Hybrid Open Access - OA-Veröffentlichung in einer Subskriptionszeitschrift/-medium
Refine
Year of publication
Document Type
- Article (143)
- Part of a Book (1)
- conference proceeding (article) (1)
- conference talk (1)
- conference proceeding (volume) (1)
- Review (1)
Is part of the Bibliography
- no (148)
Keywords
- Shotcrete (4)
- 3D volumetry (3)
- Artificial Intelligence (3)
- Biomechanik (3)
- Durability (3)
- Maschinelles Lernen (3)
- Medical Image Computing (3)
- Photoacoustic spectroscopy (3)
- Acetone breath analysis (2)
- Acetone detection (2)
Institute
- Fakultät Informatik und Mathematik (47)
- Fakultät Maschinenbau (44)
- Regensburg Center of Biomedical Engineering - RCBE (22)
- Fakultät Elektro- und Informationstechnik (18)
- Regensburg Center of Health Sciences and Technology - RCHST (18)
- Fakultät Angewandte Sozial- und Gesundheitswissenschaften (16)
- Regensburg Medical Image Computing (ReMIC) (15)
- Fakultät Bauingenieurwesen (14)
- Fakultät Angewandte Natur- und Kulturwissenschaften (13)
- Labor Biomechanik (LBM) (12)
Begutachtungsstatus
- peer-reviewed (137)
Purpose: In this trial, we used a previously developed prototype software to assess aesthetic results after reconstructive surgery for congenital breast asymmetry using automated anthropometry. To prove the consensus between the manual and automatic digital measurements, we evaluated the software by comparing the manual and automatic measurements of 46 breasts.
Methods: Twenty-three patients who underwent reconstructive surgery for congenital breast asymmetry at our institution were examined and underwent 3D surface imaging. Per patient, 14 manual and 14 computer-based anthropometric measurements were obtained according to a standardized protocol. Manual and automatic measurements, as well as the previously proposed Symmetry Index (SI), were compared.
Results: The Wilcoxon signed-rank test revealed no significant differences in six of the seven measurements between the automatic and manual assessments. The SI showed robust agreement between the automatic and manual methods.
Conclusion: The present trial validates our method for digital anthropometry. Despite the discrepancy in one measurement, all remaining measurements, including the SI, showed high agreement between the manual and automatic methods. The proposed data bring us one step closer to the long-term goal of establishing robust instruments to evaluate the results of breast surgery.
Barrett's esophagus figured a swift rise in the number of cases in the past years. Although traditional diagnosis methods offered a vital role in early-stage treatment, they are generally time- and resource-consuming. In this context, computer-aided approaches for automatic diagnosis emerged in the literature since early detection is intrinsically related to remission probabilities. However, they still suffer from drawbacks because of the lack of available data for machine learning purposes, thus implying reduced recognition rates. This work introduces Generative Adversarial Networks to generate high-quality endoscopic images, thereby identifying Barrett's esophagus and adenocarcinoma more precisely. Further, Convolution Neural Networks are used for feature extraction and classification purposes. The proposed approach is validated over two datasets of endoscopic images, with the experiments conducted over the full and patch-split images. The application of Deep Convolutional Generative Adversarial Networks for the data augmentation step and LeNet-5 and AlexNet for the classification step allowed us to validate the proposed methodology over an extensive set of datasets (based on original and augmented sets), reaching results of 90% of accuracy for the patch-based approach and 85% for the image-based approach. Both results are based on augmented datasets and are statistically different from the ones obtained in the original datasets of the same kind. Moreover, the impact of data augmentation was evaluated in the context of image description and classification, and the results obtained using synthetic images outperformed the ones over the original datasets, as well as other recent approaches from the literature. Such results suggest promising insights related to the importance of proper data for the accurate classification concerning computer-assisted Barrett's esophagus and adenocarcinoma detection.
