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Cognitive tools pipeline for assistance of mitral valve surgery


Nicolai Schoch, Patrick PhilippTobias Weller, Sandy Engelhardt, Mykola Volovyk, Andreas Fetzer, Marco Nolden, Raffaele De Simone, Ivo Wolf, Maria MaleshkovaAchim RettingerRudi Studer, Vincent Heuveline



Published: 2016 März
Herausgeber: Robert J. Webster and Ziv R. Yaniv
Buchtitel: Proc. SPIE 9786, Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling
Ausgabe: 9786
Verlag: SPIE Digital Library
Erscheinungsort: San Diego, California, United States
Referierte Veröffentlichung
BibTeX

Kurzfassung
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.

Weitere Informationen unter: Link
DOI Link: 10.1117/12.2216059

Projekt

SFB/Transregio 125



Forschungsgruppe

Web Science und Wissensmanagement


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