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Objectives Computer vision (CV) is a field of artificial intelligence thatenables machines to interpret and understand images and videos. CV has thepotential to be of assistance in the operating room (OR) to track surgicalinstruments. We built a CV algorithm for identifying surgical instruments inthe neurosurgical operating room as a potential solution for surgicalinstrument tracking and management to decrease surgical waste and opening ofunnecessary tools. Methods We collected 1660 images of 27 commonly usedneurosurgical instruments. Images were labeled using the VGG Image Annotatorand split into 80% training and 20% testing sets in order to train a U-NetConvolutional Neural Network using 5-fold cross validation. Results Our U-Netachieved a tool identification accuracy of 80-100% when distinguishing 25classes of instruments, with 19/25 classes having accuracy over 90%. The modelperformance was not adequate for sub classifying Adson, Gerald, and Debakeyforceps, which had accuracies of 60-80%. Conclusions We demonstrated theviability of using machine learning to accurately identify surgicalinstruments. Instrument identification could help optimize surgical traypacking, decrease tool usage and waste, decrease incidence of instrumentmisplacement events, and assist in timing of routine instrument maintenance.More training data will be needed to increase accuracy across all surgicalinstruments that would appear in a neurosurgical operating room. Suchtechnology has the potential to be used as a method to be used for proving whattools are truly needed in each type of operation allowing surgeons across theworld to do more with less.

Computer Vision for Increased Operative Efficiency via Identification of Instruments in the Neurosurgical Operating Room: A Proof-of-Concept Study