Estimating similarity of surgical situations with Case-Retrieval-Nets
Research output: Contribution to book/conference proceedings/anthology/report › Conference contribution › Contributed › peer-review
Contributors
Abstract
Representation and recognition of surgical situations is a prerequisite for the development of context-aware surgical assistance systems. In this publication a method for recognition of surgical situations with Case-Retrieval-Nets is presented. It enables the estimation of similarity between models of surgical situations. The main advantage of this approach is the combined use of domain knowledge and reasoning algorithms to estimate similarity. Domain knowledge about human anatomy is based on a reference ontology. Evaluation is performed on situations of two cholecystectomies.
Details
Original language | English |
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Title of host publication | Medicine Meets Virtual Reality 17 - NextMed |
Publisher | IOS Press |
Pages | 358-363 |
Number of pages | 6 |
ISBN (print) | 9781586039646 |
Publication status | Published - 2009 |
Peer-reviewed | Yes |
Publication series
Series | Studies in health technology and informatics |
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Volume | 142 |
ISSN | 0926-9630 |
Conference
Title | 17th Annual MMVR Conference - NextMed: Design for/the Well Being, MMVR17 2009 |
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Duration | 19 - 22 January 2009 |
City | Long Beach, CA |
Country | United States of America |
External IDs
PubMed | 19377184 |
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ORCID | /0000-0002-4590-1908/work/163294165 |
Keywords
ASJC Scopus subject areas
Keywords
- Case-based-reasoning, Situation recognition, Surgical situation