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

• URL: http://arxiv.org/abs/2312.03001v2
• Date: Mon, 29 Apr 2024 19:08:15 GMT
• Title: Computer Vision for Increased Operative Efficiency via Identification of Instruments in the Neurosurgical Operating Room: A Proof-of-Concept Study
• Authors: Tanner J. Zachem, Sully F. Chen, Vishal Venkatraman, David AW Sykes, Ravi Prakash, Koumani W. Ntowe, Mikhail A. Bethell, Samantha Spellicy, Alexander D Suarez, Weston Ross, Patrick J. Codd,
• Abstract summary: 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.
• Score: 33.41411356073856
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Creating a Digital Twin of Spinal Surgery: A Proof of Concept [68.37190859183663]
  Surgery digitalization is the process of creating a virtual replica of real-world surgery. We present a proof of concept (PoC) for surgery digitalization that is applied to an ex-vivo spinal surgery. We employ five RGB-D cameras for dynamic 3D reconstruction of the surgeon, a high-end camera for 3D reconstruction of the anatomy, an infrared stereo camera for surgical instrument tracking, and a laser scanner for 3D reconstruction of the operating room and data fusion.
  arXiv  Detail & Related papers  (2024-03-25T13:09:40Z)
• Surgical tool classification and localization: results and methods from the MICCAI 2022 SurgToolLoc challenge [69.91670788430162]
  We present the results of the SurgLoc 2022 challenge. The goal was to leverage tool presence data as weak labels for machine learning models trained to detect tools. We conclude by discussing these results in the broader context of machine learning and surgical data science.
  arXiv  Detail & Related papers  (2023-05-11T21:44:39Z)
• Next-generation Surgical Navigation: Marker-less Multi-view 6DoF Pose Estimation of Surgical Instruments [66.74633676595889]
  We present a multi-camera capture setup consisting of static and head-mounted cameras. Second, we publish a multi-view RGB-D video dataset of ex-vivo spine surgeries, captured in a surgical wet lab and a real operating theatre. Third, we evaluate three state-of-the-art single-view and multi-view methods for the task of 6DoF pose estimation of surgical instruments.
  arXiv  Detail & Related papers  (2023-05-05T13:42:19Z)
• Using Hand Pose Estimation To Automate Open Surgery Training Feedback [0.0]
  This research aims to facilitate the use of state-of-the-art computer vision algorithms for the automated training of surgeons. By estimating 2D hand poses, we model the movement of the practitioner's hands, and their interaction with surgical instruments.
  arXiv  Detail & Related papers  (2022-11-13T21:47:31Z)
• Dissecting Self-Supervised Learning Methods for Surgical Computer Vision [51.370873913181605]
  Self-Supervised Learning (SSL) methods have begun to gain traction in the general computer vision community. The effectiveness of SSL methods in more complex and impactful domains, such as medicine and surgery, remains limited and unexplored. We present an extensive analysis of the performance of these methods on the Cholec80 dataset for two fundamental and popular tasks in surgical context understanding, phase recognition and tool presence detection.
  arXiv  Detail & Related papers  (2022-07-01T14:17:11Z)
• Exploring Deep Learning Methods for Real-Time Surgical Instrument Segmentation in Laparoscopy [0.4155459804992016]
  We evaluate and compare some popular deep learning methods that can be explored for the automated segmentation of surgical instruments in laparoscopy. Our experimental results exhibit that the Dual decoder attention network (DDNet) produces a superior result compared to other recent deep learning methods.
  arXiv  Detail & Related papers  (2021-07-05T23:32:05Z)
• Mapping Surgeon's Hand/Finger Motion During Conventional Microsurgery to Enhance Intuitive Surgical Robot Teleoperation [0.5635300481123077]
  Current human-robot interfaces lack intuitive teleoperation and cannot mimic surgeon's hand/finger sensing and fine motion. We report a pilot study showing an intuitive way of recording and mapping surgeon's gross hand motion and the fine synergic motion during cardiac micro-surgery.
  arXiv  Detail & Related papers  (2021-02-21T11:21:30Z)
• Autonomously Navigating a Surgical Tool Inside the Eye by Learning from Demonstration [28.720332497794292]
  We propose to automate the tool-navigation task by learning to mimic expert demonstrations of the task. A deep network is trained to imitate expert trajectories toward various locations on the retina based on recorded visual servoing to a given goal specified by the user. We show that the network can reliably navigate a needle surgical tool to various desired locations within 137 microns accuracy in physical experiments and 94 microns in simulation on average.
  arXiv  Detail & Related papers  (2020-11-16T08:30:02Z)
• Searching for Efficient Architecture for Instrument Segmentation in Robotic Surgery [58.63306322525082]
  Most applications rely on accurate real-time segmentation of high-resolution surgical images. We design a light-weight and highly-efficient deep residual architecture which is tuned to perform real-time inference of high-resolution images.
  arXiv  Detail & Related papers  (2020-07-08T21:38:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.