Landmark-based registration of a cochlear model to a human cochlea using conventional CT scans.

Cochlear implants can provide an advanced treatment option to restore hearing. In standard pre-implant procedures, many factors are already considered, but it seems that not all underlying factors have been identified yet. One reason is the low quality of the conventional computed tomography images taken before implantation, making it difficult to assess these parameters. A novel method is presented that uses the Pietsch Model, a well-established model of the human cochlea, as well as landmark-based registration to address these challenges. Different landmark numbers and placements are investigated by visually comparing the mean error per landmark and the registrations' results. The landmarks on the first cochlear turn and the apex are difficult to discern on a low-resolution CT scan. It was possible to achieve a mean error markedly smaller than the image resolution while achieving a good visual fit on a cochlear segment and directly in the conventional computed tomography image. The employed cochlear model adjusts image resolution problems, while the effort of setting landmarks is markedly less than the segmentation of the whole cochlea. As a next step, the specific parameters of the patient could be extracted from the adapted model, which enables a more personalized implantation with a presumably better outcome.

[What support does a mobile, assistive app provide for informal caregivers? A qualitative analysis of the MoCaB app user experience]. / Unterstützung pflegender Angehöriger durch eine mobile, assistive App. Eine qualitative Analyse der Perspektive von Nutzerinnen und Nutzern der App MoCaB.

BACKGROUND: In the joint project "Mobile Care Backup" funded by the German Federal Ministry of Education and Research, the smartphone-based app "MoCaB" was developed in close cooperation with informal caregivers. It provides individualized, algorithm-based information and can accompany and support caring relatives in everyday life. After a multi-step development, informal caregivers tested the MoCaB app in a home setting at the end of the research project. The goal was to find out how the test persons evaluate MoCaB and in which form the app can provide support to informal caregivers. METHODS: Eighteen test persons caring for relatives participated in a four-week test of MoCaB. Guideline-based qualitative interviews to record usage behavior and experiences with the app were conducted after two and four weeks of testing, transcribed and analyzed using qualitative content analysis. RESULTS: The test persons described the care-related information as helpful. The individualized, algorithm-based mode of information delivery and the exercises provided for family caregivers were generally rated as helpful, but their use depends on the individual usage style. Three dimensions can describe the effects of MoCaB: 1) expansion of care-relevant knowledge, 2) stimulation of self-reflection, and 3) behavior towards the care recipients. DISCUSSION: With few exceptions, the testing caregivers felt that the MoCaB app was enriching. The support dimensions have an effect at different points in everyday life and vary in intensity, depending on the duration of the existing care activity and the individual preferences of the users. CONCLUSION: The way in which caregivers used the app was not always consistent with the expected behaviors. This demonstrates the relevance of open-ended, qualitative research methods in the evaluation of health apps.

Image Processing of Conventional Computer Tomography Images for Segmentation of the Human Cochlea.

Against the background of increasing numbers of indications for Cochlea implants (CIs), there is an increasing need for a CI outcome prediction tool to assist the process of deciding on the best possible treatment solution for each individual patient prior to intervention. The hearing outcome depends on several features in cochlear structure, the influence of which is not entirely known as yet. In preparation for surgical planning a preoperative CT scan is recorded. The overall goal is the feature extraction and prediction of the hearing outcome only based on this conventional CT data. Therefore, the aim of our research work for this paper is the preprocessing of the conventional CT data and a following segmentation of the human cochlea. The great challenge is the very small size of the cochlea in combination with a fairly bad resolution. For a better distinction between cochlea and surrounding tissue, the data has to be rotated in a way the typical cochlea shape is observable. Afterwards, a segmentation can be performed which enables a feature detection. We can show the effectiveness of our method compared to results in literature which were based on CT data with a much higher resolution. A further study with a much larger amount of data is planned.

Risk factors for Covid-19 severity and fatality: a structured literature review.

