Regensburg Center of Health Sciences and Technology - RCHST
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Introduction
Dislocation of the shoulder joint is one of the more common complications after reverse total shoulder arthroplasty [1], which is often associated with malposition of the prosthetic components [2]. Therefore, achieving sufficient shoulder stability should not be neglected when positioning the implant components. One parameter for assessing shoulder stability can be shoulder stiffness. The aim of this work is to develop a reverse shoulder implant prototype that allows intraoperative measurement of shoulder stiffness while varying the position of the implant components. The measured stiffness could provide a quantitative statement regarding the optimal positioning of the implant components, which can be adjusted accordingly in the final reverse shoulder prosthesis.
Methods
To measure the stiffness of the shoulder joint, it is necessary to record the joint angles and the torques generated during movement. The changes in the rotation angles were measured using 3D hall sensors and magnets. The magnets were placed under the humerosocket, and the hall sensors were integrated into the glenosphere. The strength of the magnetic field was used to determine the position of the humerosocket in relation to the glenosphere. The accuracies of the angle measurements were tested using a test bench.
Three thin film pressure sensors were used to record forces at different points under the humerosocket. To obtain a force value from the sensor signal, the sensors were calibrated using a load cell. The variation of the implant components positions was integrated into the prototype implant through different constructive mechanisms to adjust the stiffness of the shoulder joint.
Results
In the range of ±45° flexion/extension combined with ±15° adduction/abduction, the joint position could be determined with sufficient accuracy (error e ≤ 5°). The areas near the combined maximum deflections of ±45° flexion/extension and ±45° adduction/abduction indicate the greatest deviation from the target angle. The force values of the thin film sensors enable the calculation of moments around two axes. As variable component position parameters, the tray offset, the neck-shaft angle and the humerus version were integrated into the implant prototype.
Discussion
Ideally, the accuracy of the angle measurements should only depend on the amount of deflection and not on the direction of deflection. The asymmetric behavior indicates a deviation from the correct positioning of the hall sensors. The application of a calibration matrix could compensate for the measurement errors and could demonstrate the potential of the new method for joint angle measurements. The accuracy of the torque measurements and the functionality of the mechanical arresting mechanisms must be investigated in further studies. Overall, the developed measurement method can help to avoid malpositioning of the implant components in reverse total shoulder arthroplasty.
References
1. Clark et al, J Shoulder and elbow surgery, 21:36-41 2012.
2. Randelli et al, J Musculoskeletal surgery, 98:15-18, 2014.
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.
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.
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.
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.
Sind Impfbereitschaft und Impfablehnung rational erklärbar?
Und wenn ja: Welche Rolle spielen dabei Verschwörungsüberzeugungen
und die Nutzung sozialer Medien?
Welchen Einfluss haben Freunde und Bekannte und das
Wohl der Gesellschaft? Und inwieweit vertraut die Bevölkerung
in Deutschland überhaupt in Impfungen und staatliche
Institutionen wie das Robert Koch-Institut?
Aufgrund fehlender Informationen zu tatsächlich eingesetzten digitalen Assistenztechniken in der ambulanten und stationären Pflege sind die Auswirkungen auf die Pflegepraxis und Pflegepersonal dieser Systeme weitestgehend unerschlossen. Das Projekt DAAS-KIN (Diffusion altersgerechter Assistenzsysteme – Kennzahlenerhebung und Identifikation von Nutzungshemmnissen) untersucht mittels eines Mixed-Methods-Ansatzes (Fragebogen, Experteninterviews, Wertbaumanalyse) Verbreitung, Reaktion und Auswirkung digitaler Assistenzsysteme sowie potentielle Nutzungs- und Diffusionshemmnisse
Die Forschungspyramide ist ein Modell als Basis für Clinical Reasoning und Clinical Decision Making in der Physiotherapie. Es basiert auf einer Dekonstruktion der klassischen Hierarchie der externen Evidenz und einer Rekonstruktion der Evidenzhierarchie nach den Kriterien interne und externe Validität (experimentelle und beobachtende Studien) sowie nach den Kriterien Abstraktion und Konkretion (quantitative und qualitative Studien). Dabei bestehen zwischen den Seiten teilweise fließende Übergängen.
