ActiveAdvice : "intelligente" Beratung im Alter

Der Anteil der älteren Bevölkerung steigt. Für die Schweiz wird bis 2060 ein Bevölkerungszuwachs von 89 Prozent der über 65-Jährigen und eine Zunahme von 180 Prozent der Altersgruppe der über 80-Jährigen prognostiziert (BFS 2010). Pflege und Versorgung müssen sich neu ausrichten und Alternativen, wie z. B den Einsatz assistierender Technologien, zulassen. Das Institut für Facility Management engagiert sich bereits seit 2009 in den EU-geförderten AAL-Projekten (Active and Assisted Living), die die Entwicklung und den Einsatz solcher Technologien vorantreiben

M3W maintaining and measuring mental wellness

In der Schweiz leiden knapp 120 000 Menschen an einer dementiellen Erkrankung. Fachleute für Ambient Assisted Living (AAL) und Smart Living vom Institut für Facililty Management und aus dem Institut für Datenanalyse und Prozessdesign der ZHAW School of Engineering engagieren sich gemeinsam in einem zukunftsweisenden EU-Projekt, das Onlinespiele für den individuellen Gebrauch entwickelt. Diese sollen künftig als Instrument der eigenverantwortlichen Demenzvorsorge und zur Unterstützung der Früherkennung eingesetzt werden

Wohnen im Alter : Technikeinsatz für mehr Sicherheit und Komfort

Der Einsatz von Technik soll Menschen im Alter ein selbständiges und sicheres Leben ermöglichen. Unser Zuhause wird dabei immer intelligenter, vernetzter, intuitiver. Durch assistierende Technologien garantierte Sicherheit und Komfort sind jedoch nicht nur Themen des privaten Wohnens. Auch im institutionellen Kontext, in Alters- und Pflegeheimen und in Spitälern haben diese ihren Platz gefunden

Versorgung Digital : AALBridge

Pflege- und Alterseinrichtungen bekunden zunehmend Interesse an Active Assisted Living (AAL)-Lösungen. Aktuell fehlen aber Wegleitungen für deren Einsatz, insbesondere bei Sondersettings. AALBridge untersucht einerseits das Sondersetting «Brückenfunktionsangebot » als ein neues mögliches Angebot von Pflege- und Alterseinrichtungen für ältere Menschen nach einem Spitalaufenthalt. Andererseits sollen konkrete user-centred AAL-Lösungen und digitale Daten- und Informationslösungen für dieses Setting identifiziert werden. Ziel ist es, zu verstehen, wie eine bessere Integration smarter ICT-Lösungen aus dem AAL-Bereich und digitaler Informationen (des medizinisch-pflegerischen und des nicht-medizinischen Bereichs) erreicht werden kann. Egal, ob wir in Zukunft Sondersettings im Sinne eines Hospital at home oder eines Brückenfunktionsangebotes realisieren, jedes Angebot für ältere Menschen braucht konsequent die Abstimmung und Vernetzung aller Beteiligten, also der älteren Menschen, der Angehörigen und des Fachpersonals über die institutionellen Grenzen hinweg

The impact of Rotavirus mass vaccination on hospitalization rates, nosocomial Rotavirus gastroenteritis and secondary blood stream infections

Background The aim of the study was to evaluate the effects of universal mass vaccination (UMV) against rotavirus (RV) on the hospitalization rates, nosocomial RV infections and RV-gastroenteritis (GE)-associated secondary blood stream infections (BSI). Methods The retrospective evaluation (2002–2009) by chart analysis included all clinically diagnosed and microbiologically confirmed RV-GE cases in a large tertiary care hospital in Austria. The pre-vaccination period (2002–2005) was compared with the recommended and early funded (2006–2007) and the funded (2008–2009) vaccination periods. Primary outcomes were RV-GE-associated hospitalizations, secondary outcomes nosocomial RV disease, secondary BSI and direct hospitalization costs for children and their accompanying persons. Results In 1,532 children with RV-GE, a significant reduction by 73.9% of hospitalized RV-GE cases per year could be observed between the pre-vaccination and the funded vaccination period, which was most pronounced in the age groups 0–11 months (by 87.8%), 6–10 years (by 84.2%) and 11–18 years (88.9%). In the funded vaccination period, a reduction by 71.9% of nosocomial RV-GE cases per year was found compared to the pre-vaccination period. Fatalities due to nosocomial RV-GE were only observed in the pre-vaccination period (3 cases). Direct costs of hospitalized, community-acquired RV-GE cases per year were reduced by 72.7% in the funded vaccination period. The reduction of direct costs for patients (by 86.9%) and accompanying persons (86.2%) was most pronounced in the age group 0–11 months. Conclusions UMV may have contributed to the significant decrease of RV-GE-associated hospitalizations, to a reduction in nosocomial RV infections and RV-associated morbidity due to secondary BSI and reduced direct hospitalization costs. The reduction in nosocomial cases is an important aspect considering severe disease courses in hospitalized patients with co-morbidities and death due to nosocomial RV-GE

