ArticleInternational journal of computer assisted radiology and surgery2016
Bridging the gap between formal and experience-based knowledge for context-aware laparoscopy.
Article in International journal of computer assisted radiology and surgery, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 citations in OpenAlex.
- A systematic review of annotation for surgical process model analysis in minimally invasive surgery based on video.Surgical endoscopy · 2023Pooled it
- Toward an interoperable, intraoperative situation recognition system via process modeling, execution, and control using the standards BPMN and CMMN.International journal of computer assisted radiology and surgery · 2024Article
- State-of-the-art of situation recognition systems for intraoperative procedures.Medical & biological engineering & computing · 2022Review
- Surgical data science - from concepts toward clinical translation.Medical image analysis · 2022Review
- Sensor-based machine learning for workflow detection and as key to detect expert level in laparoscopic suturing and knot-tying.Surgical endoscopy · 2019Article
- Design and Integration of Electrical Bio-impedance Sensing in Surgical Robotic Tools for Tissue Identification and Display.Frontiers in robotics and AI · 2019Article
- Toward a standard ontology of surgical process models.International journal of computer assisted radiology and surgery · 2018Article
- Surgical data science: The new knowledge domain.Innovative surgical sciences · 2017Article
- Tissue classification for laparoscopic image understanding based on multispectral texture analysis.Journal of medical imaging (Bellingham, Wash.) · 2017Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeComputer 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.
methodsWe 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.
resultsThe 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.
conclusionFormal and experience-based knowledge can be successfully combined for robust phase recognition.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.