Bridging the gap between formal and experience-based knowledge for context-aware laparoscopy
Research output: Contribution to journal › Research article › Contributed › peer-review
Contributors
Abstract
Purpose: Computer assistance is increasingly common in surgery. However, the amount of information is bound to overload processing abilities of surgeons. We propose methods to recognize the current phase of a surgery for context-aware information filtering. The purpose is to select the most suitable subset of information for surgical situations which require special assistance. Methods: We combine formal knowledge, represented by an ontology, and experience-based knowledge, represented by training samples, to recognize phases. For this purpose, we have developed two different methods. Firstly, we use formal knowledge about possible phase transitions to create a composition of random forests. Secondly, we propose a method based on cultural optimization to infer formal rules from experience to recognize phases. Results: The proposed methods are compared with a purely formal knowledge-based approach using rules and a purely experience-based one using regular random forests. The comparative evaluation on laparoscopic pancreas resections and adrenalectomies employs a consistent set of quality criteria on clean and noisy input. The rule-based approaches proved best with noisefree data. The random forest-based ones were more robust in the presence of noise. Conclusion: Formal and experience-based knowledge can be successfully combined for robust phase recognition.
Details
Original language | English |
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Pages (from-to) | 881-888 |
Number of pages | 8 |
Journal | International journal of computer assisted radiology and surgery |
Volume | 11 |
Issue number | 6 |
Publication status | Published - 1 Jun 2016 |
Peer-reviewed | Yes |
Externally published | Yes |
External IDs
PubMed | 27025604 |
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ORCID | /0000-0002-4590-1908/work/163294038 |
Keywords
ASJC Scopus subject areas
Keywords
- Cognition-guided assistance, Context-awareness, Ontology