Computer Vision for Increased Operative Efficiency via Identification of
  Instruments in the Neurosurgical Operating Room: A Proof-of-Concept Study
Tanner J. Zachem Sully F. Chen Vishal Venkatraman David AW Sykes Ravi Prakash Samantha Spellicy Alexander D Suarez Weston Ross Patrick J. Codd
Objectives Computer vision (CV) is a field of artificial intelligence that
enables machines to interpret and understand images and videos. CV has the
potential to be of assistance in the operating room (OR) to track surgical
instruments. We built a CV algorithm for identifying surgical instruments in
the neurosurgical operating room as a potential solution for surgical
instrument tracking and management to decrease surgical waste and opening of
unnecessary tools. Methods We collected 1660 images of 27 commonly used
neurosurgical instruments. Images were labeled using the VGG Image Annotator
and split into 80% training and 20% testing sets in order to train a U-Net
Convolutional Neural Network using 5-fold cross validation. Results Our U-Net
achieved a tool identification accuracy of 80-100% when distinguishing 25
classes of instruments, with 19/25 classes having accuracy over 90%. The model
performance was not adequate for sub classifying Adson, Gerald, and Debakey
forceps, which had accuracies of 60-80%. Conclusions We demonstrated the
viability of using machine learning to accurately identify surgical
instruments. Instrument identification could help optimize surgical tray
packing, decrease tool usage and waste, decrease incidence of instrument
misplacement events, and assist in timing of routine instrument maintenance.
More training data will be needed to increase accuracy across all surgical
instruments that would appear in a neurosurgical operating room. Such
technology has the potential to be used as a method to be used for proving what
tools are truly needed in each type of operation allowing surgeons across the
world to do more with less.