Cognitive tools pipeline for assistance of mitral valve surgery
Publication date
2016
Document type
Konferenzbeitrag
Author
Schoch, Nicolai
Philipp, Patrick
Weller, Tobias
Engelhardt, Sandy
Volovyk, Mykola
Fetzer, Andreas
Nolden, Marco
De Simone, Raffaele
Wolf, Ivo
Rettinger, Achim
Studer, Rudi
Heuveline, Vincent
Organisational unit
Karlsruhe Institute of Technology
ISBN
ISSN
Conference
Image-Guided Procedures, Robotic Interventions, and Modeling, San Diego, California, United States. 28 February - 1 March 2016
Series or journal
Proceedings of SPIE
Periodical volume
9786
Book title
Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling
Part of the university bibliography
Nein
Keyword
Cognition-guided surgery
Cognitive system
Deductive system
Intelligent data processing architecture
Mitral valve reconstruction
Patient-specific biomechanical simulation setup
Surgery support
Surgical reasoning
Abstract
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. © 2016 SPIE.
Version
Published version
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