Shadow IT describes covert/hidden IT systems that are managed by business entities themselves. Additionally, there are also overt forms in practice, so-called Business-managed IT, which share most of the characteristics of Shadow IT. To better understand this phenomenon, we interviewed 29 executive IT managers about positive and negative cases of Shadow IT and Business-managed IT. By applying qualitative comparative analysis (QCA), we derived four conditions that characterize these cases: Aligned, local, simple, and volatile. The results show that there are three sufficient configurations of conditions that lead to a positive outcome; one of them even encompasses Shadow IT. The most important solution indicates that IT systems managed by business entities are viewed as being positive if they are aligned with the IT department and limited to local requirements. This allows to balance local responsiveness to changing requirements and global standardization. In contrast, IT systems that are not aligned and permanent (and either organization-wide or simple) are consistently considered as negative. Our study is the first empirical quantitative–qualitative study to shed light on the success and failure of Shadow IT and Business-managed IT.
IT-Backsourcing
(2020)
IT-Backsourcing bezeichnet die Re-Integration vormals outgesourcter IT-Services zurück zum auslagernden Unternehmen. Die vorliegende Studie stellt die Ergebnisse einer Umfrage unter IT-Managern zu Einflussfaktoren von IT-Backsourcing-Entscheidungen vor. Es lässt sich feststellen, dass IT-Backsourcing von Unternehmen in unterschiedlichen Branchen und für eine Vielzahl an unterschiedlichen IT-Services, wie z. B. Applikationsentwicklung oder Infrastruktur-Leistungen, durchgeführt wird. Hauptgründe für die Entscheidung für ein IT-Backsourcing sind mangelnde Qualität, hohe Kosten sowie eine schlechte Beziehung zum IT-Dienstleister. Die Ergebnisse der Umfrage zeigen, dass diejenigen Unternehmen, die sich für ein Backsourcing entschieden haben, nach der Entscheidung insgesamt zufriedener waren als diejenigen, die in einer Outsourcing-Beziehung geblieben sind. Zudem werden verschiedene Handlungsempfehlungen für IT-Manager aufgezeigt. Zur Ausarbeitung einer zukunftsorientieren IT-Sourcing-Strategie empfiehlt es sich, eine service-spezifische Sourcing-Strategie zu verfolgen und entsprechend spezialisierte Dienstleister einzusetzen. Im Falle einer Entscheidung für ein IT-Backsourcing sollten verschiedene Erfolgsfaktoren beachtetet werden, wie z. B. eine frühzeitige Neubesetzung von Schlüsselstellen sowie ein lückenloser Wissenstransfer.
Cybersecurity in health
(2019)
Purpose
Cybersecurity in healthcare has become an urgent matter in recent years due to various malicious attacks on hospitals and other parts of the healthcare infrastructure. The purpose of this paper is to provide an outline of how core values of the health systems, such as the principles of biomedical ethics, are in a supportive or conflicting relation to cybersecurity.
Design/methodology/approach
This paper claims that it is possible to map the desiderata relevant to cybersecurity onto the four principles of medical ethics, i.e. beneficence, non-maleficence, autonomy and justice, and explore value conflicts in that way.
Findings
With respect to the question of how these principles should be balanced, there are reasons to think that the priority of autonomy relative to beneficence and non-maleficence in contemporary medical ethics could be extended to value conflicts in health-related cybersecurity.
Research limitations/implications
However, the tension between autonomy and justice, which relates to the desideratum of usability of information and communication technology systems, cannot be ignored even if one assumes that respect for autonomy should take priority over other moral concerns.
Originality/value
In terms of value conflicts, most discussions in healthcare deal with the conflict of balancing efficiency and privacy given the sensible nature of health information. In this paper, the authors provide a broader and more detailed outline.
Purpose
Age-related macular degeneration (AMD) is a common threat to vision. While classification of disease stages is critical to understanding disease risk and progression, several systems based on color fundus photographs are known. Most of these require in-depth and time-consuming analysis of fundus images. Herein, we present an automated computer-based classification algorithm.
