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

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 i...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2024-04
Main Authors: Zachem, Tanner J, Chen, Sully F, Venkatraman, Vishal, David AW Sykes, Prakash, Ravi, Ntowe, Koumani W, Bethell, Mikhail A, Spellicy, Samantha, Suarez, Alexander D, Weston, Ross, Codd, Patrick J
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial intelligence
Artificial neural networks
Computer vision
Machine learning
Medical instruments
Surgical apparatus & instruments
Surgical instruments
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2331-8422