Bridging the gap between formal and experience-based knowledge for context-aware laparoscopy
Publikation: Beitrag in Fachzeitschrift › Forschungsartikel › Beigetragen › Begutachtung
Beitragende
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
Originalsprache | Englisch |
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Seiten (von - bis) | 881-888 |
Seitenumfang | 8 |
Fachzeitschrift | International journal of computer assisted radiology and surgery |
Jahrgang | 11 |
Ausgabenummer | 6 |
Publikationsstatus | Veröffentlicht - 1 Juni 2016 |
Peer-Review-Status | Ja |
Extern publiziert | Ja |
Externe IDs
PubMed | 27025604 |
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ORCID | /0000-0002-4590-1908/work/163294038 |
Schlagworte
ASJC Scopus Sachgebiete
Schlagwörter
- Cognition-guided assistance, Context-awareness, Ontology