Ontology-based specification and generation of search queries for post-market surveillance.

BACKGROUND: The vigilant observation of medical devices during post-market surveillance (PMS) for identifying safety-relevant incidents is a non-trivial task. A wide range of sources has to be monitored in order to integrate all accessible data about the safety and performance of a medical device. PMS needs to be supported by an efficient search strategy and the possibility to create complex search queries by domain experts. RESULTS: We use ontologies to support the specification of search queries and the preparation of the document corpus, which contains all relevant documents. In this paper, we present (1) the Search Ontology (SON) v2.0, (2) an Excel template for specifying search queries, and (3) the Search Ontology Generator (SONG), which generates complex queries out of the Excel template. Based on our approach, a service-oriented architecture was designed, which supports and assists domain experts during PMS. Comprehensive testing confirmed the correct execution of all SONG functions. The applicability of our method and of the developed tools was evaluated by domain experts. The test persons concordantly rated our solution after a short period of training as highly user-friendly, intuitive and well applicable for supporting PMS. CONCLUSIONS: The Search Ontology is a promising domain-independent approach to specify complex search queries. Our solution allows advanced searches for relevant documents in different domains using suitable domain ontologies.

Integration of model-based weighting into an ICP variant to account for measurement errors in intra-operative A-Mode ultrasound-based registration.

This paper addresses error modeling in A-Mode ultrasound- (US-) based registration and integration of model-based weighting into the Random-ICP (R-ICP) algorithm. The R-ICP is a variant of the Iterative Closest Point (ICP) algorithm, and it was suggested for surface-based registration using A-Mode US in the context of skull surgery. In that application area the R-ICP could yield high accuracy even in case of a small number of data points and a very inaccurate user-interactive pre-registration. However, it cannot cope with unequal point uncertainty, which is an important drawback in the context of hip surgery: Uncertainty about the average speed of sound is an error source, whose impact on the registration accuracy increases with the thickness of the scanned soft tissue. It can, therefore, lead to considerable localization errors if a thick soft tissue layer is scanned, and it might vary a lot from data point to data point as the soft tissue thickness is inhomogeneous. The present work investigates how to account for this error source considering also other error sources such as the establishment of point correspondences. Simulation results show that registration accuracy can be substantially improved when model-based weighting is integrated into the R-ICP.