Design Algorithm development for AMD classification based on a large collection of color fundus images. Validation is performed on a cross-sectional, population-based study.
Participants.
We included 120 656 manually graded color fundus images from 3654 Age-Related Eye Disease Study (AREDS) participants. AREDS participants were >55 years of age, and non-AMD sight-threatening diseases were excluded at recruitment. In addition, performance of our algorithm was evaluated in 5555 fundus images from the population-based Kooperative Gesundheitsforschung in der Region Augsburg (KORA; Cooperative Health Research in the Region of Augsburg) study.
Methods.
We defined 13 classes (9 AREDS steps, 3 late AMD stages, and 1 for ungradable images) and trained several convolution deep learning architectures. An ensemble of network architectures improved prediction accuracy. An independent dataset was used to evaluate the performance of our algorithm in a population-based study.
Main Outcome Measures.
κ Statistics and accuracy to evaluate the concordance between predicted and expert human grader classification.
Results.
A network ensemble of 6 different neural net architectures predicted the 13 classes in the AREDS test set with a quadratic weighted κ of 92% (95% confidence interval, 89%–92%) and an overall accuracy of 63.3%. In the independent KORA dataset, images wrongly classified as AMD were mainly the result of a macular reflex observed in young individuals. By restricting the KORA analysis to individuals >55 years of age and prior exclusion of other retinopathies, the weighted and unweighted κ increased to 50% and 63%, respectively. Importantly, the algorithm detected 84.2% of all fundus images with definite signs of early or late AMD. Overall, 94.3% of healthy fundus images were classified correctly.
Conclusions
Our deep learning algoritm revealed a weighted κ outperforming human graders in the AREDS study and is suitable to classify AMD fundus images in other datasets using individuals >55 years of age.
One common method to fix fractures of the human hand after an accident is an osteosynthesis with Kirschner wires (K-wires) to stabilize the bone fragments. The insertion of K-wires is a delicate minimally invasive surgery, because surgeons operate almost without a sight. Since realistic training methods are time consuming, costly and insufficient, a virtual-reality (VR) based training system for the placement of K-wires was developed. As part of this, the current work deals with the real-time bone drilling simulation using a haptic force-feedback device.
To simulate the drilling, we introduce a virtual fixture based force-feedback drilling approach. By decomposition of the drilling task into individual phases, each phase can be handled individually to perfectly control the drilling procedure. We report about the related finite state machine (FSM), describe the haptic feedback of each state and explain, how to avoid jerking of the haptic force-feedback during state transition.
The usage of the virtual fixture approach results in a good haptic performance and a stable drilling behavior. This was confirmed by 26 expert surgeons, who evaluated the virtual drilling on the simulator and rated it as very realistic. To make the system even more convincing, we determined real drilling feed rates through experimental pig bone drilling and transferred them to our system. Due to a constant simulation thread we can guarantee a precise drilling motion.
Virtual fixtures based force-feedback calculation is able to simulate force-feedback assisted bone drilling with high quality and, thus, will have a great potential in developing medical applications.
Computer-aided diagnosis using deep learning in the evaluation of early oesophageal adenocarcinoma
(2019)
Computer-aided diagnosis using deep learning (CAD-DL) may be an instrument to improve endoscopic assessment of Barrett’s oesophagus
(BE) and early oesophageal adenocarcinoma (EAC). Based on still images from two databases, the diagnosis of EAC by CAD-DL reached sensitivities/specificities of 97%/88% (Augsburg data) and 92%/100% (Medical Image Computing and Computer-Assisted Intervention [MICCAI]
data) for white light (WL) images and 94%/80% for narrow band images (NBI) (Augsburg data), respectively. Tumour margins delineated by
experts into images were detected satisfactorily with a Dice coefficient (D) of 0.72. This could be a first step towards CAD-DL for BE assessment. If developed further, it could become a useful
adjunctive tool for patient management.