Experimentelle Studien werden in der Forschungspyramide als Studien definiert, in denen die Intervention (die unabhängige Variable) zum Zwecke der Studie manipuliert wird. Beobachtende Studien hingegen sind darauf angelegt, den Untersuchungsgegenstand einschl. der unabhängigen Variablen durch die Studie zwar zu erfassen, aber möglichst wenig zu beeinflussen. Beide Varianten quantitativer Studien untersuchen unmittelbar quantifizierbare Merkmale, qualitative Studien hingegen arbeiten mit akustischem (verbalem) und optischem (Beobachtungen) Material, welches zunächst einem Interpretationsprozess durch die Forscher unterzogen werden muss. Qualitative Untersuchungsergebnisse sind prinzipiell quantifizierbar, umgekehrt sind quantitativ erhobene Daten einer qualitativen Interpretation nicht zugänglich.
Somit ergeben sich vier Hierarchien bezogen auf die interne Validität der Studiendesigns, welche jeweils unterschiedlich abstrakte bzw. konkrete Aussagen sowie intern bzw. extern valide Studienergebnisse ermöglichen: experimentelle und beobachtende quantitative bzw. qualitative Studien. Diese bilden – in der neuen Fassung – vier Seiten der Forschungspyramide. Die höchste Stufe der externen Evidenz ist so nur zu erreichen, wenn Studien mit höchstmöglicher interner Validität von allen vier Seiten vorliegen und in einem systematischen Review (quantitativ-qualitative Meta-Analyse) integriert wurden.
Systematische Reviews, die auf der Grundlage der Forschungspyramide durchgeführt werden, bilden den zum jeweiligen Zeitpunkt der Recherche höchsten Grad an externer Evidenz ab. Sie bieten aktuelle Informationen zu dem Wirkungspotenzial einer Intervention unter idealen Rahmenbedingungen und zu den bislang erzielten Wirkungen unter alltäglichen (ggf. auch unterschiedlichen) Rahmenbedingungen. Sie stellen gleichzeitig abstrakte, statistisch abgesicherte Informationen zur Verfügung, die den Grad der Sicherheit bzw. Unsicherheit beim Transfer der auf ihrer Grundlage gewonnenen Handlungsempfehlungen auf einen einzelnen Patienten quantifizieren, sowie konkrete, interpretativ abgesicherte Informationen, die helfen, die auf der Erfahrung beruhenden Handlungsintentionen zu systematisieren und mögliche Handlungsoptionen aufzeigen. Durch solchermaßen ausgearbeitet systematische Reviews erhöht sich die Wahrscheinlichkeit, im Einzelfall mit dem Patienten gemeinsam eine adäquate Behandlungsentscheidung zu treffen.
Diese grundsätzlichen Überlegungen sollen an einem Beispiel exemplifiziert werden. Um den potenziellen und tatsächlichen Einfluss von Physiotherapie oder interdisziplinären Konzepten, die Physiotherapie als Schwerpunkt beinhalten, auf die Rückkehr an den Arbeitsplatz bei Patienten mit Beschwerden des unteren Rückens zu evaluieren, wurde ein Review durchgeführt, der die beiden quantitativen Seiten der Forschungspyramide zusammenführt. Die systematische Suche in den Datenbanken CINAHL und PubMed zeigte mit 44 experimentellen und 15 beobachtenden Arbeiten, von denen zudem sieben einarmig durchgeführt waren, ein erhebliches Ungleichgewicht, sodass nur für zwei Interventionen Studien aus beiden Forschungsansätzen berücksichtigt werden konnten. Für beide ergaben sich Hinweise auf Variationen der Effektgröße und -richtung bei Gegenüberstellung experimentell und beobachtend gewonnener Daten. Sollten sich solche Variationen bei einer größeren Anzahl von Studien systematisch zwischen beiden Studientypen unterscheiden, so müssten die Ursachen der Diskrepanz zwischen der Wirksamkeit unter Idealbedingungen und der Wirkung unter realen Bedingungen zu analysieren in einem nächsten Schritt experimentelle und beobachtende qualitative Studien durchgeführt und in die Ergebnisse integriert werden. Auf diesem Weg könnten die Einflussfaktoren auf die Umsetzbarkeit einer potenziell wirksamen Intervention im alltäglichen Einsatz als erkennbare Muster eruierbar werden.
Goal: The Research Pyramid is a model that values and integrates external evidence from multiple research approaches: experimental and observational as well as quantitative and qualitative.
