Andreas Fetzer, Jasmin Metzger, Darko Katic, Keno März, Martin Wagner, Patrick Philipp, Sandy Engelhardt, Tobias Weller, Sascha Zelzer, Alfred Franz, Nicolai Schoch, Vincent Heuveline, Maria Maleshkova, Achim Rettinger, Stefanie Speidel, Ivo Wolf, Hannes Kenngott, Arianeb Mehrabi, Beat Müller-Stich, Lena Maier-Hein, Hans-Peter Meinzer, Marco Nolden
KEYWORDS: Surgery, Data integration, Data modeling, Imaging informatics, Data storage, Standards development, Data modeling, Knowledge acquisition, Cognition, Knowledge management, Medical imaging, Image segmentation, Picture Archiving and Communication System, Information science, Neuroimaging, Data archive systems
In the surgical domain, individual clinical experience, which is derived in large part from past clinical cases, plays
an important role in the treatment decision process. Simultaneously the surgeon has to keep track of a large
amount of clinical data, emerging from a number of heterogeneous systems during all phases of surgical treatment.
This is complemented with the constantly growing knowledge derived from clinical studies and literature. To
recall this vast amount of information at the right moment poses a growing challenge that should be supported
by adequate technology.
While many tools and projects aim at sharing or integrating data from various sources or even provide knowledge-based
decision support - to our knowledge - no concept has been proposed that addresses the entire surgical
pathway by accessing the entire information in order to provide context-aware cognitive assistance. Therefore a
semantic representation and central storage of data and knowledge is a fundamental requirement.
We present a semantic data infrastructure for integrating heterogeneous surgical data sources based on a common
knowledge representation. A combination of the Extensible Neuroimaging Archive Toolkit (XNAT) with semantic
web technologies, standardized interfaces and a common application platform enables applications to access and
semantically annotate data, perform semantic reasoning and eventually create individual context-aware surgical
assistance.
The infrastructure meets the requirements of a cognitive surgical assistant system and has been successfully
applied in various use cases. The system is based completely on free technologies and is available to the community
as an open-source package.
Nicolai Schoch, Patrick Philipp, Tobias Weller, Sandy Engelhardt, Mykola Volovyk, Andreas Fetzer, Marco Nolden, Raffaele De Simone, Ivo Wolf, Maria Maleshkova, Achim Rettinger, Rudi Studer, Vincent Heuveline
KEYWORDS: Surgery, Data processing, Intelligence systems, Data integration, Cognition, Numerical simulations, Computer architecture, Computer simulations, Medical imaging, Data modeling, Image processing, Information science
For cardiac surgeons, mitral valve reconstruction (MVR) surgery is a highly demanding procedure, where an artificial annuloplasty ring is implanted onto the mitral valve annulus to re-enable the valve's proper closing functionality. For a successful operation the surgeon has to keep track of a variety of relevant impact factors, such as patient-individual medical history records, valve geometries, or tissue properties of the surgical target, and thereon-based deduce type and size of the best-suitable ring prosthesis according to practical surgery experience. With this work, we aim at supporting the surgeon in selecting this ring prosthesis by means of a comprehensive information processing pipeline. It gathers all available patient-individual information, and mines this data according to 'surgical rules', that represent published MVR expert knowledge and recommended best practices, in order to suggest a set of potentially suitable annuloplasty rings. Subsequently, these rings are employed in biomechanical MVR simulation scenarios, which simulate the behavior of the patient-specific mitral valve subjected to the respective virtual ring implantation. We present the implementation of our deductive system for MVR ring selection and how it is integrated into a cognitive data processing pipeline architecture, which is built under consideration of Linked Data principles in order to facilitate holistic information processing of heterogeneous medical data. By the example of MVR surgery, we demonstrate the ease of use and the applicability of our development. We expect to essentially support patient-specific decision making in MVR surgery by means of this holistic information processing approach.
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