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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
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2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2312.03001 |
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| _version_ | 1866929331085770752 |
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| author | Zachem, Tanner J. Chen, Sully F. Venkatraman, Vishal Sykes, David AW Prakash, Ravi Ntowe, Koumani W. Bethell, Mikhail A. Spellicy, Samantha Suarez, Alexander D Ross, Weston Codd, Patrick J. |
| author_facet | Zachem, Tanner J. Chen, Sully F. Venkatraman, Vishal Sykes, David AW Prakash, Ravi Ntowe, Koumani W. Bethell, Mikhail A. Spellicy, Samantha Suarez, Alexander D Ross, Weston Codd, Patrick J. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_03001 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Computer Vision for Increased Operative Efficiency via Identification of Instruments in the Neurosurgical Operating Room: A Proof-of-Concept Study Zachem, Tanner J. Chen, Sully F. Venkatraman, Vishal Sykes, David AW Prakash, Ravi Ntowe, Koumani W. Bethell, Mikhail A. Spellicy, Samantha Suarez, Alexander D Ross, Weston Codd, Patrick J. Image and Video Processing Computer Vision and Pattern Recognition Machine Learning 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. |
| title | Computer Vision for Increased Operative Efficiency via Identification of Instruments in the Neurosurgical Operating Room: A Proof-of-Concept Study |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2312.03001 |