A Dempster-Shafer Approach to Trustworthy AI With Application to Fetal Brain MRI Segmentation

Deep learning models for medical image segmentation can fail unexpectedly and spectacularly for pathological cases and images acquired at different centers than training images, with labeling errors that violate expert knowledge. Such errors undermine the trustworthiness of deep learning models for medical image segmentation. Mechanisms for detecting and correcting such failures are essential for safely translating this technology into clinics and are likely to be a requirement of future regulations on artificial intelligence (AI). In this work, we propose a trustworthy AI theoretical framework and a practical system that can augment any backbone AI system using a fallback method and a fail-safe mechanism based on Dempster-Shafer theory. Our approach relies on an actionable definition of trustworthy AI. Our method automatically discards the voxel-level labeling predicted by the backbone AI that violate expert knowledge and relies on a fallback for those voxels. We demonstrate the effectiveness of the proposed trustworthy AI approach on the largest reported annotated dataset of fetal MRI consisting of 540 manually annotated fetal brain 3D T2w MRIs from 13 centers. Our trustworthy AI method improves the robustness of four backbone AI models for fetal brain MRIs acquired across various centers and for fetuses with various brain abnormalities.</p

A Dempster-Shafer approach to trustworthy AI with application to fetal brain MRI segmentation

Deep learning models for medical image segmentation can fail unexpectedly and spectacularly for pathological cases and images acquired at different centers than training images, with labeling errors that violate expert knowledge. Such errors undermine the trustworthiness of deep learning models for medical image segmentation. Mechanisms for detecting and correcting such failures are essential for safely translating this technology into clinics and are likely to be a requirement of future regulations on artificial intelligence (AI). In this work, we propose a trustworthy AI theoretical framework and a practical system that can augment any backbone AI system using a fallback method and a fail-safe mechanism based on Dempster-Shafer theory. Our approach relies on an actionable definition of trustworthy AI. Our method automatically discards the voxel-level labeling predicted by the backbone AI that violate expert knowledge and relies on a fallback for those voxels. We demonstrate the effectiveness of the proposed trustworthy AI approach on the largest reported annotated dataset of fetal MRI consisting of 540 manually annotated fetal brain 3D T2w MRIs from 13 centers. Our trustworthy AI method improves the robustness of a state-of-the-art backbone AI for fetal brain MRIs acquired across various centers and for fetuses with various brain abnormalities

Minimally Invasive Coronary Revascularisation Surgery: A Focused Review of the Available Literature

Minimally invasive coronary revascularisation was originally developed in the mid 1990s as minimally invasive direct coronary artery bypass (MIDCAB) grafting is a less invasive approach compared to conventional coronary artery bypass grafting (CABG) to address targets in the left anterior descending coronary artery (LAD). Since then, MIDCAB has evolved with the adoption of a robotic platform and the possibility to perform multivessel bypass procedures. Minimally invasive coronary revascularisation surgery also allows for a combination between the benefits of CABG and percutaneous coronary interventions for non-LAD lesions – a hybrid approach. Hybrid coronary revascularisation results in fewer blood transfusions, shorter hospital stay, decreased ventilation times and patients return to work sooner when compared to conventional CABG. This article reviews the available literature, describes standard approaches and considers topics, such as limited access procedures, indications and patient selection, diagnostics and imaging, techniques, anastomotic devices, hybrid coronary revascularisation and outcome analysis