Towards an Intelligent Framework for Personalized Simulation-enhanced Surgery Assistance: Linking a Simulation Ontology to a Reinforcement Learning Algorithm for Calibration of Numerical Simulations

  • Nicolai Schoch (Author)
    Engineering Mathematics and Computing Lab (EMCL), Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University
    http://orcid.org/0000-0002-7806-6435
  • Vincent Heuveline (Author)
    Engineering Mathematics and Computing Lab (EMCL), Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University

Abstract

Evolving our previous research results in the context of cognition-guidance and patient-specifity for simulation-enhanced cardiac surgery assistance, in this work we further investigate on (1) a machine learning framework which allows to patient-individually calibrate soft tissue material parameters for subsequent simulation, and (2) a profound knowledge management framework which may enhance the ontology-driven overall setup of the cognition-guided surgery simulation in a clinic environment. Rather than being a closed research work with an in-depth theory backup and a complete evaluation, we here present a technical report and some interesting experimental works that are to serve for further research and development.

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Published
2017-10-25
Language
en
Academic discipline and sub-disciplines
Simulation Calibration, Parameter Identification, Personalized Numerical Simulation, Machine Learning, Reinforcement Learning, Knowledge Modeling, Simulation Ontology
Keywords
Simulation Calibration, Parameter Identification, Personalized Numerical Simulation, Machine Learning, Reinforcement Learning, Knowledge Modeling, Simulation Ontology