This proves the practicability of Jailhouse as well as the suitability for real-time safety critical systems by porting an existing application to a Jailhouse cell. We stress its concept and show up current hardware limitations, like undesired behaviour and present possible workarounds and solutions.
Introduction: Consultant Effectiveness is one of the main pillars of successfully implementing Psychological Skills Training (PST) in both elite mainstream and disability sports (Martin, 2015;
Vealey, 2007). Yet, there is a dearth of research on the articularities of PST consultation in elite Deaf Sport (Schliermann, 2015). Since hearing impairments have multiple intrapsychic as
well as interpsychic implications (Leigh & Andrews, 2016), it might influence Consultant Effectiveness in Deaf Sport. Therefore, the main purpose of this exploratory study was to investigate
preconceptions and actual experience of sport psychologists in regard to the consultation of elite deaf and hard-of-hearing (HOH) athletes. Method: A self-constructed questionnaire was administrated to 93 accredited sport psychology consultants (Mconsultant-years = 10.78) in
German-speaking countries. They were asked about their awareness, opinions and experience with deaf and HOH athletes. Results: Seven consultants (8%) reported ever to have worked with a deaf or HOH athlete. Four had ever watched a competition of the Deaflympics
in comparison to 77 who had watched one of the Paralympic games. Yet most subjects (71%) declared their willingness to work with deaf or HOH athletes regardless of their years of experience as consultants or prior contact with deaf or HOH individuals. In addition, the vast majority of the sample evaluated the implementation of common PST techniques in Deaf Sport as potentially “very effective” or “extremely effective”. When asked about potential particularities in the work with this group of athletes, most consultants focused mainly on communication issues. Conclusion: The analyses revealed a lack of awareness of elite Deaf Sport within the professional sport psychology communities in Germany, Austria and Switzerland. Furthermore, a low rate of professional PST implementation in elite Deaf Sport was found in these countries. Considering the lack of experience and knowledge about the particularities in Deaf Sport, further research is needed to provide hearing consultants with tools to understand the world of elite deaf and HOH athletes beyond the means of communication with them.