title: A smart trocar for automatic tool recognition in laparoscopic surgery.
abstract: BACKGROUND
  Operating rooms have become increasingly complex environments and more prone to errors because of loss of situation awareness. Adding computer intelligence to the operating room may help overcome these limitations particularly if the system can automatically track which step of an operation a surgeon is performing. To develop such a platform, it is necessary to track which laparoscopic instruments are being used and in which port they are inserted. This article describes the development and validation of a "Smart Trocar" that can automatically perform this function.
  
  
  METHODS
  A Smart Trocar system prototype was developed that uses a wireless camera attached to a standard laparoscopic port and custom software algorithms. The system recognizes color wheels attached to the handle of a laparoscopic instrument and compares the unique color pattern to an instrument library for proper tool identification. The system was tested for reliability in a box trainer environment using a variety of tool positions and levels of room light illumination.
  
  
  RESULTS
  Correct color classification was achieved in 96.7% of trials. There were no errors in detection of the color wheel in space. In addition, the distance of the color wheel from the camera did not influence results and correct classifications were evenly distributed among the 12 laparoscopic tool positions tested.
  
  
  CONCLUSIONS
  This work describes a Smart Trocar system that identifies which laparoscopic tool is being used and in which port and proves its reliability. The system is an important element of a more comprehensive program being developed to automatically understand what step of an operation a surgeon is performing and use these data to improve situation awareness in the operating room.
authors.affiliation: University of Houston, Houston, TX, USA gtoti@uh.edu.
authors.forename: Giulia
authors.initials: G
authors.lastname: Toti
authors.affiliation: University of Houston, Houston, TX, USA Methodist Institute for Technology, Innovation and Education, Houston, TX, USA.
authors.forename: Marc
authors.initials: M
authors.lastname: Garbey
authors.affiliation: Houston Methodist Hospital, Houston, TX, USA.
authors.forename: Vadim
authors.initials: V
authors.lastname: Sherman
authors.affiliation: Methodist Institute for Technology, Innovation and Education, Houston, TX, USA Houston Methodist Hospital, Houston, TX, USA.
authors.forename: Barbara L
authors.initials: BL
authors.lastname: Bass
authors.affiliation: Methodist Institute for Technology, Innovation and Education, Houston, TX, USA Houston Methodist Hospital, Houston, TX, USA.
authors.forename: Brian J
authors.initials: BJ
authors.lastname: Dunkin
mesh_terms: D003116:Color; D004867:Equipment Design; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000295:instrumentation* / Q000379:methods*; D010535:Laparoscopy / Q000193:education* / Q000295:instrumentation*; D013525:Surgical Instruments
keywords: automatic tool recognition; operating room awareness; surgical assessment; training