It provides a basis to collect and synthesize research findings for answering questions that emerge in therapy practice and for subsequent decision making, based on research evidence.
Aims
Celiac disease (CD) is a complex condition caused by an autoimmune reaction to ingested gluten. Due to its polymorphic manifestation and subtle endoscopic presentation, the diagnosis is difficult and thus the disorder is underreported. We aimed to use deep learning to identify celiac disease on endoscopic images of the small bowel.
Methods
Patients with small intestinal histology compatible with CD (MARSH classification I-III) were extracted retrospectively from the database of Augsburg University hospital. They were compared to patients with no clinical signs of CD and histologically normal small intestinal mucosa. In a first step MARSH III and normal small intestinal mucosa were differentiated with the help of a deep learning algorithm. For this, the endoscopic white light images were divided into five equal-sized subsets. We avoided splitting the images of one patient into several subsets. A ResNet-50 model was trained with the images from four subsets and then validated with the remaining subset. This process was repeated for each subset, such that each subset was validated once. Sensitivity, specificity, and harmonic mean (F1) of the algorithm were determined.
Results
The algorithm showed values of 0.83, 0.88, and 0.84 for sensitivity, specificity, and F1, respectively. Further data showing a comparison between the detection rate of the AI model and that of experienced endoscopists will be available at the time of the upcoming conference.
Conclusions
We present the first clinical report on the use of a deep learning algorithm for the detection of celiac disease using endoscopic images. Further evaluation on an external data set, as well as in the detection of CD in real-time, will follow. However, this work at least suggests that AI can assist endoscopists in the endoscopic diagnosis of CD, and ultimately may be able to do a true optical biopsy in live-time.
Aims
Eosinophilic esophagitis (EoE) is easily missed during endoscopy, either because physicians are not familiar with its endoscopic features or the morphologic changes are too subtle. In this preliminary paper, we present the first attempt to detect EoE in endoscopic white light (WL) images using a deep learning network (EoE-AI).
Methods
401 WL images of eosinophilic esophagitis and 871 WL images of normal esophageal mucosa were evaluated. All images were assessed for the Endoscopic Reference score (EREFS) (edema, rings, exudates, furrows, strictures). Images with strictures were excluded. EoE was defined as the presence of at least 15 eosinophils per high power field on biopsy. A convolutional neural network based on the ResNet architecture with several five-fold cross-validation runs was used. Adding auxiliary EREFS-classification branches to the neural network allowed the inclusion of the scores as optimization criteria during training. EoE-AI was evaluated for sensitivity, specificity, and F1-score. In addition, two human endoscopists evaluated the images.
Results
EoE-AI showed a mean sensitivity, specificity, and F1 of 0.759, 0.976, and 0.834 respectively, averaged over the five distinct cross-validation runs. With the EREFS-augmented architecture, a mean sensitivity, specificity, and F1-score of 0.848, 0.945, and 0.861 could be demonstrated respectively. In comparison, the two human endoscopists had an average sensitivity, specificity, and F1-score of 0.718, 0.958, and 0.793.
Conclusions
To the best of our knowledge, this is the first application of deep learning to endoscopic images of EoE which were also assessed after augmentation with the EREFS-score. The next step is the evaluation of EoE-AI using an external dataset. We then plan to assess the EoE-AI tool on endoscopic videos, and also in real-time. This preliminary work is encouraging regarding the ability for AI to enhance physician detection of EoE, and potentially to do a true “optical biopsy” but more work is needed.
Pixel-level classification is an essential part of computer vision. For learning from labeled data, many powerful deep learning models have been developed recently. In this work, we augment such supervised segmentation models by allowing them to learn from unlabeled data. Our semi-supervised approach, termed Error-Correcting Supervision, leverages a collaborative strategy. Apart from the supervised training on the labeled data, the segmentation network is judged by an additional network.
Die Einsetzbarkeit der artikulatorischen Diadochokinese als Diagnostikinstrument für Dysarthrien
(2013)
Als Systemerkrankung beeinflusst die neurologische Sprachstörung Aphasie nicht nur die Betroffenen sondern in vergleichbarem Maße auch die Angehörigen. Es zeigt sich, dass die Lebensqualität der Angehörigen beeinträchtigt ist. Ziel der qualitativen Studie ist es, die subjektive Perspektive der Angehörigen auf die eigene Situation näher zu betrachten.