PURPOSE: Covid-19 is a global threat that pushes health care to its limits. Since there is neither a vaccine nor a drug for Covid-19, people with an increased risk for severe and fatal courses of disease particularly need protection. Furthermore, factors increasing these risks are of interest in the search of potential treatments. A systematic literature review on the risk factors of severe and fatal Covid-19 courses is presented. METHODS: The review is carried out on PubMed and a publicly available preprint dataset. For analysis, risk factors are categorized and information regarding the study such as study size and location are extracted. The results are compared to risk factors listed by four public authorities from different countries. RESULTS: The 28 records included, eleven of which are preprints, indicate that conditions and comorbidities connected to a poor state of health such as high age, obesity, diabetes and hypertension are risk factors for severe and fatal disease courses. Furthermore, severe and fatal courses are associated with organ damages mainly affecting the heart, liver and kidneys. Coagulation dysfunctions could play a critical role in the organ damaging. Time to hospital admission, tuberculosis, inflammation disorders and coagulation dysfunctions are identified as risk factors found in the review but not mentioned by the public authorities. CONCLUSION: Factors associated with increased risk of severe or fatal disease courses were identified, which include conditions connected with a poor state of health as well as organ damages and coagulation dysfunctions. The results may facilitate upcoming Covid-19 research.

Extending a Knowledge-Based System with Learning Capacity.

Informal caregivers often complain about missing knowledge. A knowledge-based personalized educational system is developed, which provides caregiving relatives with the information needed. Yet, evaluation against domain experts indicated, that parts of the knowledge-base are incorrect. To overcome these problems the system can be extended by a learning capacity and then be trained further utilizing feedback from real informal caregivers. To extend the existing system an artificial neural network was trained to represent a large part of the knowledge-based approach. This paper describes the found artificial neural network's structure and the training process. The found neural network structure is not deep but very wide. The training terminated after 374.700 epochs with a mean squared error of 7.731 ∗ 10-8 for the end validation set. The neural network represents the parts of the knowledge-based approach and can now be retrained with user feedback, which will be collected during a system test in April and May 2019.

Evaluating the Validity of a Knowledge-Based System for Proactive Knowledge Transfer for Caregiving Relatives.

The evaluation of the validity of a knowledge-based system is of great importance during its development. It determines whether the system represents the experts' knowledge correctly. This is highly important, but also particularly difficult, if the expert knowledge is not explicit, but only implicit and tacit. In the following the validity's evaluation of a system for education of caregiving relatives is presented. To evaluate the system's knowledge delivery strategy against the experts' opinion, several fictious characters were created. The evaluation revealed inconsistencies in the knowledge base. After resolving these, the experts' opinion is represented to a large extent by the system. Nevertheless, the used evaluation approach is not capable of detecting all inconsistencies. Therefore, a strong need of a system's learning capacity to integrate feedback from a larger group of real caregiving relatives exists. In addition, a rule-based component, representing disease specific knowledge, should be implemented.

[Providing knowledge and support for caring relatives with the smartphone - the MoCaB project]. / Über das Smartphone Wissen und Unterstützung für pflegende Angehörige bereitstellen.

Providing knowledge and support for caring relatives with the smartphone - the MoCaB project Abstract. Background: Caring relatives often have a lack of knowledge of illness and care options. Surveys, as part of the MoCaB (Mobile Care Backup) project, showed that this is often experienced as a burden. Aim: The aim is to support caring relatives by increasing their knowledge. Therefore care-relevant knowledge and instructions for self-care should be provided according to their needs. Methods: The core element of the mobile application, personalized knowledge transfer in dialogue form, will be developed in a participative process with potential users. The relevant evidence-based nursing knowledge was reviewed by experts and written down for the target group. Results: So far, 86 relevant topics have been identified and formulated. First usability tests showed that the content itself, the used expressions and the presentation via the MoCaB app are well received. Outlook: In a next step, the app will be tested in the home setting with caring relatives and, in order to identify any further need and areas for improvement.

Personalized Knowledge Transfer for Caregiving Relatives.

Caregiving relatives have to manage very diverse tasks and need a lot of care-relevant knowledge. For most of them it is not easy to find the knowledge required. Thus, a personalized knowledge transfer for caregiving relatives is necessary. Against this background, methods to determine the personal relevance and importance of knowledge resources for caregiving relatives are developed. To evaluate these methods, an exemplary fictitious person is created by experts in Nursing Science. In this evaluation, the approach's results are compared with an expert opinion. The approach indicates that a personalized knowledge transfer is possible, providing caregiving relatives with necessary care knowledge according to their personal life situation.