Ontological Knowledge Bases for Computer Assisted Surgical Applications

1. Ontological Knowledge Bases for Computer Assisted Surgical Applications Innovation Center Computer Assisted Surgery (ICCAS) Universität Leipzig Raj Mudunuri Raj.Mudunuri@iccas.de Ontologies in Biomedicine and Life Sciences (OBML) 26-27 November 2009 Leipzig

2. Computer Assisted Surgery (CAS) Monitoring devices Radiological images Robotic assistance systems Workflow protocols Surgical tools Tracking devices Pathalogy Anatomy

3. Types of knowledge representation modern classical

4. Types of knowledge representation future

5. Computer Assisted Surgery (CAS) Monitoring devices Radiological images Robotic assistance systems Workflow protocols Surgical tools Tracking devices Pathalogy Anatomy How to maintain the coherence of this diverse information?

6. Problems and general insights • How was the surgical knowledge represented and used before the advent of CAS, and how is it being processed today? • How can the large amount of heterogeneous data from CAS be represented and processed in the future Operating Room? • Minimise the gap for data-sharing among different information sources within CAS systems • Establish a common minimum knowledge base for these various information sources • Enhance the inter-operability of the data by incorporating semantics into it

7. Our approach • Recognize the various aspects of CAS where ontologies are needed for conceptual description (Anatomy, surgical tools, surgical actions, tracking devices etc.) • Use appropriate programming tools that are expressive enough to model these domains (OWL,SWRL) • Use appropriate querying methods that suit the needs of the applications (SPARQL – for simple rdfs queries,SQWRL – for owl based queries)

8. Methodology of our approach General Formal Ontology GFO (IMISE) Top-Level ontology SOCAS (Surgical Ontologies for CAS) Domain level core ontology COCAS (Core Ontology for CAS) FESS-ont NC-Mastoid-ont Discipline specific ontologies Repository ontologies Anatomy Instruments Activities RFID

9. Methodology of our approach cocas:AnatomicalStructure fess:Polyp gfo:Material-Object cocas:SurgicalDevice fess:Scalpel gfo:Presential cocas:OperatingRoom gfo:Concrete cocas:Personnel cocas:Surgeon gfo:Process gfo:Individual cocas:Patient cocas:OperativePhase cocas:PreOperativePhase cocas:Functional-MI cocas:-Tomographic-MI cocas:IntraOperativePhase cocas:MedicalImage cocas:Microscopy-MI gfo:Entity cocas:Optical-MI cocas:PostOperativePhase cocas:Pathology gfo:Category cocas:FESS cocas:Surgery cocas:HNO cocas:MLS

10. Methodology of our approach CAS Application SQWRL (for complex conceptual info) SPARQL (for simple rdfs) Ontological KB (OWL Concepts) (RDFS/OWL semantics) (SWRL rules) Eg. Get the list of entities along with tag ids for objects of the type Knife Eg. Get the list of concepts based on the entities that are used in application 1 and application 2

11. Testing on simulated environment

12. Testing on simulated environment

13. Testing on simulated environment

14. Conclusions • Designed ontologies (OWL-DL) which can act as conceptual models for few CAS disciplines • Tested their feasibility to be used as background knowledge bases • Further tests need to be done with different models to evaluate the performance of the knowledgebase

15. Thank you Acknowledgement The Innovation Center Computer Assisted Surgery (ICCAS) at the Faculty of Medicine at the Universität Leipzig is funded by the German Federal Ministry of Education and Research (BMBF) and the Saxon Ministry of Science and the Fine Arts (SMWK) in the scope of the initiative “Unternehmen Region” with the grant numbers 03 ZIK 031, 03 ZIK 032 and 03 ZIK 435.