Pre-operative ordering of minimally invasive surgical tools: A fuzzy inference system approach

David J. Miller, Carl A Nelson, Dmitry Oleynikov, David D. Jones

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

Objective: With a limited number of access ports, minimally invasive surgery (MIS) often requires the complete removal of one tool and reinsertion of another in order to provide surgeons with the full functionality necessary to complete a procedure. Materials and Methods: Endoscope video from 14 MIS procedures performed at the University of Nebraska Medical Center was used to collect usage statistics for various surgical instruments. This usage data was normalized and input to a fuzzy inference system (FIS) with four membership functions (MFs) to provide a crisp rating value for each instrument. Input membership functions included: number of uses ("Use"), total time used ("Time"), number of changes ("Change") and time per use ("Ave-Time"). Tools were arranged in a simulated cartridge system based on a "Usefulness" output membership function in such a way as to allow easy selection of the next instrument necessary to complete the procedure. Performance was measured by comparing the amount of cartridge indexing needed to complete a procedure using the FIS-generated arrangement against a set of random tool arrangements. Results: The 14 FIS-generated tool arrangements considered in this investigation performed better than 64.11% of randomly generated tool arrangements and as well or better than 80.48% of tool arrangements. Using the FIS in conjunction with a multifunction laparoscopic tool, it is projected that an average cycle savings of 17.75% and 17.39% can be achieved over the mean and median of the random tool arrangements, respectively. Conclusions: For a given set of tools, the FIS used in this investigation provides an efficient method of arranging tools for MIS that performs at least as well or better than simply placing the tool tips into the chambers in a random configuration. This leads to a decrease in operating room time and corresponding decreases in both patient trauma from insertion and removal of tools and monetary cost, which is directly related to the amount of time spent changing instruments.

Original languageEnglish (US)
Pages (from-to)35-45
Number of pages11
JournalArtificial Intelligence in Medicine
Volume43
Issue number1
DOIs
Publication statusPublished - May 1 2008

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Keywords

  • Fuzzy inference system
  • Fuzzy logic
  • Laparoscopic surgery
  • Minimally invasive surgery
  • Surgical robotics

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Artificial Intelligence

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