Minimally invasive postmortem telebiopsy

For the past 10 years, medical imaging techniques have been increasingly applied to forensic investigations. To obtain histological and toxicological information, tissue and liquid samples are required. In this article, we describe the development of a low-cost, secure, and reliable approach for a telematic add-on for remotely planning biopsies on the Virtobot robotic system. Data sets are encrypted and submitted over the Internet. A plugin for the OsiriX medical image viewer allows for remote planning of needle trajectories that are used for needle placement. The application of teleradiological methods to image-guided biopsy in the forensic setting has the potential to reduce costs and, in conjunction with a mobile computer tomographic scanner, allows for tissue sampling in a mass casualty situation involving nuclear, biological, or chemical agents, in a manner that minimizes the risk to involved staff.

Senseo: Enriching Eclipse's Static Source Views with Dynamic Metrics

Maintaining object-oriented systems that use inheritance and polymorphism is difficult, since runtime information, such as which methods are actually invoked at a call site, is not visible in the static source code. We have implemented Senseo, an Eclipse plugin enhancing Eclipse's static source views with various dynamic metrics, such as runtime types, the number of objects created, or the amount of memory allocated in particular methods.

Individual Video Training - Entwicklung einer nationalen Infrastruktur für Video-unterstütztes Lernen

Video-basiertes Lernen ist besonders effektiv, wo es um Fertigkeiten und Verhalten geht. Videoaufzeichnungen von Gesprächen, Unterrichtssituationen oder der Durchführung praktischer Tätigkeiten wie dem Nähen einer Wunde erlauben es den Ausführenden, ihren Peers und ihren Tutoren, die Qualität der Leistung zu beurteilen und Anregungen zur Verbesserung zu formulieren. Wissend um den grossen didaktischen Wert von Videoaufzeichnungen haben sich vier Pädagogische Hochschulen (Zürich, Freiburg, Thurgau, Luzern) und zwei Medizinische Fakultäten (Bern, Lausanne) zusammen getan, um eine nationale Infrastruktur für Video-unterstütztes Lernen anzustossen. Ziel was es, ein System zu entwickeln, das einfach zu bedienen ist, bei dem viele Arbeitsschritte automatisiert sind und das die Videos im Internet bereit stellt. Zusammen mit SWITCH, der nationalen IT-Support-Organisation der Schweizer Hochschulen, wurde basierend auf den vorbestehenden Technologien AAI und SWITCHcast das Programm iVT (Individual Video Training) entwickelt. Die Integration des nationalen Single Logon System AAI (Authentification and Authorization Infrastructure) erlaubt es, die Videos mit dem jeweiligen User eindeutig zu verknüpfen, so dass die Videos nur für diesen User im Internet zugänglich sind. Mit dem Podcast-System SWITCHcast können Videos automatisch ins Internet hochgeladen und bereit gestellt werden. Es wurden je ein Plugin für die Learning Management Systeme ILIAS (PH Zürich, Uni Bern) und Moodle (Uni Lausanne) entwickelt. Dank dieser Plugins werden die Videos in den jeweiligen LMS verfügbar gemacht. Der Einsatz von iVT ist beim Kommunikationstraining unserer Medizinstudierenden in Bern inzwischen Standard. Das Login gilt gleichzeitig als Beleg für das Testat. Studierende, die keine Videoaufzeichnung wünschen, können diese nach dem Login stoppen. Bis anhin ist das Betrachten der Videos freiwillig. Szenarios mit Peer Feedback sind geplant. Eine entsprechende Erweiterung des Systems um gegenseitige Annotationsmöglichkeiten besteht bereits und wird fortlaufend weiterentwickelt.

Automatic quality control in clinical (1) H MRSI of brain cancer.

MRSI grids frequently show spectra with poor quality, mainly because of the high sensitivity of MRS to field inhomogeneities. These poor quality spectra are prone to quantification and/or interpretation errors that can have a significant impact on the clinical use of spectroscopic data. Therefore, quality control of the spectra should always precede their clinical use. When performed manually, quality assessment of MRSI spectra is not only a tedious and time-consuming task, but is also affected by human subjectivity. Consequently, automatic, fast and reliable methods for spectral quality assessment are of utmost interest. In this article, we present a new random forest-based method for automatic quality assessment of (1) H MRSI brain spectra, which uses a new set of MRS signal features. The random forest classifier was trained on spectra from 40 MRSI grids that were classified as acceptable or non-acceptable by two expert spectroscopists. To account for the effects of intra-rater reliability, each spectrum was rated for quality three times by each rater. The automatic method classified these spectra with an area under the curve (AUC) of 0.976. Furthermore, in the subset of spectra containing only the cases that were classified every time in the same way by the spectroscopists, an AUC of 0.998 was obtained. Feature importance for the classification was also evaluated. Frequency domain skewness and kurtosis, as well as time domain signal-to-noise ratios (SNRs) in the ranges 50-75 ms and 75-100 ms, were the most important features. Given that the method is able to assess a whole MRSI grid faster than a spectroscopist (approximately 3 s versus approximately 3 min), and without loss of accuracy (agreement between classifier trained with just one session and any of the other labelling sessions, 89.88%; agreement between any two labelling sessions, 89.03%), the authors suggest its implementation in the clinical routine. The method presented in this article was implemented in jMRUI's SpectrIm plugin. Copyright © 2016 John Wiley & Sons, Ltd.